Business intelligence (BI) reporting tools help an organization gather, consolidate, and derive value from its raw data. These tools allow users to analyze their data in-depth, improving decision-making at every level in the organization. Various BI reporting tools are about 80-85% similar in terms of functionalities and features. Different BI tools cater to different user needs like Self Service Reporting, Advanced Analytics, Enterprise-level reporting, etc.
There are certain expectations from a BI reporting tool when it comes to evaluating them based on the below-mentioned parameters:
1. Self Service: Self-service business intelligence (SSBI) is an approach to data analytics that enables business users to access and work with corporate data even though they do not have a background in statistical analysis, BI, or data mining.
An ideal self-service BI reporting tool should qualify on the below-mentioned parameters:
• Visualization & Intuitiveness: Ability to present KPI Driven Visual aids for quickly getting a business pulse at that time.
• Data Blending: Ability to join/blend data from multiple sources like excel and data marts.
• Report Templates: Ability to create report templates and reuse them
• Web-Based Modifications: Ability for end consumers/users to change the format of the report, slice and dice, and save it.
• Export Functionality: Ability to export data to PDF, Excel, and other commonly used platforms.
• Report Sharing: Ability to deliver a report to a mailbox or any specific location.
• Self-Scheduling and Refresh: Ability for user to self-subscribe to schedules or refresh reports on-demand/event-based.
2. Advanced Analytics: Advanced analytics goes beyond mathematical calculations such as sums and averages. It generates new information, identifies patterns and dependencies, and calculates forecasts.
An ideal advanced analytics BI reporting tool should qualify on the below-mentioned parameters:
• Data Capturing: Ability to enable processing and analysis of large amounts of data.
• Data Mining: Data and text mining may be used to find specific trends or pieces of data.
• Predictive Analysis: Ability to use techniques associated with data mining, machine learning, statistical analysis, and others to generate highly accurate predictions about future business trends
• Statistics: Ability to figure out what future trends or results might come about based on the statistics being reviewed
3. Enterprise level reporting: Enterprise reporting is the creation and distribution of reports concerning business performance to key decision-makers in an organization. This may include reports on metrics on key performance indicators or information curated for day-to-day activities. Various factors such as operational reporting, database connectivity support, and user authentication are essential from an enterprise standpoint.
An ideal enterprise-level BI reporting tool should qualify on the below-mentioned parameters:
• Data authorization: Data authorization with inheritance from business application
• Vendor support for tool: There should be easy-to-access vendor support for the tool.
• Pixel-perfect reporting: Ability to make reports which can be formatted in their components down to the individual pixel level
Let us consider 3 leading BI reporting tools and evaluate them based on different parameters.
MicroStrategy: MicroStrategy is an enterprise analytics platform that delivers Dashboards, Visualizations, Mobile apps and supports custom solutions.
Salient features:
• Reusability: Metadata created in MicroStrategy can be reused in the same project many times
• Supports Self Service BI: Supports Self Service BI along with other powerful tools to create reports/dashboards
• Supports a full range of BI applications: Supports a full range of BI applications from departmental BI (small workgroups) to Enterprise BI
• Massive Data access: Can access massive amounts of data from different data sources like EDW and Transactional
Tableau: Tableau helps in simplifying raw data in a very easily understandable format. Data analysis is very fast with Tableau, and the visualizations created are in the form of dashboards and worksheets.
Salient features:
• Supports Self Service BI: Designed from the ground up to support self-service
• Visual Analytics Capabilities: Provides robust visual analytics capabilities to enable data discovery with rapidly promoted roll up and drill-down capabilities
• Data Blending: Easy to use report data blending options facilitate merging multiple data sources
• Server Component Features: The server components provide scalability, access authorization, scheduling, and governance
• Distribution: Distribution is via a variety of client interfaces like Web Browser and Mobile
Power BI: Power BI enables users to gather business insights from both on-premise and cloud-stored data in a dynamic, interactive visualization at the low cost of ownership.
Salient features:
• Content Packs: Power BI uses Content Packs, which has dashboard reports, data models, and embedded queries.
• Custom Visualization: Power BI has a library of custom visualization. If the business needs are different, then so should the visuals.
• Access to a variety of Data sources: Power BI Desktop includes a huge array of on-premise and cloud data sources.
• Print Dashboard: Power BI provides a unique feature for printing dashboards, which can be handy in board meetings and discussions.
https://nexumbs.com/wp-content/uploads/2021/12/Power-BI-Reports-scaled.jpg17072560Nexumhttps://nexumbs.com/wp-content/uploads/2021/03/logo.pngNexum2021-12-01 07:53:312021-12-19 08:07:56Comparative Analysis of BI Reporting Tools
Business intelligence (BI) reporting tools help organizations gather, consolidate, and derive value from their raw data. These tools allow users to analyze their data in-depth, improving decision-making at every level in the organization. Various BI reporting tools are about 80-85% similar in terms of functionalities and features. Different BI tools cater to different user needs like Self Service Reporting, Advanced Analytics, Enterprise-level reporting, etc.
There are certain expectations from a BI reporting tool when it comes to evaluating them based on the below-mentioned parameters:
1. Self Service: Self-service business intelligence (SSBI) is an approach to data analytics that enables business users to access and work with corporate data even though they do not have a background in statistical analysis, BI, or data mining.
An ideal self-service BI reporting tool should qualify on the below-mentioned parameters:
• Visualization & Intuitiveness: Ability to present KPI Driven Visual aids for quickly getting a business pulse at that time.
• Data Blending: Ability to join/blend data from multiple sources like excel and data marts.
• Report Templates: Ability to create report templates and reuse them
• Web-Based Modifications: Ability for end consumers/users to change the format of the report, slice, and dice, and save it.
• Export Functionality: Ability to export data to PDF, Excel, and other commonly used platforms.
• Report Sharing: Ability to deliver a report to a mailbox or any specific location.
• Self-Scheduling and Refresh: Ability for user to self-subscribe to schedules or refresh reports on-demand/event-based.
2. Advanced Analytics: Advanced analytics goes beyond mathematical calculations such as sums and averages. It generates new information, identifies patterns and dependencies, and calculates forecasts.
An ideal advanced analytics BI reporting tool should qualify on the below-mentioned parameters:
• Data Capturing: Ability to enable processing and analysis of large amounts of data.
• Data Mining: Data and text mining may be used to find specific trends or pieces of data.
• Predictive Analysis: Ability to use techniques associated with data mining, machine learning, statistical analysis, and others to generate highly accurate predictions about future business trends
• Statistics: Ability to determine what future trends or results might come about based on the reviewed statistics.
3. Enterprise level reporting: Enterprise reporting is the creation and distribution of reports concerning business performance to key decision-makers in an organization. This may include messages on metrics on key performance indicators or information curated for day-to-day activities. Various factors such as operational reporting, database connectivity support, and user authentication are essential from an enterprise standpoint.
An ideal enterprise-level BI reporting tool should qualify on the below-mentioned parameters:
• Data authorization: Data authorization with inheritance from business application
• Vendor support for tool: There should be easy-to-access vendor support for the tool.
• Pixel-perfect reporting: Ability to make reports formatted in their components down to the individual pixel level.
Let us consider three leading BI reporting tools and evaluate them based on different parameters.
MicroStrategy: MicroStrategy is an enterprise analytics platform that delivers Dashboards, Visualizations, Mobile apps and supports custom solutions.
Salient features:
• Reusability: Metadata created in MicroStrategy can be reused in the same project many times
• Supports Self Service BI: Supports Self Service BI along with other powerful tools to create reports/dashboards
• Supports a full range of BI applications: Supports a full range of BI applications from departmental BI (small workgroups) to Enterprise BI
• Massive Data access: Can access massive amounts of data from different data sources like EDW and Transactional
Tableau: Tableau helps in simplifying raw data in a very easily understandable format. Data analysis is very fast with Tableau, and the visualizations created are in the form of dashboards and worksheets.
Salient features:
• Supports Self Service BI: Designed from the ground up to support self-service
• Visual Analytics Capabilities: Provides robust visual analytics capabilities to enable data discovery with rapidly promoted roll up and drill-down capabilities
• Data Blending: Easy to use report data blending options facilitate merging multiple data sources
• Server Component Features: The server components provide scalability, access authorization, scheduling, and governance
• Distribution: Distribution is via a variety of client interfaces like Web Browser and Mobile
Power BI: Power BI enables users to gather business insights from both on-premise and cloud-stored data in a dynamic, interactive visualization at the low cost of ownership.
Salient features:
• Content Packs: Power BI uses Content Packs, dashboard reports, data models, and embedded queries.
• Custom Visualization: Power BI has a library of custom visualization. If the business needs are different, then so should the visuals.
• Access to a variety of Data sources: Power BI Desktop includes a considerable array of on-premise and cloud data sources.
• Print Dashboard: Power BI provides a unique feature for printing dashboards, handy in board meetings and discussions.
https://nexumbs.com/wp-content/uploads/2021/10/BI-tools.jpg442848Nexumhttps://nexumbs.com/wp-content/uploads/2021/03/logo.pngNexum2021-10-09 06:59:392021-10-24 07:25:06Comparative Analysis of BI Reporting Tools
Data scientists need a tailored portfolio of projects that they own and manage to have a sense of autonomy.
The top skill or personality trait a successful data scientist can possess (and should possess) is curiosity.
Managing a successful analytics team and individual analytics professionals is different than managing any other type of team.
Data and analytics will be ubiquitous in the very near future.
Analytics teams are different than any other team in the organizationandanalytics professionals are unique variants of creative professionals. Providing challenging, interesting, and valuable work in the form of a personal project portfolio of work for a data scientist can be done and needs to be done to ensure productivity, job satisfaction, value delivery, and retention.
We interviewed Analytics Leader, and bestselling author,John K Thompson on data analytics, the future of analytics, and his recent book, Building Analytics Teams.
The interview in detail:
1. What are the fundamental concepts of building and managing a high-performing analytics team?
It is critically important to remember that data scientists are creative and intelligent people. They cannot be managed well in a command-and-control environment.
Data scientists need a tailored portfolio of projects that they own and manage to have a sense of autonomy. If they have a portfolio of projects and can manage their time and effort, the productivity of the team will be much higher than what is typically seen in teams managed in a traditional manner.
The relationship of the analytics leader with their peers and executives of the company is critically important to the success of the analytics team.
It is very important to realize that most analytics projects fail at the point where analytical models are to be implemented in production systems.
2. Tell us about your book, Building Analytics Teams. How is your book new and/or different from other books on Data Analytics?
Building Analytics Teams is focused on the practical challenges faced by people who are building and managing high-performance analytics teams and the staff members who make up those analytics teams.
The book is different from other books in that it examines the process of building and managing a team from a holistic view. The book considers the organization framework, the required processes, the people, the projects, the problems, and pitfalls.
The content of the book guides the reader through how to navigate these challenges and provides illustrations and examples of how to be successful.
The book is a “how-to” guide on how to successfully manage the analytics process in a large corporate environment.
3. What was the motivation behind writing this book?
I have not seen a book like this, and I wish I had a book like this earlier in my career.
I have built a number of analytics teams. While building and growing those teams, I noticed certain recurring patterns.I wanted to address the misconceptions and the misperceptions people hold about analytics teams.
