Business intelligence exercises are a “real world” method to get the analytical skills needed to turn raw business data into useful insights and improved business decision-making. Rather than only studying BI concepts, exercises can be used to engage students, business professionals, managers and consultants with real-life scenarios that include dashboards, KPIs, customer data, forecasting, financial reporting, supply chains and predictive analytics. The provided Veralytiq guide represents the business intelligence exercises as scenario based predetermined problems to help people gather, model, analyse and present data for real business decisions.
Business Intelligence Exercises Are What?
Business intelligence exercises are practical problems, or problems created to simulate the types of problems that companies face when conducting business. These might include importing data, data cleaning, the development of KPIs, SQL queries, dashboards, anomaly detection, revenue forecasting, customer segmentation or the development of a BI strategy.
It is not aimed at producing pretty charts. Good business intelligence endeavors are centered on a business problem or question that is being addressed and a business decision that is to be made. The Veralytiq reference makes this point strongly by suggesting that in addition to learning the use of technical tools, one should learn to make judgments in BI practice.
This is useful because the basic analytical thinking is relevant to all types of technologies and tools, whereas BI tools and platforms evolve over time.
Why Are Business Intelligence Exercises Important?
Organizations have massive amounts of data to gather from sales systems, customer relationship management (CRM) systems, financial applications, websites, inventory systems, and operational databases. But data alone does not necessarily provide any insight for business. Business intelligence activities enable students to practice all of the steps from information to action.
In a typical exercise, the language learner might be asked to:
- State a problem of a business.
- Identify the data that is required.
- Organize and clean up the data.
- Define meaningful KPIs.
- Identify and interpret patterns and trends.
- Create a visualization/dashboard.
- Explain the findings.
- Make a suitable business decision.
This process mirrors the process that is followed in real BI projects, and enables learners to understand why business context is important as well as the technical skills.
Who Are The People That Will Benefit From BI Practice Exercises?
Business intelligence exercises can be beneficial to a number of groups.
BI Analysts
Exercises can be used to reinforce technical skills in SQL, Excel, Power BI, Python, DAX, data modelling and visualisation.
Students
Practical assignments can be used to create a student’s portfolio and for technical interviews and assessment for students preparing for careers in business intelligence and data analysis.
Managers
It is not essential for managers to learn to be BI developers, but they can benefit from exercises that help them to understand how to interpret dashboards, ask questions of KPIs, look for anomalies and assess the conclusions drawn by the BI.
Consultants
Consultants can train and practice solving problems in simulated scenarios that are similar to the problems seen in their actual clients’ projects, before trying similar techniques on projects in the real world.
Structured BI practice has key audiences, including analysts, managers, students and consultants, according to the Veralytiq guide.
Business Intelligence Exercises can be of various types:
Business intelligence exercises can typically be divided into three categories – Analytical, Technical, and Strategic.
Analytical BI Exercises
In Analytical exercises, you will look for patterns and derive insights from data.
Examples include:
- KPI analysis
- Customer segmentation
- Forecasting
- Anomaly detection
- Churn analysis
- Market basket analysis
- Trend analysis
The exercises will help the learner to understand information, not just “play with data sets.
Technical BI Exercises
Technical exercises focus on the tools and processes to prepare and analyze business data.
Examples include:
- SQL queries
- Data modeling
- ETL transformations
- DAX calculations
- Power BI dashboards
- Excel analysis
- Python-based analytics
Reliable data preparation, and visualization are key to strong BI work, particularly technical practice.
Strategic BI Exercises
Strategic exercises are activities that link analytics to organizational planning.
Examples include:
- BI roadmap development
- KPI governance
- Executive dashboard design
- Data governance planning
- Business case development
- BI implementation simulations
These exercises will support learners to comprehend how analytics can assist with organisational priorities.
Exercise 1: Sales Dashboard (Business Intelligence)
If you are new to the sales dashboard, you should begin with a sales dashboard.