Analytics teams are unique. The team members who are successful have a different mindset and attitude toward project work and teamwork. I wanted to communicate the differences inherent in a high-performance analytics team when compared to other teams.
Also, I wanted to communicate that managing a successful analytics team and individual analytics professionals is different than managing any other type of team.
I wanted to write a guide for managers and analytics professionals to help them understand how the broader organization views them and how they can interface and interact with their peers in related organizational functions to increase the probability of joint success.
4. What should be the starting point for data analytics enthusiasts aiming to begin their journey in Data Analytics? Howdo you think your book will help them in their journey?
It depends on where they are starting their journey.
If they are in the process of completing their undergraduate or graduate studies, I would suggest that they take classes in programming, data science, or analytics.
If they are professionals, I would suggest that they take classes on Coursera, Udemy, or any other online educational platform to see if they have a real interest in, and affinity for, analytics.
If they do have an interest, then they should start working on analytics for themselves to test out analytical techniques, apply critical thinking and try to understand what they can see or cannot see in the data.
If that works out and their interest remains, they should volunteer for projects at work that will enable them to work with data and analytics in a work setting.
If they have the education, the affinity, and the skill, then apply for a data science position. Grab some data and make a difference!
5. What are the key skills required for someone to be successful working in Data Analytics? What are the pain points/challenges one should know?
The top skill or personality trait a successful data scientist can possess (and should possess) is curiosity. Without curiosity, you will find it difficult to be successful as a data scientist.
It helps to be talented and well educated, but I have met many stellar data scientists that are neither. Beyond those traits, it is more important to be diligent and persistent.
The most successful business analysts and data scientists I have ever worked with were all naturally and perpetually curious and had a level of diligence and persistence that was impressive.
As for pain points and challenges; data scientists need to work on improving their listening skills, their written&verbal communication, and presentation skills. All data scientists need improvement in these areas.
6. What is the future of analytics? What will we see next?
I do believe that we are entering an era where data and analytics will be increasing in importance in all human endeavors. Certainly, corporate use of data and analytics will increase in importance, hence the focus of the book.
But beyond corporations, the active and engaged use of data and analytics will increase in importance and daily use in managing multiple aspects of – people’s personal lives, academic pursuits, governmental policy, military operations, humanitarian aid, tailoring of products and services; building of roads, towns, and cities, planning of traffic patterns, provisioning of local federal and state services, intergovernmental relationships and more.
There will not be an element of human endeavor that will not be touched and changed by data and analytics. Data is ubiquitous today and data and analytics will be ubiquitous in the very near future.
We will see more discussions on who owns data and who should be able to monetize data.
We will experience increasing levels of AI and analytics across all systems that we interact with, and most of it will be unnoticed and operate in the background for our benefit.
https://nexumbs.com/wp-content/uploads/2021/10/Analytics-Teams.jpg461696Nexumhttps://nexumbs.com/wp-content/uploads/2021/03/logo.pngNexum2021-10-01 07:32:192021-10-24 07:43:04Understanding the Fundamentals of Analytics Teams with John K. Thompson
The secret is out, and has been for a while: In order to remain competitive, businesses of all sizes, from startup to enterprise, need business intelligence (BI) and different types of dashboards.
Business intelligence has evolved into smart solutions that provide effective data management – from extracting, monitoring, analyzing, and deriving actionable insights needed to stay competitive on the market, to powerful visualizations created with a dashboard builder which enables business users to interact with data and drill into bits and pieces of information they might need, at any time, any place.
But what do you do with all this business intelligence? You can have the most robust BI infrastructure in place. However, if the underlying information isn’t easy to access, analyze, or understand, it is pointless. This is where the power of business dashboards comes into play. Dashboards often are the best way to gain insight into an organization and its various departments, operations, and performance. Well-built, focused dashboards easily serve up summaries and reports of the BI that’s most critical to the organization. Moreover, different types of dashboards will enable you to convey an improved message to your audience, organize your data more effectively, and boost business processes across the board.
That being said, in this post, we will explain what is a dashboard in business, the features of strategic, tactical, operational, and analytical dashboards, and expound on examples that these different types of dashboards can be used. Let’s get started.
What Is A Dashboard In Business?
A dashboard in business is a tool used to manage all the business information from a single point of access. It helps managers and employees to keep track of the company’s KPIs and utilizes business intelligence to help companies make data-driven decisions.
Let’s take an analogy to explain this notion further: A car dashboard instantaneously identifies and provides feedback regarding the status of the automobile: speed, servicing needs, tire pressure, fuel level, etc. Dashboards in business do the same thing, only much more. Through dashboards, organizations can quickly identify current and historical performance. Organizations can also further utilize the data to define metrics and set goals. By integrating these key performance indicators (KPIs) and goals into their dashboards, companies can proactively identify issues, minimize costs, and strive to exceed performance expectations. Of course, it is also important to choose the right KPI.
In the recent years, dashboards have been used and implemented by many different industries, from healthcare, HR, marketing, sales, logistics, or IT, all of which have experienced the importance of dashboard implementation as a way to reduce cost and increase the productiveness of their respected business. It doesn’t matter from which business you’re coming from or how big your company is, you always want effective results and clear actions to be taken after an issue is discovered.
Let’s explain that with a dashboard example:
The sales performance dashboard above is a one-stop shop for sales insights. The dashboard provides the perfect overview of the progress of the sales department by focusing on various sales KPIs: sales growth, sales targets, average revenue per unit (ARPU), customer acquisition cost (CAC), and customer lifetime value (CLV).
At a glance, sales managers can see whether or not their team is meeting their individual goals. Managers can also see if the team as a whole is reaching its goals. The value this brings to the business is significant. Once companies gain regular insights into their KPIs, they see deeper into their data and generate actionable insight.
This type of analysis is not feasible with traditional paper reports and spreadsheet tools. The traditional types of reporting don’t meet the requirements of today’s data management nor can they produce efficiency like an interactive dashboard where sets of data are presented in a complementary way. An effective dashboard combines information dynamically to measure performance and drive business strategy. That interactivity is indeed what drives a profitable result by visually depict important data that can be accessed by different departments. Cloud-based, real-time online data visualization software enables fast, data-driven action by decision-makers.
The digital age needs digital data. Now when you have plenty of information about dashboards, let’s take a closer look at each type, and how to choose the one you need in your daily operations or strategic goals.
Dashboard Types For Each Business Need
There is another important factor to a dashboard’s success, besides avoiding “data puke.” It is as simple as choosing the right type of dashboard.
As mentioned, the purpose of a dashboard is to drive action. In this data-driven world, many dashboard types are changing the way a successful business intelligence strategy is conducted. That means that although you can have a healthy approach to your business development, if you don’t communicate the right sets of data to the right people in your company, long-term success can be jeopardized and costly. This is why choosing the right type of dashboard can bring lasting and cost-effective results.
But that’s no easy task. With all the amount of data these days, and all the objectives and goals that managers need to achieve in a short timeframe, it’s not uncommon to be confused and overwhelmed with all the dashboards out there. First and foremost, you need to ask yourself the question of all questions:
What Problems Are You Trying To Solve?
To help you on your way to determine what kind of problems you need to solve, you should start with these inquiries: What is the main purpose of a dashboard? And, is your dashboard analytical or operational? Determining which overarching category your dashboard sits in is the first order of business.
Operational dashboards look at current performance related to your KPIs. They help organizations understand, in real-time, if their performance is on target. They are often used across various levels of an organization.
Analytical dashboards help organizations establish targets based on insights into historical data. They are often complex: utilizing complex models and what-if statements. Analytical dashboard ownership usually falls on business analysts/experts.
When discussing dashboard types, it is easy to get caught up in a game of semantics. Of course, there is overlap between the two genres and dashboard naming conventions are evolving with the field. The important thing is that you identify what questions you are trying to answer before you build a dashboard.
So What Types Of Dashboard Works Best For Your Business?
Now that we have separated the dashboards into two large categories, let’s dig deeper. There are 4 general subtypes of dashboards:
Strategic – focused on long-term strategies and high-level metrics
Operational – shows shorter time frames and operational processes.
Analytical – contains vast amounts of data created by analysts.
Tactical – used by mid-management to track performance.
Yes, with our current hierarchy you can have an operational-operational dashboard. We told you we could get into some dashboard semantics. Each of these dashboard types comes with different requirements for the level of summary, analytic capabilities, and user interfaces.
What Is A Strategic Dashboard?
A strategic dashboard is a reporting tool for monitoring the long-term company strategy with the help of critical success factors. They’re usually complex in their creation, provide an enterprise-wide impact to a business, and are mainly used by senior-level management.
Strategic dashboards are commonly used in a wide range of business types while aligning a company’s strategic goals. They track performance metrics against enterprise-wide strategic goals. As a result, these dashboards tend to summarize performance over set time frames: past month, quarter, or year. When the strategic dashboard is properly developed, designed, and implemented, it can effectively reduce the amount of time needed to accomplish a specific business key performance indicator, while reducing operational costs. To know what is a dashboard in strategic planning doing and why it’s important, it’s important to keep in mind that these dashboards can provide senior teams a clear picture of strategic issues, and therefore, grant them the opportunity to accomplish a specific course of action.
Although they can provide opportunities for specific departments’ operations and further analysis, strategic reports and dashboards are usually fairly high-level. As mentioned, senior members of a team can identify strategic concerns fairly quickly and provide comprehensive strategic reports with the analyzed data. The importance lies in analyzing management processes, using common qualitative and quantitative language, and identifying a specific system, which has to be incorporated into the dashboard so that every decision-maker understands the presented data.
Let’s see this through 5 strategic dashboard examples.
a) Management strategic dashboard
This management dashboard below is one of the best strategic dashboard examples that could easily be displayed in a board meeting. It isn’t cluttered, but it quickly tells a cohesive data story. The dashboard focuses on revenue in total as well as at the customer level plus the cost of acquiring new customers. The dashboard is set to a specific time frame and it includes significant KPIs: customer acquisition costs, customer lifetime value, and sales target.
**click to enlarge**
This dashboard answers the following: What is my customer base and revenue compared to this time last year? While addressing specific values, incorporating specific key performance indicators, and using a common qualitative and qualitative language, this dashboard represents the management board’s clear value and specific course of action, while using comparison metrics and analysis.
b) CMO strategic dashboard
Another example comes from the marketing department. Chief Marketing Officers (CMOs) often don’t have time to check numbers such as traffic or CTR of certain campaigns. But they do need to have a closer look at a more strategic level of marketing efforts, even cooperating with sales to reach the best possible marketing results a business can have, and, therefore, generate profit. This marketing dashboard shows these important strategic KPIs in a visual, informative, and straightforward way.
The strategic dashboard example above expounds on the cost of acquiring each customer, leads, MQL, and compares them to previous periods, and set targets. A CMO must have a birds-eye view of the strategic goals so that he/she can react promptly and keep the department’s results under control. An executive can immediately see where his/her targets are, which gives them the ability to drill down further into these marketing KPIs and see what can be improved in the overall marketing funnel.
To build this strategy dashboard, you don’t need to have extensive IT skills or advanced database management knowledge. The important part is that you understand your strategic goals, and the KPIs you need to achieve them. The rest is done by a simple drag-and-drop interface of a KPI software which enables you to cut the manual tasks of data management and dig faster into your data by interacting with each metric.
c) SaaS management dashboard
Our next example is another management dashboard but focusing on the executive level of a SaaS business. Here, managers get an overview of the three most important areas for any Software as a Service company: customers, revenue, and costs. Tracking these metrics closely and over time, allows you to get a birds-eye view into relevant metrics from past, present, and future performance in order to optimize processes and ensure your business stays profitable over time.
**click to enlarge**
Looking into this strategic dashboard more in detail starting with the left side, we see two line charts displaying relevant metrics related to customers. On the top chart, we see the paying customers, the lost ones, and the churn rate. All of them are displayed in a one-year period that enables you to see how each value has developed and if your strategies were successful and your targets were met. A similar situation we have in the bottom chart. This one displays the development of the CAC, ARPU, and CLTV. This way you can understand how much each of your customers is bringing to your business.
The right section of the dashboard displays important information about the Monthly Recurring Revenue. The MRR is one of the most important metrics to track for a SaaS business as it can give a notion of how much is the business growing. It does this by measuring the predictable revenue the company expects in a given month, and you calculate it by dividing the total MRR by the number of customers (contracts).
d) CFO dashboard for strategic planning
Chief financial officers need to keep a company’s strategy on track, monitor the financial performance closely, and react when there are deviations from strategic goals and objectives. But not only, as the finances of a company are affected also by non-direct factors such as employee and customer satisfaction. For example, if employees are not satisfied with their working environment, they can call in sick or leave the company which will cause financial bottlenecks. But let’s take a closer look at what kind of dashboards for strategy CFOs need.
**click to enlarge**
We will start the dashboard analysis with key metrics critical for strategic financial analytics optimization, expressed here both in dollars and percentages while simple gauge charts immediately put the focus on red and green colors. The visual interface will immediately show you that operating expenses are higher than expected, which you can use to dig deeper and identify the causes. On the other hand, we can see that metrics such as revenue, gross profit, EBIT, and net income are kept under control and, in fact, generate positive values. A quick overview of the targets shows exactly how much the gains increased, expressed in dollars.
By having all these numbers in a clear and concise format, each CFO can utilize the visual as a comprehensive financial report template, consolidate data from multiple touchpoints and automate this strategic plan dashboard for future use.
Let’s continue with more details on the right of the dashboard. The costs are visualized through a percentage breakdown depicting sales, general and admin, marketing, and other expenses. Here we can see that sales use up most of the costs, followed by general and admin. Maybe there is space to eliminate some costs but be careful not to cause the opposite effect.
Finally, employee and customer satisfaction levels are financial charts that are not directly related to the general financial performance but they can certainly affect it. Modern times require modern solutions, hence, CFOs need to have a close overview of other elements that can impact the company’s finances.
e) Sales KPI dashboard
Moving on to our next strategic dashboard template comes a powerful sales BI tool. The Sales KPI dashboard focuses on high-level sales metrics that c-level executives and managers need to closely monitor in order to ensure goals are being met.
First, we see that the dashboard displays 4 key metrics: the number of sales, revenue, profit, and cost, each of them is compared to a set target as well as the values of the last period, this way you get a quick glance into the performance of the month by just looking quickly at the charts. Next, the dashboard breaks down each of these metrics more in detail to extract conclusions and also analyze if the current strategies need additional adjustments. Getting a view into past data allows managers to understand where the numbers should be, and find efficient solutions to get there. The bottom line of a sales strategy should be to increase revenues and profits for the business, this can all be achieved by leveraging the power of the data in hand.
Now that we have illustrated the power of these strategic reports, it is time to take a closer look at our next types of dashboards, continuing with operational dashboards.
What Is An Operational Dashboard?
An operational dashboard is one of the types of dashboards used for monitoring and managing operations that have a shorter time horizon. Since they focus on tracking operational processes, they’re usually administrated by junior levels of management.
Their value in today’s digital age lies in the fact that businesses start to realize the importance of fast and correct data between operational teams and departments. While the unprecedented developments in the field of dashboard reporting and analysis have made operational undertakings quite simplified, operational managers can also greatly profit from using these kinds of dashboards, and visually and interactively point to a real-time data issue that has to be swiftly addressed.
These kinds of dashboards are arguably the most common ones. They are mostly used for monitoring and analyzing a company’s activities in a given business area. These dashboards are usually focused on alerting about business exceptions and are based on real-time data. Operational metrics dashboards usually end up in the hands of the subject matter experts. This often leads to more direct action, then further analysis. Because of this, operational dashboards often are more detailed than strategic dashboards. They can also provide operational reports with a more detailed view of specific data sets.
Operational dashboards help departments stay proactive and ahead of problems. For example, a manufacturing firm may use them to track products manufactured along with the number of defects, complaints, or returns. This helps in the manufacturing analytics processes – with a dashboard, any problematic changes would be highlighted in real-time. We will see this more in detail in one of our examples.
But let’s take a look at another marketing example. We have illustrated a strategy dashboard of a marketing department in our last example above, and now we will focus on an operations dashboard example, also for marketing purposes.
a) Marketing operational dashboard
The marketing performance dashboard above is one of our top operational dashboard examples. It shows the performance of 3 campaigns over the past 12 weeks. It provides important operational information and key performance indicators for the marketing team on cost per acquisition, the total number of clicks, total acquisitions gained, and the total amount spent in the specific campaign. Any significant changes would immediately alert the marketing team. Why is it useful? Because a fast-paced marketing department or agency can adjust their operational activities based on real-time data and teams don’t have to wait for extensive, traditional reports and analysis presented in a spreadsheet.
**click to enlarge**
We can see how each campaign performed and what kind of results brought in a set timeframe. This is extremely useful when each campaign needs to be optimized to deliver the best possible results, and often it’s done on a daily basis, especially in agencies. Operational reports need to be built fast, and this dashboard can help each campaign manager by having real-time, accurate data.
b) Manufacturing Production dashboard
As we mentioned before, an operational dashboard can be a perfect tool to track production. To put this into perspective let’s take a look at our manufacturing dashboard example. This visual tool gives a detailed overview of all aspects related to production, from the volume, quantity ordered, returned items, to machines’ performance. Getting this level of insights can help manufacturers to spot any potential issues or hidden trends that could harm production as well as find improvement opportunities to optimize key processes.
Going a bit more into detail about this operations dashboard, we first see the production volume and the quantity ordered, this enables you to understand what to expect in terms of production and machines usability on a daily, weekly, monthly, or annual basis. Comparing the production volume with the quantity ordered also allows you to monitor if your production is being efficient and that you are not over or underperforming. Hand in hand with these metrics, comes the performance of the machines. Machines are the beating heart of any manufacturing company, by carefully monitoring the performance of each of them you can identify the ones that are most efficient, schedule maintenance, and be always aware of what assets you have available.
Last but not least, is one of the most costly issues for a manufacturing company: returned items. Tracking this metric in detail can tell you how efficient is your business at delivering what it is supposed to be delivered. A lower return rate means your clients are getting their orders right, while a higher rate means you are not providing the best service. A good way to keep this rate as low as possible is to take a deeper look at the reasons for the return and tackle the issues so they won’t keep happening.
c) Pick and pack operational dashboard for logistics
Our next example is a logistics dashboard tracking all aspects related to order processing. Better know as pick and pack, it is the process in which a worker finds an item from an order in the warehouse and puts it in a box, or another type of packaging, to be shipped to the customer. Tracking these metrics in detail allows businesses to optimize key processes and save costs while still ensuring a quality service. Let’s look into the dashboard more in detail.
This operational dashboard is divided into 4 main areas of the picking and packing process: financial, effectiveness, utilization, and quality. Each area is compared to a target of last month’s performance as a benchmark to improve. Paired to this, the dashboard displays the performance of 3 different lines of work on each area that is being covered. Tracking each line separately allows you to implement different strategies on each of them and later test which one was more efficient. It can also help you understand if an employee or team is underperforming and find training opportunities to improve their efficiency.
d) LinkedIn operations dashboard example
We continue our list of operations dashboard examples with LinkedIn. This social media network is critical for building business relationships, either on a profile level or company. With the number of users steadily growing and reaching more than 610 million members in 2020, LinkedIn should be on a higher priority for companies that want to reach decision-makers and business professionals. To effectively manage a company’s presence, companies can use an operational data dashboard that will solve multiple social media problems such as automation, customization of reports, and provide advanced analytical features. Let’s take a look at an operational dashboard design example specifically created for LinkedIn.
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To understand how your company is developing, managing your account in a shorter time frame is a must in the fast-paced social media world. Communicating with followers, monitoring the engagement rate and recent updates will ensure you stay present and reachable for any social messages that you receive or send. If you compare your results over time, you can identify trends, spot inefficiencies in your operational management, and create a social media report that will consolidate all LinkedIn-related communication.
In the example above, we can see the numbers of followers gained and the development on a weekly basis. It’s good to have an increment in the number of followers as your posts will have a greater chance to reach more people. The dashboard continues with metrics such as impressions, followers by industry, and engagement rate. It’s important to know where your audience is coming from since different industries require different content. The breakdown of the engagement rate through total engagement, likes, shares, and comments will let you know what kind of content works best so that you can reuse it in the future. Examine what happened if you see a certain spike and try to recreate the same strategy again.
The final part shows the CTR and the last 5 company updates. These metrics are critical to track since you will find out how many users actually click on your link and how your most recent updates behave. Modern BI reporting tools will ensure all these data is calculated automatically and delivered on a time frame that you set (for example daily or weekly), without the need to export numerous spreadsheets or work with other potential limitations.
c) Customer service operational metrics dashboard
One of our next operational dashboard examples focuses on customer service. By having all the important customer service KPIs on a single screen, the team can manage its operations much more efficiently. Let’s see this through a visual example.
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This type of dashboard expounds on the customer service team’s performance over a shorter timeframe, in this case, daily, with an additional monthly overview of the first, second, third call, and unresolved ones. We can see that the customer service dashboard is divided into 2 parts: the resolutions and the response time. Each day of the week gives an additional insight which helps teams to reduce the response time metric if they track it on a regular basis. That way a team can know if they need more staff or a better schedule during the days where response time is higher. That’s why it is important to keep the operations on track and keep an eye on the team’s performance on a daily level. For added value, you can check our article on the top call center KPIs and ensure the best possible customer service.
We have seen our operational dashboard examples focused on marketing and customer service, now we will continue with additional, different types of dashboards concentrated on the analytical processes of a company.
What Is An Analytical Dashboard?
An analytical dashboard is a type of dashboard that contains a vast amount of data created and used by analysts to provide support to executives. They supply a business with a comprehensive overview of data, with middle management being a crucial part of its usage.
Like mentioned earlier, the importance of an analytical dashboard lies within its impact on historical data usage, where analysts can identify trends, compare them with multiple variables and create predictions, and targets, which can be implemented in the business intelligence strategy of a company. They are often useful when complex categorized information is massive and broad, and need visualization to perform a clear analysis of generated data.
The analytical dashboard can also be found at the intersection of the strategic and operational dashboard. They consist of different modules that can bring a positive effect on the performance of a business if used correctly.
a) Financial performance dashboard
In the example below, the analysis of the financial dashboard focused on performance can help decision-makers to see how efficiently the company’s capital is being spent and to establish a specific operational task to structure future decisions better.