The scenario may be set by a company that requires to track monthly sales, best customers, sales per region, sales performance of products and order volume.
The CSV file may include columns like order date, customer, region, product category, revenue, units sold, and more.
This activity may feature:
- Bringing in the data to Power BI or Excel.
- Making a date field to represent each month.
- Creating a trend chart of revenues.
- Determining the best customers.
- Comparing regional sales.
- Creating KPI cards for Revenue, Units and AVOs.
- Adding filters for various periods in time.
The desired outcome is a dashboard that will enable users to get a rapid overview of sales performance and interact with it in an interactive way. For this type of exercise, the Veralytiq example proposes to use sample data like Microsoft’s AdventureWorks dataset.
The focus of this Business Intelligence exercise is to analyze key performance indicators and detect anomalies.
Learners learn about how to recognize unusual changes in the performance of a business using KPI analysis.
An example of this might be a SaaS business tracking its monthly recurring revenue, churn, customer acquisition cost, lifetime value and customer satisfaction.
The learner can work out the month-to-month differences and make a rule to indicate the results of excessively large differences.
A threshold, for example 15% change, could be used by a learner to determine if there are any anomalies. The following Veralytiq exercise illustrates this and also how to compare current and previous values using a SQL LAG() window function.
This is a great example of using this type of exercise to help teach an important BI principle: That unusual numbers should be investigated, not necessarily assumed to be errors.
Business Intelligence Exercise #3: Customer Segmentation
Another customer business intelligence exercise is Customer Segmentation.
An RFM model can be used to segment the customers based on purchasing behaviour, which is Recency, Frequency, and Monetary value.
The basic process consists of:
- Knowing the time of the last purchase for each customer.
- Tallying the numbers of time(s) the customer buys.
- Working out the total expenditure by customers.
- To give the three measurements scores.
- Creating customer segments.
- Conceptualizing the size and value of the segments.
The Veralytiq reference also introduces the UCI Online Retail dataset which is used as an example, and shows how to use SQL or Python to compute the RFM metrics.
Current exercise is related to data analysis and marketing decisions as there might be various ways to engage various customer segments.
Let’s take another advantage of the game of business intelligence in Exercise 4: Forecasting revenue.
Forecasting activities enable students to appreciate how past performances can be used to predict future performances.
The key is that a workout program can give you a number of years’ worth of monthly revenue series with trend and seasonality.
The learner can:
- Visualize historical revenue.
- Calculate moving averages.
- Construct an exponential forecast.
- Calculate confidence intervals.
- Compare different scenarios.
Make recommendations on the expected range to decision makers.
In the Veralytiq exercise, you will explore using Excel’s FORECAST function. ETS and FORECAST.ETS.CONFINT works, and can be used with Python’s statsmodels as an add-on.
What is crucial is that a forecast should convey uncertainty, not be a prediction of what is sure to happen in the future.
Exercise 5: Business Intelligence – Supply Chain Analytics
The other realistic scenario for creating a BI practice is supply chain analytics.
Using a learner’s dashboard, a learner can track:
On-time delivery rate
- Fill rate
- Inventory turnover
- Warehouse performance
- Shipment trends
- Supplier performance
The exercise can be based on a sample database, like Northwind, and can include SQL calculations of operational KPIs. This can then be linked to Power BI for visualization.
This illustrates the power of business intelligence to alert operational staff to the need to attend to certain areas without having to create a report first.
Automating Financial Reporting: Business Intelligence Exercise 6
Advanced BI exercises are ideally suited to financial reporting as they bring together structured data, calculations, reconciling and reporting together in one.
The first step in a practical exercise might be to start set up general-ledger transactions with account codes, departments, dates, debits, credits and cost centers.
The learner is able to create:
Each month, you will be able to see the profit and loss.