With the important financial KPIs such as return on assets, return on equity, working capital, and the overview of the balance sheet, a finance department has a clear picture of its capital structure. This analysis dashboard enables the department to, consequently, set specific operational activities to improve further.
b) Procurement cost dashboard
Another dashboard focused on costs but, in this case, specifically for the procurement department. As we know, procurement is found in most companies as a function that connects a company with its suppliers, contractors, freelancers, agencies, etc. It’s not only critical for industries such as manufacturing but service-oriented as well. To see the analytical perspective of a procurement department, let’s take a look at a visual example.
The procurement department handles large volumes of data and by analyzing the costs and purchase of the procurement cycle, analysts can present data that will provide a building block for different units in order to save invaluable time. A procurement dashboard as visualized above can serve as a tool to present data in a visual and straightforward manner.
The dashboard starts with a depiction of cost and savings-related metrics and the trends that are occurred in a specific time frame. The trend lines show you that, in fact, most of the indicators increased but the reason could be that the number of orders also increased. The cost reduction in the middle of the dashboard is divided into different product categories and, that way, management can identify if there is space for even more rationalization of procurement costs. The dashboard continues with the ROI, a procurement KPI that is, actually, calculated differently than the regular ROI. In this case, you need to divide the annual costs savings by the internal costs and express it as a ratio. Setting a target will help the management to immediately spot if the cost-related efforts were successful. In this case, the target was set at 10 so you can clearly see how it developed for different categories.
On the right side, we can see details on cost savings and avoidance, which is important to keep an eye on since it can ease the decision-making process for managers that want to avoid future costs by introducing specific measures such as better negotiation processes. Finally, the top 5 suppliers will show you where are your costs allocated in relation to the suppliers which management can use for further optimization.
This kind of analysis is essential since procurement departments usually gather data from multiple sources such as ERP, databases, or CSV files, e.g. In order to optimize the cost management and increase the overall positive results, an analytic dashboard such as this one can prove to be beneficial.
c) Healthcare analytical dashboard for patients
Our next analytical dashboard template is from the healthcare industry and it aims to monitor all aspects related to how satisfied are your patients with your facility and the service it provides. Covering aspects such as staff, treatment, waiting times, and safety, this dashboard will help you to assess your relationship with patients and ensure they are getting everything they need.
The healthcare dashboard starts by displaying a patient satisfaction score based on the answer to two questions related to how the hospital staff provides information to patients who need it. This is a valuable insight as communication is one of the key aspects of a good relationship with patients. If you make sure they are always clear about relevant information, you will enjoy a healthy satisfaction rate. Next, you get details on the waiting times of different activities such as lab test turnaround, time to see a doctor, to get treatment, and of arrival to bed. All these metrics are displayed in a gauge chart with colors that easily indicate if the score is bad, medium, or good. A good way to optimize this is to set target times and implement different measures to reach those targets, that way you will avoid infinite queues for your patients and more efficient care.
d) Analytical retail KPI dashboard
Another analytical dashboard example comes from the retail industry. It creates an analytical parallel between management and customer satisfaction since the supply chain can directly affect it. This comprehensive dashboard shows us an overview of important aspects of a retail business that enable analysts to identify trends and give management the support needed in business processes. Retail analytics made simple.
As we can see on the retail KPI dashboard above, some of the crucial metrics such as rate of return (also depicted by category), the total volume of sales, customer retention rate, and the number of new and returning customers through a set timeframe, can give us a bigger picture on the state of the retail business. These retail KPIs can show how good you are in keeping your customers and developing brand loyalty, the management can clearly see which aspects of the business need to be improved. If you keep your backorder rate as low as possible, customers won’t get frustrated and your overall numbers will perform well. It’s simple, keeping a customer happy will enable you to grow.
e) KPI Analytical dashboard for FMCG industry
Supply chain management is not an easy task, especially when the products at stake are from the fast-moving goods category. To help optimize several processes and ensure operational success is that this analytical dashboard was created.
Armed with KPIs related to deliveries, products sold, inventory, and stock, the FMCG dashboard gives an overview of all important aspects for the correct functioning of a fast-moving consumer goods business. By tracking the out-of-stock rate closely you get a picture of the status of your inventory and avoid stockouts that can affect sales. On the other hand, the products sold within freshness day and the average time to sell can tell you if there is a certain product that takes more time to sell so you might need to lower your stock and avoid throwing away products that are no longer fresh. By monitoring all these metrics on a regular basis you can stay one step ahead of any problems that might arise in your FMCG business.
Our next type of dashboard is focused on pure analytics that supports strategic initiatives: a tactical dashboard.
What Is A Tactical Dashboard?
A tactical dashboard is utilized in the analysis and monitoring of processes conducted by mid-level management, emphasizing the analysis. Then an organization effectively tracks the performance of a company’s goal and delivers analytic recommendations for future strategies.
Tactical dashboards are often the most analytical dashboards. They are great for monitoring the processes that support the organization’s strategic initiatives. Tactical dashboards help guide users through the decision process. They capitalize on the interactive nature of dashboards by providing users the ability to explore the data.
The detail level of a tactical dashboard falls between the strategic and operational dashboards. A tactical sales dashboard can track your sales target (actual revenue vs. forecasted revenue). It allows for various filters and segmentation, including region, sales manager, and product. An operational dashboard would alternately track sales of these specific products against their competitors at different times throughout the year. As they are a bit higher level, tactical dashboards also tend to include more data visualization than operational dashboards. Let’s see this through an example in project management.
a) IT project management dashboard
The example below shows a detailed overview of a project with specific timelines and the efficiency of the parties involved. You can define specific risks, see the overall progress, and average times of conducting specific tasks. After the project is finished, you can create a comprehensive IT report, evaluate the results, and make future projects more successful.
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The goal in every IT management is to increase efficiency, reduce the number of tickets, and deliver a successful project. By having the right tool in the form of an IT operations dashboard, a single screen can provide a project manager with all the data he/she needs to analyze all the important aspects of the project. While there are various types of project dashboards, this particular visual above is set to monitor project management efforts and alarm leaders if there are any anomalies within the process.
b) Energy Management tactical dashboard
A power plant provides energy to several sectors and industries, this leads to massive amounts of data being produced every day with hidden trends and improvement opportunities waiting to be untapped. This energy dashboard aims to do just that, by providing you with an overview of every relevant aspect for a correct management of your energy business: from the total sales to the consumption by sector, to the production costs per source type, you get the big picture of the different plants’ performance.
There are several insights that can be extracted from this analytical dashboard. With this powerful management tool, business executives can understand in which sectors their energy is being consumed the most and plan their production and delivery accordingly, see what percentage of their clients is interested in renewable energy, and invest in sustainable sources of power, as well as manage production costs, and monitor the number of power cuts to ensure the best quality of service while keeping costs at a minimum.
c) Human Resources talent management dashboard
For a long time now, one of the biggest challenges for HR departments no matter the business size has been to manage talents efficiently, and therefore, keeping them for longer periods. Our talent management dashboard allows for advanced HR analytics by monitoring metrics such as costs, hiring stats, turnover, and satisfaction rates, among others. By getting a detailed look at these values HR teams can achieve successful talent management and ensure a long talent lifecycle.
The analytical dashboard example starts with a quick overview of the total number of employees in the company, an average monthly salary, and vacancies for the first quarter of the year. Paired to this, we get other stats related to hiring such as the time to fill, the new hires, net training costs, and the costs per hire.
Next, the dashboard displays the talent turnover rate by department, and the percentage of laid-off employees by periods of labor in the company (6 months, 1 year, 2 years, and 5 years). Monitoring these metrics closely can help you find the reasons for certain trends and adjust your HR strategies accordingly.
Next, we get a really important metric, talent satisfaction, that aims to measure how happy your employees are with the company. In this case, the satisfaction levels are measured through the net promoter score of each group of employees based on the time they’ve been working at your organization. Here we can see that the most loyal employees are the ones that have been working for more than 5 years in the company. Finally, we get a graph that measures the trends by category where you can see how well employees develop their skills, knowledge, effectiveness, communication, and delivery. The important thing here is to focus on retaining the right talent and keeping the workforce satisfied to avoid high turnover rates and, subsequently, costs.
d) Social media dashboard
Since there are different types of business intelligence dashboards that cover various purposes and we have expounded on LinkedIn as a separate channel that needs to be monitored daily to keep companies in touch with their follower base and expand their reach, but now, in a tactical sense, a KPI scorecard can provide multiple benefits for managing social accounts and, consequently, ensure users have enough data to generate recommendations for future. To put this into perspective, we will show a business process dashboard focused on 4 main social media channels: Facebook, Twitter, Instagram, and YouTube.
The dashboard starts with Facebook as the biggest social media network in the world with, currently, more than 2.5 billion monthly users. In our example, we can see that the number of followers did not reach the set target but it did increase in comparison to the previous period. In this case, social media managers can dig deeper to understand why and if this Facebook KPI needs particular attention.
Other metrics and channels have the same structure and tactical approach: the analysis of targets with additional comparisons, which enable managers to dig deeper into the data and derive recommendations for the future.
e) Supply chain management tactical dashboard
When you create a tactical dashboard strategy, it is important to focus on the analytical and monitoring part of the process that gives a backbone for effective, data-driven decisions. Our next dashboard concentrates on the supply chain of a logistics company.
The supply chain metrics depicted in our example above show us how a data-driven supply chain should be monitored to ensure a healthy process for the company. Additional focus on inventory management will enable the company to have a clear overview of the logistics KPIs needed to stay competitive and avoid out-of-stock merchandise.
And not just that; you can monitor your inventory accuracy and act when you see this ratio drop. Discrepancies are normal but should be kept to a minimum. Other metrics such as the inventory-to-sales ratio and the inventory turnover show the financial stability of a company – you need to know the ratio between your sold items and items in stock. The turnover will then measure how many times per year your company sells its entire inventory, adding up the efficiency of your organization: you can then analyze it on an operational level (remember our operations dashboard examples?) and see how you answer the demand of your products, what kind of operational practices you have and how your shipment management works, for example.
By fully utilizing logistics analytics, you stand to reap great rewards in your logistics business, and, ultimately, manage to retain customers.
The Do’s & Don’ts For A Successful Business Dashboard Implementation
So, you are now sold on the power of dashboards. Before you run off to the dashboard printing presses, we mean data visualization software, let’s talk about using the right ways to build and use dashboards. It is always best to start off with the right plan and implement dashboard design principles that will take into account the most relevant data of your company. A successful dashboard implementation will:
Save time across an organization: IT, analysts, managers, C-suite, etc.
Save companies money by highlighting unnecessary operational costs
Provide insight into customer behavior
Effectively align strategy with tactics
Ensure a goal-driven and performance-based data culture
Encourages interactivity and analysis
An ineffective dashboard implementation doesn’t maximize these dashboard benefits and can quickly derail any data-driven culture. Have no fear! Read on to see how you can easily avoid dashboard fatigue at your organization.
There are 2 most important parameters to keep in mind when implementing a dashboard:
Don’t “data puke”
Choose the right type of dashboard
Avinash Kaushik, Co-Founder of Market Motive and Digital Marketing Evangelist for Google, has great insight into some of the ways that dashboards fail. He has also come up with some rules for creating powerful dashboards. Kaushik’s biggest, and most entertaining, rule is “don’t data puke.” It is important to remember that dashboards are not just reports. Make sure your dashboards include insights, recommendations for actions, and business impact. It also needs to deliver context! You don’t want executives and whoever else ends up with your dashboard making their own interpretations of the data. A dashboard should tell a clear enough data story where interpretation is unnecessary. Also remember, when it comes to KPIs, segments and your recommendations, make sure to cover the end-to-end acquisition, behavior, and outcomes.
Kaushik drives his rules home by stating “This will be controversial, but let me say it anyway. The primary purpose of a dashboard is not to inform, and it is not to educate. The primary purpose is to drive action!”
Now You Can Get To Dashboard Building!
By knowing the difference between the dashboard types, you can ensure you are presenting the right information to the right people, at the right time and using great data visualization types. While still stressing that you should always know what you are building, sometimes your strategic dashboards may seem a bit tactical, and the tactical dashboard a bit operational. Don’t worry about it. Self-service analytics give you the opportunity to best fit dashboards to your needs and create a dashboard strategy that will establish and develop a data-driven business environment.
https://nexumbs.com/wp-content/uploads/2021/10/cmo-marketing-dashboard.png613817Nexumhttps://nexumbs.com/wp-content/uploads/2021/03/logo.pngNexum2021-09-30 22:14:262021-10-04 22:35:10Make Sure You Know The Difference Between Strategic, Analytical, Operational And Tactical Dashboards
Business intelligence (BI) is the practice of blending and visualizing proprietary enterprise data to discover trends and patterns that can help businesses make better, data-driven decisions. In today’s digital age where data rules supreme, picking the business intelligence software that will turn your data into actionable insights can be a critical choice that impacts your company for years to come.
Since BI solutions span across the whole spectrum in terms of cost and features, it can be difficult to evaluate all your options and find the right one, but taking the time to do so properly can be one of the best investments of time you can make for your business. The evaluation process starts by gathering a template of business intelligence requirements; these requirements determine what you need to look for in a vendor.
After reading this article, you will:
Ask yourself and answer seven key questions to start the BI software selection process
Evaluate the business intelligence functional requirements that matter most to you
Understand the next steps to take in the BI search
During the selection process, you’ll find that not all vendors include the same features. Without understanding your organization’s needs and requirements, it’s easy to get confused and distracted by shiny features that one vendor may offer that are not offered by others. But if they don’t have a functional requirement that you absolutely need, then their product will be a waste of your investment. By preparing your business intelligence requirements gathering template, you can set a course for your search that navigates through obstacles and helps your company be more efficient in its quest for the right BI tool.
After reading this article, you will:
Ask yourself and answer seven key questions to start the BI software selection process
Evaluate the business intelligence functional requirements that matter most to you
Understand the next steps to take in the BI search
During the selection process, you’ll find that not all vendors include the same features. Without understanding your organization’s needs and requirements, it’s easy to get confused and distracted by shiny features that one vendor may offer that are not offered by others. But if they don’t have a functional requirement that you absolutely need, then their product will be a waste of your investment. By preparing your business intelligence requirements gathering template, you can set a course for your search that navigates through obstacles and helps your company be more efficient in its quest for the right BI tool.
Considerations
5 W’s and 2 H’s: Who? What? When? Where? Why? How? How much?
These are key questions to ask when seeking information, whether you’re a journalist or a software buyer.
WHO: Who will be using the solution?
WHAT: What features do we need?
WHEN: When do we need it?
WHERE: Where will we deploy it?
WHY: Why do we need BI?
HOW: How will we adopt and implement it?
HOW MUCH: How much can we spend?
When considering these questions, think about the following: Who will actually be using the solution – data analysts, businesspeople, or stakeholders? What business intelligence functional requirements are must-haves? Do you need something like a SaaS (software as a service) solution that can deploy immediately and eliminate the difficulties of on-premises installation? What deployment option is best for you, according to the resources your business currently uses? And why exactly do you need BI – what specific benefits can a BI tool bring you? It’s also important to keep in mind the logistics of user adoption and budgeting.
Answering these questions before you start looking will help you to identify pain points and better understand your needs so that you can evaluate software more effectively.
Top BI Requirements Checklist
After gathering your requirements, you can properly assess the vendors on your shortlist. By ranking your list, you can skip vendors from the get-go if they don’t offer one of your top requirements, saving you valuable time in the selection process.
So what kinds of business requirements should you have? Here is a template of the 15 top business intelligence requirements that you should consider for your list.
Functional Requirements
Getting down to basics: what are some must-have BI capabilities? What fundamental features would add the most value to your company and employees’ work? The platform functions of business intelligence software establish the baseline of the system. Many global companies work across borders, requiring internalization and localization tools like other languages, fonts, time zones, and currency formatting options. Some functionalities, like projects or workspaces, help teams or departments work more effectively, together or apart. Collaboration tools such as messaging, comment threads, email, or Slack integrations make it easy to start important conversations and keep them going.
Globalization Support
Projects or Workspaces
Collaboration and Information Sharing
Decentralized Analytics Environment
Write to Transactional Applications
Dashboarding and Data Visualization
When we say “business intelligence,” what comes to mind? For many users, it could be a colorful dashboard, full of graphs, charts, and more. Dashboards are a staple of business intelligence frankly because they work: they reveal the underlying value of data in a format that people can look at and understand in seconds. It’s no surprise then that data visualization is one of the most important requirements of BI software; by translating insights into a visual medium, data visualization turns complex results into easily understandable conclusions for the user to interpret, customize and share with others. Simply put, the human brain understands visuals better and faster than it does spreadsheets, so data visualizations make it easier to present or absorb information.
Screenshot from Tableau, a vendor on our BI leaderboard for dashboarding and data visualization
Dashboards
Storyboarding
Interactive Data Visualizations
Filtering
Drill-Down and Drill-Up Capabilities
Auto-Charting
Geospatial Visualizations and Maps
Animations
Advanced Visualizations using Python and R
Auto-refresh and Real-Time Updates
Pre-Built Templates
Web Accessibility and Embeddability
Data Source Connectivity
The top BI tools can connect to a wide variety of data sources, including relational databases, data warehouses, big data ecosystems, and more. Do you use ERP platforms or CRM software in your business? If so, make sure that a prospective solution supports or integrates with your preferred tools to save time and extend the benefits of BI to your other platforms. Double-check that you will be able to import all your data into the platform, whether it lives in Excel files, a cloud storage system, an on-premises server – or a combination of all of the above. Doing so ensures that your BI tool will deliver full visibility into all your operations and processes.
Standard Files (i.e. Excel, CSV, XML, JSON, PDF and more)
Statistical Files
Relational and NoSQL Databases
JDBC, ODBC and Parameterized Connections
Big Data Ecosystems
Enterprise BI and ERP Platforms
CRM, Customer Success, and Marketing Platforms
E-Commerce and Accounting Platforms
Social Media, SEO, and Web Analytics Platforms
Cloud File Storage Systems
Project Management and Enterprise Messaging Platforms
SFTP/FTP Support
Data Management
It’s important to consider how a BI tool manages and handles your data. Data exploration and data preparation let users describe, profile and cleanse data from large data sets to identify initial patterns. These patterns can be mapped out and diagrammed as to how the sources of data will fit together and flow into each other through data modeling and multi-dimensional analysis. Data blending then combines data from multiple data sets and different file formats from disparate sources to create a single, data warehouse or dataset ready for processing or analysis. Finally, data governance grants users oversight, making sure that all analytics processes and insights comply with business policies and procedures, ensuring data integrity, and mitigating the risk of having multiple sources of truth.
All of these functionalities help users prepare, collect and organize data to ensure greater visibility and more accurate results overall.
Data Exploration
Data Modeling
Data Preparation
Data Blending
Extract, Transform, Load (ETL) Tool
Metadata Management and Data Catalog
OLAP and Multi-Dimensional Analysis
Data Governance
Advanced-Data Preparation using Python and R
Data Querying
While a database can potentially hold a wealth of information and trends, users can only harness that potential through the data query. A query is a request for data written in a special syntax, often Structured Query Language (SQL), from a database that extracts information and formats it for consumption and analysis. Data querying can perform calculations, automate tasks or dig deeper through data mining, which uncovers hidden trends and relationships between data points. Though more specialized for the fields of data science and big data than business intelligence specifically, it is certainly a feature you can consider depending on your business needs.
Query Multiple Data Sources
Complex Queries
Scheduled Queries
Readable and Modifiable SQL
Multi-pass SQL
Batch Updates
Visual Querying
In-Memory Analysis
Live Connection
Data Analysis
Data analysis turns raw information into actionable insights, helping businesses maximize the value of their data to make better business decisions. Data analysis assists users in extracting value from operational information and empowers them to take a deeper look into their business. There are four main types of analytics: descriptive, predictive, prescriptive, and diagnostic. Each is useful and important in its own way and not all BI solutions can perform every type of analysis, so it’s important to identify your data analysis needs and make sure that prospective solutions can address them.
Ad-Hoc Analysis
Segmentation and Cohort Analysis
Cluster Analysis
Scenario and What-if Analysis
Statistical and Regression Analysis
Time-Series Analysis and Forecasting
Predictive Analytics and Predictive Modeling Markup Language (PMML) Support
Text Mining (Text Analytics) and Sentiment Analysis
Social Media and Web Analytics
Geolocation Analysis
Advanced-Data Analysis using Python and R
Internet of Things (IoT) and Streaming Analytics
Augmented Analytics
Augmented analytics play a huge role in enabling organization-wide data literacy, empowering all users with the power of self-service BI, while freeing up IT and data scientists professionals for more specialized projects. Augmented analytics is the use of technologies such as machine learning, artificial intelligence, and automated algorithms to analyze data, accelerating the work done by human data analysts and data scientists. Through augmented analytics, modern BI solutions can now also process data faster and return deeper, more valuable insights with minimal human bias. They can provide automated predictions and prescriptions, helping users prepare for what-ifs. Through natural language generation and natural language processing, they can simplify data analysis and make actionable insights accessible to users without coding knowledge.
No longer just a buzzword, augmented analytics is often referred to as the future of business analytics and Gartner predicts that by the end of this year, more than 40% of data science tasks will be augmented. If you want a BI solution that’s fully equipped for the future, augmented analytics may be a feature you want to consider for your requirements list.
Example of augmented analytics, featuring a system automatically turning data into written sales reports
Source: Oracle Analytics Cloud, a vendor on our BI augmented analytics leaderboard.
Augmented Data Preparation
Automated Descriptive Insights
Key Driver Analysis
Automated Anomaly Alerting
Autogenerated and Analyzed Segments or Clusters
Auto-generated Forecasts or Predictions
Contextualized or Relevant Insights
Automated Feature Generation or Selection
Automated Algorithm Selection and Model Tuning
Automated Model Packaging and Monitoring
Text-Based and Voice-Based Natural Language Search
Reporting
You may think that reporting and analysis are the same things, but they are very different in terms of their purpose and delivery. Reporting organizes data into information that displays how different areas of a business are performing, while analysis transforms that data into insights. Reporting shows what is happening in a business, while analysis explains why it’s happening and what can be done about it. While reporting without BI tools is often IT-centric, many BI solutions break down this barrier and allow for self-service reporting so that users can generate their own reports. This helps users get reports in minutes, not days while alleviating the burden on a company’s IT department to deliver reports. The productivity and efficiency that reporting can provide make it important to consider in your business intelligence report requirements template.
Embedded analytics refers to a BI solution that can be embedded into other programs to perform in-application analysis, offering features like reporting, data processing, drill-down, and more from directly within the system. It delivers the benefits of business intelligence and data-informed decision-making to users of an existing platform without requiring that they access a separate BI application. If you’re looking to add embedded analytics to an existing platform and don’t want to have to build an in-house analytics solution within your chosen application, you can purchase an embedded BI tool that supports out-of-the-box integration.
Here are some embedded BI requirements:
Embeddability
White Labeling
Multi-Tenancy Support
Version Control
Mobile App Embeddability
Embedded Reporting
Secure Write-Backs
Background Processing
Integrated Workflow Actions
Native Mobile Apps
Responsive Web Design
Mobile Dashboards and Reports
Offline Mode
Push Notifications and Alerts
Mobile Collaboration
Mobile Geospatial Analysis
Scan Machine Readable Codes
Security
Security is one of the bigger issues in software, especially when choosing a cloud deployment, so it makes sense that checking a prospective vendor’s security protocols and certifications is a best practice for software selection. User filtering and permissions are essential features for a BI solution. On many platforms, administrators can set limits on who can access, edit and export files, data, and dashboards based on roles, teams, or licenses. Single sign-on and trusted authentication provide secure automatic access to authorized users who have already authenticated themselves through the corporate system. Encryption protects data while at rest and in transit. Additional features like activity tracking, auditing, row-level security, and monitoring help keep an even closer eye on data security.
Authentication Protocols and Systems
Single Sign-on and Trusted Authentication
Object-Level Security
Row-Level Security (RLS)
Column Level Security
User Filtering
Application Activity Tracking
Integrated Security
Encryption
Annual Security Audit and Penetration Test
Mobile BI
Business intelligence no longer stays in the office; it goes with us wherever we go. Through native mobile apps and responsive web design, many platforms help extend insights to your Android or iOS phone or tablet, seamlessly fitting a variety of screen sizes and maintaining dashboard interactivity. Often, mobile support includes push notifications or alerts that allow users to take immediate action, as well as collaboration tools. Some platforms even emphasize mobile-first optimization or support offline mode to enable access to insights even with limited or no network availability. If your employees or users are frequently on the move, support for mobile devices is critical to making sure everyone has access to the platform wherever they work.
Deployment Environment
Your chosen deployment method depends on the existing infrastructure of your business and your storage and security needs. An on-premise solution appeals to organizations that want to keep their proprietary data in-house, but not every BI tool supports Windows, Mac, and Linux or offers online access. Alternatively, cloud-based software as a service (SaaS) offering provides security and on-the-go availability but may incur higher maintenance fees. Perhaps you want a hybrid deployment to maintain control over infrastructure and costs by hosting the platform on a private cloud, a public cloud IaaS like Amazon Web Services or Azure, or a multi-cloud environment. Whichever deployment option you use, checking out whether the platform supports migration to and from the cloud and on-premises environments is also worthwhile, in case your company’s needs shift in the future.
On-Premise OS Support
Cloud-Based SaaS Option
Self-Hosted Deployment
Hybrid Deployment Option
Migration Option
Pricing and Plans
As mentioned earlier, every company has a budget to work with and it’s important to consider that budget when compiling requirements. Is the vendor’s pricing model a recurring subscription or perpetual license? Costs can vary depending on the deployment option as well as how many features, licenses, and users you need. Additional costs may come in the form of implementation, training, and support services. Some vendors provide transparent pricing but others don’t, so you’ll need to make sure to do your research so you don’t get hit with unexpected hidden fees.
After you have established your budget requirements, you can then compare BI pricing and costs between vendors that offer products that suit your needs.
License Type
Number of Licenses Needed
Pricing Model
Project Budget
Pricing Factors
Implementation Cost
Support Cost
Training Cost
Extensibility, Availability & Scalability
These three features collectively refer to the ability of the software to be expanded, scaled, and stretched as your company’s needs change. As a major investment of time and money, BI tools must evolve and adapt with your business to provide maximum value; the extensibility, availability, and scalability of a product determine how well a product can accomplish that. These factors will affect the long-term viability and user adoption of a BI solution as your organization continues to generate more and more data, so make sure to choose a tool that can keep up with your company’s expected growth when assembling your business intelligence requirements gathering template.
Scalability
Dynamic Scaling
High Availability
Fault Tolerance
API Extensibility
Document API
API Integration
IDE Support
Vendor Qualifications
The product you’re buying is only as good as the company selling it. Before purchasing software, it’s a good idea to look into the vendor and evaluate what they offer to their clients in terms of services and support. While 24/7 technical support is ideal, it’s definitely not standard, and even that doesn’t guarantee fast response times. Researching your prospective vendors and their estimated response times will help you get a feel for how long you might be waiting if your platform encounters an issue. Some vendors may offer premium or paid plans that provide priority technical support, so if a guaranteed speedy response is important to you, be sure to look into these options as well. Other things to consider include services to help accelerate implementation and user adoption, as well as the resources at users’ disposal for self-help, community discussion, or instructor-led training.
Phone, Email, and Chat Support
24/7 Technical Support
Forum/Community Support
Service Level Agreement (SLA)
Support Issue Priority Levels and Response Times
Maintenance Contracts and On-Premise Maintenance Support
Implementation Services
Free Trial
In-Product Help and Suggestions
Training and Certifications
Social Media Presence and Responsiveness
Reviews and User Sentiment
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Our relationships with enterprise software vendors are almost as important as the products themselves, and nowhere is this more true than with embedded BI vendors.
Yes, a CRM or ERP system may become fundamental to your daily operations, but an embedded BI solution becomes fundamental to your product. Your relationship with your embedded BI vendor will, therefore, not only directly impact your customers, but also your sales prospects, product roadmap, and brand identity.
The stakes are higher, so getting an early read on the vendor’s customer care practices is critical.
This isn’t news, of course. No customer willingly walks into a crummy experience, but plenty discovers that they’ve mistakenly stumbled into one. It’s hard to spot problematic customer care without the benefit of hindsight. If you’ve never been an embedded BI customer before, you barely have a sense of what your needs will be, let alone how a vendor would best go about accommodating them. We don’t know what we don’t know!
But with a little insight into embedded BI partnerships, you can hone in on the relationship details that will really matter down the line and evaluate the vendor relationship with more than a quick gut check. We’re going to explore the three qualities most essential to an embedded BI vendor’s customer care; but first, we’re going to dig a little deeper into why this relationship matters so much in the first place.
Why Customer Care Will Make or Break Your Embedded BI Project
A vendor’s ability to advise and support its customers matters most when the product or service being transacted is complex, costly, or long-term. Embedded BI, in most cases, is all three.
When a software provider purchases embedded BI for the first time, they typically rely on the BI vendor for guidance and expertise. This is particularly true of small-to-midsize companies who often have fewer specialists on staff.
The customer has never implemented or delivered BI before, and yet, in the few months it takes to deploy, they must become knowledgeable enough not only to administer the solution, but also skilled enough to train their end-users, provide technical support, and effectively market the new features.
All that know-how needs to come from somewhere, and that somewhere in the vendor.
An embedded BI vendor essentially becomes an extension of the customer’s team, helping them overcome snags as they gain skill and experience.
This knowledge transference is critical to long-term success, as the Business Application Research Center (BARC) found in an ongoing survey. “There is a clear correlation between the quality of implementer support and the achievement of business benefits in BI projects,” writes BARC in their analysis. “A lack of expertise on the part of the implementer can be especially damaging to the success of a project.”
So not only is customer care important in addressing immediate challenges, but it’s also critical in building the customer’s self-sufficiency over time, equipping them with the knowledge and troubleshooting skills they need to captain their BI initiative ongoingly and evolve as they scale.
Assessing an embedded BI vendor’s customer care practices can be challenging, particularly during the sales process. How can you tell whether the white-glove treatment you receive as a prospect will persist after you sign? We put together a three-part plan to help you and your team answer that question.
Is Embedded BI Vendor Transparent?
Honesty and clear communication go a long way toward fostering trust between vendors and their customers. Use the following tactics to get a read on a prospective embedded BI vendor’s transparency.
Pricing. Find out all you can about what your implementation will cost, factoring in any alterations you might make one or two years down the road. Some embedded BI vendors offer straightforward answers while others gradually reveal a minefield of add-ons, usage caps, and hidden fees.
Also, take note of when the vendor gives you pricing details. Some will only provide that information after you’ve had a discovery call and sat through a product demonstration. If their price turns out to be way outside your budget, you’ve wasted your time. If their price is feasible, you now have to weigh whether or not their lack of transparency is a deal-breaker.
Capabilities. According to Jorge García, senior analyst of BI and data management at Technology Evaluation Centers (TEC), “vendors tend to hide their product limitations.” Validate embedded BI vendors’ claims regarding their solution’s capabilities by having your technical staff listen in on sales calls and familiarize themselves with the documentation. Take note if the vendor shows signs of being less than forthcoming about what the product can do and how, as this evasiveness will carry significantly more weight when you’re in production.
Roadmap. In addition to finding out what’s on the vendor’s roadmap presently, find out what their process is for keeping clients apprised of future enhancements. Transparency around roadmap planning is important, as your team may one day be invested in a new feature and need forecasting to plan your rollout of the update accordingly.
Service-Level Agreement. Request a draft of the SLA you’d be signing in partnership with the embedded BI vendor, and take your time reviewing it. The more detailed the SLA, the more it will protect you against lapses in the level of service. Look for clear definitions and timeframes around things like issue priority levels, acknowledgment, and resolution. Don’t hesitate to ask for further clarification on anything that seems vague or confusing, and make sure to add any necessary modifications in writing.
Can Your Vendor Guide You?
There’s a good chance this is your first time implementing embedded BI — and if it’s not, there’s a high probability your last implementation didn’t go as planned. While the success of a BI initiative ultimately depends on you, the customer, the vendor plays a major role in guiding and supporting you through the process, particularly if you’re new to the BI space. Here’s how to find out if an embedded BI vendor will have what it takes to help you meet your goals.
Structured evaluation. A structured evaluation centered on a custom proof-of-concept is absolutely critical to determining product fit. An embedded BI vendor should be able to provide you with an outline of the evaluation process, complete with a rough timeline and guidance on how to test the right areas of the application without overinvesting in the process. If a vendor leaves you to your own devices during this pivotal stage, take it as a sign that they will be unlikely to advise you in your future endeavors.
Structured implementation. Before you enter into contract negotiations with an embedded BI vendor, learn all you can about their implementation process. A vendor adept at caring for its customers will have a plan for facilitating the implementation — not just from a tactical standpoint, but a strategic standpoint. Find out how much guidance you can expect to receive on things like data management, user management, security, performance, and scaling. Check also to see whether these services are included or at an additional cost.
Training materials. Third-party software providers should not only be able to train your staff but also equip them with the videos, manuals, and documentation they’ll need to craft a training program specific to your end-users. Confirm that you will have permission to reproduce modified versions of their training materials.
Will Your Vendor Grow With You?
The only constant in life is change — it’s cliché because it’s true. Your business needs will change, and your embedded BI product will change. The question is, will they change together? Find out if your embedded BI vendor’s customer relationship practices will allow you to weigh in on the platform’s future.
User testing and betas. Find out if customers can volunteer to participate in user testing and beta testing. This is a way to directly influence the product’s direction and make your preferences known. Every vendor’s testing practices differ, however, so it’s worth asking specifically about opportunities to take new features and designs for a trial spin.
Regular status updates. An embedded BI vendor who routinely checks in with customers will simply be more aware of their wants and needs than a vendor who doesn’t. Message boards and ticketing systems are important, certainly, but they’re no substitute for status calls. For one, status updates give the BI vendor insight into what’s going right — not just what’s going wrong. It also sometimes leads customers to discoveries about how they could be utilizing the solution to a better effect. This focused, individualized attention forms the bedrock of a sustainable vendor partnership.
Frequent product releases. Be sure to ask about embedded BI vendors’ release cycles before you buy. There will generally be two different types of releases: new versions and version updates or “patches.” Both will impact your product roadmap. The new version schedule will give you a sense of how often to expect new features while the update schedule will speak to issue fixes. Also, find out how long versions are supported (and how) before they are sunsetted. In general, the more frequently vendors update their product, the likelier they are to introduce changes significant to you and your customers in a timely fashion.
Client advisory board. Does the embedded BI vendor in question actively solicit customer feedback? Those with a client advisory board or similar are usually eager to share their plans and learn how they might improve. Surveys might fill this function to some extent, but a sit-down conversation with the vendor and other customers makes for a better dialog while also fostering a sense of community. Ask about such opportunities to make your voice heard.
With this checklist in hand, you’ll have the tools to assess a vendor relationship before locking it in for the long haul. The success of an embedded solution rests on the preparedness of its implementation team, and implementation teams need to know they can trust their embedded BI vendors to communicate transparently, give good guidance, and develop the product in their interest.
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As more businesses realize the importance of end-to-end Business Intelligence (BI) solutions, demand for data engineers has risen significantly. Data engineers are responsible for extracting, cleaning, and normalizing data while building data pipelines that data scientists can utilize to explore and build models. They are the backbones of data algorithm development and infrastructure design.
To succeed in their work, data engineers require a variety of data management tools, data warehouses, programming languages, and a host of other tools for data analytics, data processing, and AI/ML. This article discusses essential tools that data engineers require to create effective, efficient data infrastructure.
1. Amazon Redshift
Amazon Redshift is an excellent fully-managed cloud-based data warehouse powered by Amazon. It’s the optimal choice when it comes to choosing a solution to warehouse your data. Your data should be easy to access, well-sorted, and easy to manipulate and store to get maximum value from it, and Amazon Redshift offers you just that. Features that make Amazon Redshift an excellent data warehouse solution include:
Ease of use
It enables fast scaling with few or no complications
It’s cost-effective
It provides robust security tools
2. Databank platform
Databank is an excellent data observability platform for data engineers. It monitors what is happening in a data pipeline, allowing you to create reliable analytics, which helps you produce trusted data products. It offers insights that monitoring tools cannot. Apart from telling you what went wrong, data observability platforms also reveal the cause of the problem and recommend actions to fix the problem.
3. Apache Spark
Companies today understand how crucial it is to capture data and make it available within the organization quickly. Stream processing allows you to process data as it is being produced or received, and Apache Spark is one such implementation of stream processing. It is an open-source analytics platform for big data processing and supports different programming languages, including Python, R, Scala, and Java.
4. Apache Airflow
Automating some tasks plays a key role in any sector and is an excellent way to reach functional efficiency. Without automating some tasks, you end up repeating the same task several times. As a data engineer, you have to deal with workflows such as collecting data from several databases, processing, cleaning, uploading, and reporting it. Consequently, it would be great if you automated some of these tasks.
Apache Airflow is one such tool that can help you schedule tasks, automate repetitive tasks, and streamline workflows. It makes running complex data pipelines easy. Apache Airflow is easy to use and has a great user interface that allows you to monitor progress and troubleshoot problems when required.
5. Snowflake
Snowflake is another excellent data warehouse with unbeatable data sharing capabilities and architecture. It offers the concurrency, elasticity, performance, and scale that today’s businesses need. It can easily ingest, transform and deliver data for deeper insights, helping to streamline data engineering activities. Among the unique benefits of this virtual data warehouse include:
Ease of use – Snowflake has a simple and intuitive interface
Fully automated – With snowflake, you don’t have to worry about updates, configuration, scaling your infrastructure, or failure
Great tools like Mode Analytics, Tableau, Looker, and Power BI, which allows you to query data against large datasets
Cost-effective
Flexibility
Robust security
6. SQL
Structured Query Language (SQL) is one of the key tools that data engineers need to build logic business models, extract key performance metrics, execute complex queries, and create reusable data structures. Additionally, SQL is one of the key tools that help access, insert, update, modify and manipulate data using data transformation techniques, queries, and more.
7. PostgreSQL
One of the most popular open-source relational databases, PostgreSQL, is a crucial tool for data engineers. It’s designed to work with large datasets, which makes it appropriate for data engineers. It is also popular with data engineers because of its extensibility and flexibility.
8. Tableau
Tableau is probably the most popular data visualization tool for business intelligence. You can use it to shape your output in the form of interactive charts and graphs. It also provides great visuals, and even a person with no knowledge about graphic design can create some incredible interactive charts and graphs. Tableau is mobile-friendly, and you can use it on your mobile device.
9. Power BI
Power BI is an excellent business intelligence tool by Microsoft. It’s an open-source cloud-based platform with a simple interface that allows users to create their own dashboards and reports.
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Artificial intelligence is often portrayed as an all-knowing machine. However, it can also learn as it goes. Location intelligence is one of the most important lessons AI should be taught. It will help you find people, assets, and events in different locations and understand how they relate to one another.
Think about all the technology that has helped the pandemic-era tech thrive based on location: the delivery apps, COVID testing, vaccine dashboards, safe-to-go store orders, and vaccine dashboards.
Location intelligence is the output of a geographical information system (GIS) and provides critical context, which forms the basis of what machines can learn. Here are four ways to ramp up AI with location intelligence.
Top 4 AI Business Applications for 2021
1. Make Keen Predictions with Recommender Systems
AI does not require huge amounts of historical data to get started. Instead, AI uses reinforcement learning whenever an answer isn’t known. To find the best solution, machines use trial and error to solve problems.
Jensen Huang (CEO of NVIDIA semiconductor-maker) described the near future as “the automation of automation.” Huang spoke with Times magazine and said that AI would improve complex operations like food production, logistics, health care, and healthcare.
Huang spoke about recommender systems, which use reinforcement learning to predict people’s choices based upon their preferences and habits. He called them the most important AI system of our time.
Although this AI doesn’t need historical data to predict better outcomes, it still needs context. Harvard Business Review notes that designing the inputs, actions, and rewards that nurture machine learning upfront is crucial. GIS adds location intelligence to a recommender program, allowing it to predict what might happen and how.
2. Find Trends When and Where They’re Happening
It’s one thing for AI to do all the heavy lifting, crunching profit/loss statements and producing a result. It’s much more helpful to give AI context that helps make sense of these numbers. AI can enhance decision-making and assist company leaders in identifying trends with the right data inputs (e.g., location intelligence).
Leading banks use machine learning with location intelligence to analyze the revenue from existing branches and pair it with rich demographic data from possible expansion areas.
This allows them to predict the best locations for new branch openings. They can decide where to expand to attract new customers and not pull existing customers from their branches.
Companies use AI-powered location-based imagery and weather data to determine which neighborhoods are best for their rooftop sun-catching technology in the solar panel industry.
Location intelligence is used to help determine the best route. Insurance companies have taken this literally. Companies built around risk formulas expect fewer claims by combining location intelligence with AI to forecast accident-prone roads and recommend safer routes.
3. Scan Big Data to Locate Hidden Markets
Companies that are growing quickly have access to the data they need to expand. However, it can be difficult to find the right information.
Artificial intelligence makes it easier to analyze the vast amount of data required to forecast spikes in demand and identify areas with higher margins. This includes collecting several thousand points of demographic data to determine potential customers and where they may be.
Wireless network companies have embedded location intelligence into their process. They can use the troves of data regarding dropped calls to map potential tower expansion sites. It’s all gone! Companies can map their growth plans more precisely by adding layers of information about the area’s demographics and expected growth.
Retailers can use GIS and AI technology to determine which physical locations are most likely to drive future sales. This creates the highly sought “halo effect,” where store owners capture customers in person.
4. Plan Ahead by Ingesting Imagery and IoT Data
The amount of data streaming from IoT sensors and satellites is almost overwhelming. The results of filtering that data through an AI app with location context are even more clear.
The robot scans the aisles at a sporting goods retailer, which has equipped its products with RFID sensors. It can predict what customers will want and where employees should concentrate their restocking efforts.
Stores that have moved to online ordering and pickup and are not looking back after the pandemic have used location intelligence to train their systems to determine when to make someone’s drink or get their groceries ready. This is based on the person’s proximity to the store.
Future growth areas can be monitored with the same sophistication as military-grade surveillance. Aerial imagery can be used to scan vacant lots with earthmovers and cranes so that AI can identify areas where houses or retail developments are likely to be built.
Artificial Intelligence with location intelligence can scan aerial images of competitor parking lots to determine which model cars are outside. This information is useful for potential demographic data.
AI in the energy sector uses satellite imagery to detect competitors who might be building oil wells or using LIDAR-equipped drones for measuring their power lines. AI can analyze 10 billion data points, which would take human analysts over five years to process manually. This allows AI to detect obstacles like vegetation that need to prune or be removed.
Hinting at the Future of AI
With consumer behavior changing quickly and external risks like the COVID-19 epidemic and climate change disrupting, machine intelligence can be greatly improved. Machine learning can be greatly enhanced by adding geographic information to it. GIS can be incorporated into AI applications to gain location intelligence.
AI, which includes reinforcement learning, is aided by valuable location information. This helps decision-makers assess and make decisions confidently and in much less time than it takes for humans to dig into the data.
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A lack of BI adoption is slowing down organizations of all sizes from making the most of the massive amount of data they’re producing daily. BI applications and tools enable greater insight and intelligence into areas of business models never analyzed or understood.
Of the many areas BI is contributing to today, one of the most valuable is gaining greater insights into customer behavior by predicting buying outcomes. By knowing more about their customers, businesses can streamline their business processes and make them more efficient. When this occurs, they excel at improving product quality and customer service.
Data is proliferating in each of the six areas shown in the graphic below. Data scientists, cloud-based applications, and the Internet of Things (IoT) are among the most prolific sources of data today. The combination of these factors is driving a data onslaught in every organization. BI applications and tools need to gain greater adoption for companies to make the most valuable data from each of these sources.
Facing The Challenges Of BI Adoption
The problem of increasing BI adoption is a multidimensional one with no quick fix. Ideally, there needs to be a balance and alignment of the organizational, process, and technological factors for business intelligence adoption to succeed. Galvanizing all three of these core areas needs to be a central business focus that everyone can identify with and take an active part in accomplishing. This is where the majority of business intelligence adoption strategies fail.
There’s often no unifying business purpose that benefits everyone; it’s just not how the world works. But when every employee using the system has a strong sense of ownership and purpose, all the strategic areas needed to improve BI adoption can fit into place, increasing adoption rates. A senior management champion alone can’t galvanize the purpose of a BI system as powerfully as a shared goal and a desire to excel with the new BI system.
Overcoming Technology-Related Challenges To BI Adoption
The leading technological factor that’s slowing down BI adoption is the lack of integration with legacy and 3rd-party databases and the many enterprise systems that provide greater contextual data. BI applications running on a single database have limited potential to deliver contributions to organizations. The greatest technological inhibitor to business intelligence adoption is non-integrated BI and analytics tools in which users are manually importing data to get greater insights.
Many organizations begin their BI integration strategy by concentrating on legacy and 3rd-party databases before moving on to larger, more complex integrations. These integrations involve enterprise applications like customer relationship management (CRM) and enterprise resource planning (ERP). Integrating with legacy and 3rd-party databases often requires customizing a connector or adapter, translating into professional services fees and other additional costs.
For organizations with in-house IT teams, implementation is a relatively straightforward process. It costs time, and time is often what many IT teams are very short on as they try to support groups of users across a larger organization. Fortunately, many analytics and BI applications provide advanced adapters for integrating with CRM, ERP, and other enterprise apps. It’s less expensive to purchase an adapter or connector created by the BI provider than to pay a system integrator to complete a custom integration from a BI app to an enterprise system that’s already implemented and running.
Every leadership team grapples with balancing the costs of integration versus the goal of providing real-time analytics and BI access company-wide. The following graphic provides an overview of organizations’ stages as they integrate BI applications into their IT systems and workflows.
Starting with legacy and 3rd-party databases before progressing to CRM, ERP, and other enterprise-wide systems, most organizations often rely on 3rd-party connectors and adapters to complete this work. Integrating with enterprise-wide systems, including CRM and ERP, is where the value of BI increases exponentially.
This is a critical phase of integration, as it provides customer-driven data from the CRM system, along with a wealth of transactional data from the ERP system and its supporting apps. Organizations reaching the highest strategic business intelligence adoption levels can integrate all of these systems, attaining real-time analytics and reporting enterprise-wide.
The ascension of BI adoption from only integrating with legacy and 3rd-party applications to integrating with enterprise apps is critically important for accelerating BI adoption. Without the added data from enterprise applications, BI adoption tends to stall, stop and eventually decline. From this standpoint, it’s a fair assumption that if any company wants to gain high BI adoption levels, it’ll need to integrate with CRM, ERP, and other enterprise systems that are core to their daily functions as a business.
Factors Driving Greater Adoption
Real-time integration between BI systems and legacy/3rd-party databases, CRM and ERP systems
Timeframes must be communicated company-wide regarding integration to databases and apps. That provides other departments visibility into when they need to begin their part of the BI implementation project. BI projects that attain the highest levels of adoption focus on these areas first and start building out a roadmap of integration points to guide development. In larger organizations, the project management office (PMO) manages the business intelligence roadmap, and a senior executive takes ownership of the responsibility. If an organization doesn’t have a PMO, the best approach is to define a project leader in its headquarters who can manage the BI roadmap strategy to completion daily.
Defining and acting on data quality standards early and often during the BI implementation phase
Data quality can make or break any BI implementation, as users will immediately judge the value of any BI system by the results it generates when they first use it. Making data quality a priority pays, and it helps accelerate software adoption when users see accurate reporting and analysis that reflects the actual conditions of the company.
Selecting a flexible, modular system that can scale with your user’s needs is a must-have to drive BI adoption.
BI adoption increases when a system can flex and respond to the needs of a broad base of users without forcing them to change how they work. The more modular and agile a BI system is, including the flexibility for defining custom workflows by business analysts, the greater the level of adoption will be.
The ability to customize dashboards and reports, generate advanced data visualizations, and enable more responsive self-service are critical success factors driving BI adoption.
These are must-have features in any BI application to drive greater adoption. Across the spectrum of small and medium businesses (SMBs) to enterprises, these four areas are the foundational features of applications that drive adoption. Companies that excel in these dimensions of BI include Power BI, MicroStrategy, Tableau, and Yellowfin. The following graphic provides an overview of technology priorities by organization size. It’s a part of a broader study by Dresner Advisory Services summarized in the Forbes post, Small Businesses Are The Real MVPs Of Analytics And BI Growth.
Selecting a BI application that delivers excellent customer experience and intuitive, easy-to-use, streamlined workflows are essential.
BI applications continue to improve in this area of product design. Today’s leaders include Birst, ClearStory Data, MicroStrategy, Power BI, Oracle BI, QlikView, Salesforce, TIBCO, Spotfireand, ZoomData. Based on the research by Dresner Advisory Services, Gartner and others, it’s clear that this is a future product direction of all BI vendors in the market today. Selecting a vendor that excels in this dimension will drive greater BI adoption when the implementation considers the other factors mentioned.
Roadmaps Bring Technology Key Success Factors Together
Bringing the five technology success factors together into a unified business intelligence roadmap helps everyone visualize what success looks like and helps it move faster towards that success. Every BI vendor has a product roadmap available for each product line, and several have roadmaps defining their product direction by vertical market. Making sense of all the vendor roadmaps requires organizations aiming for high BI adoption to create their own.
Defining a BI Implementation strategy defines which legacy, 3rd-party databases, and enterprise systems will be integrated. It also provides an assessment of how BI will be used. Efforts to implement business intelligence are often first focused on customer-driven advanced analytics and creating role-based dashboards.
As BI adoption grows over time, greater insights can be gained from manufacturing, logistics, and supply chain systems leading to a new base of knowledge in the company and manufacturing intelligence. Predictive analytics-based efforts shown on the right side of the following figure are often the catalyst that leads to greater operational and manufacturing performance.
Strategies To Increase Adoption
Concentrating on technology-related success factors sets the foundation for enabling greater process and organizational change. From a process standpoint, key success factors include clearly defining the business problem/processes and gaining consensus on what problems the BI system needs to solve. Second, processes need to be defined by user expectations, using an audit of their needs. Third, there need to be process workflows that allow the BI application and components to align with your user’s specific needs.
Change management plans and frameworks often take these process-based key success factors into account when defining an overall business intelligence implementation strategy. Organizational success factors include having an adequate budget defined before the project begins, support from senior management, having a dedicated BI project manager in place, a scalable team supporting that manager, a clear plan, and a dedicated implementation specialist from the provider of the BI application.
Taking the technology, process, and organizational success factors into account, here are the top five strategies for increasing BI adoption:
A clear, well-defined BI business case that gives every participating employee a chance to see how their contribution drives success
Providing the opportunity for greater autonomy, mastery, and purpose for every employee is the cornerstone to making BI adoption rates improve. The greatest BI implementations aren’t pushed to high adoption levels; employees drive them there.
Selecting a BI application with a flexible, agile architecture that can flex to changing requirements and needs, including supporting embedded analytics
The five technology success factors address having an agile, flexible BI application that can scale. Flexing across the analysis and content creation, data management, infrastructure, and embedded analytics are essential to set the foundation for businesses to implement business intelligence and grow.
An experienced management team that includes directors, vice presidents, and C-level executives who can cut through the cross-functional confusion and get things done
Contrary to popular belief that it only takes a senior-level management champion, experience has shown that cross-functional teams will often resist change. It often takes a unified effort on senior management to get BI software implementations done; all must be in favor and actively support the effort to break down barriers.
Ensuring data quality from the very beginning of the project is a must-have
Oftentimes, data quality is relegated to the last of a series of factors that companies look at when planning and developing their BI implementation. Data quality needs to be designed from the beginning to get the maximum results possible while ensuring that the BI applications being launched deliver data that users can take action on.
A business results-driven development approach needs to underscore all efforts.
Always tying back to business factors and the urgency to gain greater insights that can be turned into revenue, emphasizing business results can keep the intensity and focus at a high level until a BI project is completed. Keeping the intensity level up and focused on how BI adoption can drive revenue helps maintain it as a priority until it’s complete.
Conclusion
Today, organizations face the many challenges of improving BI adoption, and oftentimes they only get a fraction of the data they could from their systems. These five strategies for increasing business intelligence adoption help create a unified, cohesive strategy. Then, the urgency of gaining greater revenue based on insights can help fuel greater adoption.
Providing users with greater autonomy, mastery, and purpose and seeing how their contributions matter also helps. The bottom line is that BI systems are designed to flex to evolving requirements more than ever before, so taking a customer- and revenue-driven approach to defining its role improves the adoption rate.
What obstacles are preventing your business from implementing business intelligence software? How did you surpass them? Let us know in the comments!
79% of respondents use BI or data analytics tools, according to a recent TechRepublic Premium poll.
From dashboards to data visualizations — not to mention descriptive, predictive, and prescriptive analytics — the enterprise has no shortage of business intelligence and data analytics tools at its disposal.
Leveraging such tools could make measurable contributions to businesses. For example, companies could maximize an impending opportunity, mitigate future risk, meet deliverable milestones, gain a competitive advantage, and much more.
How are companies translating analytics into actionable information that can be used to make better business decisions? ZDNet’s sister site TechRepublic Premium surveyed 161 professionals to find out.
The survey asked about the type of business intelligence, and data analytics companies use and how they are used, the benefits of analytics tools, and any obstacles that have hampered analytics efforts within the company, and how well executive management recognizes the business value of analytics.
Data analytics is a major driver of corporate success. This will continue in the future, with most respondents (79%) reporting that their companies used analytics. Of the 11% of respondents who said that their company does not use any analytics tools, 21% attributed it to a lack of in-house talent, skill, or business knowledge about the tools, and 13% cited budget constraints or lack of executive buy-in as reasons for opting out from using analytics.
More than 40% of respondents reported that their companies primarily use analytics for operational or sales and marketing purposes. Improved strategic decision-making was the biggest benefit of using analytics for most respondents (65%). Other reported benefits included improved knowledge about customers (45%), operational cost savings (44%), and improved sales (32%).
According to 78% of respondents, dashboards were the most popular data analytics tool used. Another 65% used reporting tools, and 64% employed visualization tools. Although less robust, 44% of survey respondents were using analytics to support the tracking of key performance indicators (KPIs).
Microsoft was the favored vendor/product for more than half (52%) of the respondents. Vendors such as Tableau (17%), Salesforce (15%), and IBM (10%) came much further down in the rankings.
The majority (77%) survey respondents said their executive management team saw value in their deployed analytics. Only 5% reported that their executive management team saw no business value in these tools. Further, 41% of respondents reported that their executive management team was more excited about the potential of analytics than one year ago.
Yet despite relatively high perceived support from the C-suite, 19% of survey respondents wanted to see more active participation by upper management in analytics. When it came to improving the business value of analytics in their organizations, 27% of respondents believed that a sharper focus on business use cases would build value, and 21% said that their internal staff skills in analytics needed to be stronger for that to happen.
The infographic below contains selected details from the research.
https://nexumbs.com/wp-content/uploads/2021/05/data-analytics-2-metamorworks.jpg7681365Nexumhttps://nexumbs.com/wp-content/uploads/2021/03/logo.pngNexum2021-05-05 22:10:252021-05-12 22:11:04Research: Executive management recognizes business value of analytics