- Revenue analysis
- Cost analysis
- Actual-versus-budget comparisons
- Year-over-year comparisons
- Reconciliation checks
- Department-level drilldowns
The reference example illustrates how to use SQL to produce an overview of the monthly P&L, and then how to use Power BI to transform the data set into a financial report that is automated.
Exercise: Market Basket Analysis
Advanced exercise that looks into the value of market basket analysis.
Learners can use transaction-level retail data to create a basket matrix and implement association-rule mining algorithms, like the Apriori algorithm.
Measures that are important to consider might be:
- Support
- Confidence
- Lift
The example that uses the UCI Online Retail dataset and Python’s mlxtend library is included to illustrate how to perform association-rule analysis.
In this exercise, learners will be able to relate to analytical output and practical use like product recommendations and cross selling.
Business Intelligence Exercise 8: Predicting Customer Churn.
Predictive analytics is a component of the business intelligence process that is introduced by churn prediction.
Often, a subscription or telecommunications business is looking to identify customers that are likely to cancel.
The learner can:
- Load customer information.
- Prepare categorical variables.
- Divide the data set into training and testing data sets.
- Train classification model.
- Generate churn probabilities.
- Copy and paste the results into Power BI.
- Create a dashboard for customers with different risk level(s).
This supplied Veralytiq exercise is an example based on a publicly available IBM Telco Customer Churn dataset, and illustrates logistic regression + Power BI visualization.
One important thing to note is that predictive BI should always be a means to an end – an action. It may be easier for a commercial team to translate the potentially at-risk list into a model score if they have an operational process in place.
This Product Is A Business Intelligence Exercise That Includes Real-Time Monitoring
For more advanced learners, interested in streaming data, real-time monitoring is appropriate.
For a manufacturing scenario, the sensors might measure temperature, pressure, vibration or conditions of the equipment.
The exercise may be with:
- Simulating sensor readings.
- Transmission to a streaming or a database system.
- Linking the data to visualization system.
- Creating threshold alerts.
- The use of timelines to track change.
- Identifying one off noise from continuous abnormalities.
The Veralytiq example suggests a simulated sensor data and a Power BI dashboard of monitoring as the advanced level exercise.
This scenario is an introduction to some of the extra infrastructure requirements for near real-time analytics.
Business Intelligence Exercise 10: BI Strategy Simulation
Technical doesn’t necessarily mean the most advanced exercise.
The goal of a BI strategy simulation is to have the learner role-play a BI Strategy Leader for an organization’s BI roadmap.
The learner can start by answering the most important executive questions, by drawing out the data sources available, by assessing the readiness of the data, by prioritizing the BI use cases, by understanding the KPI calculations and by creating a roadmap of phases.
The example that Veralytiq provided suggests a three-stage approach to the roadmap: a foundation, insight and intelligence phase.
In this exercise, learners will learn that business intelligence isn’t just a dashboard or machine learning. It also needs to be governed, prioritized, communicated, data quality controlled and aligned to business goals.
Learn How To Create A Learning Path For Business Intelligence
Business intelligence activities can be more effective if there is a structured learning path.
Beginner Level
Start with:
- Excel
- Basic Power BI
- Simple dashboards
- KPI calculations
- Basic data cleaning
- Simple anomaly detection
The learner should be able to build this connection in this stage, between business questions and business measures.
Intermediate Level
Move toward:
- SQL
- Data joins
- Customer segmentation
- Forecasting
- Data models in Power BI
How To Analyze KPIS More In-Depth
The objective is to get used to utilizing various datasets and more complicated analytical issues.
Advanced Level
There are advanced business intelligence exercises that can introduce:
- Python
- Machine learning
- Financial automation
- Association rules
- Predictive analytics
Create Advanced Power BI dashboards in a streamlined manner.
Technical output should now start to be linked with business action more and more.
Expert Level
Expert practice may be:
- Real-time monitoring
- Streaming data
- BI governance
- Data strategy
- Executive reporting
- BI roadmaps
Enterprise analytics architecture
The trajectory of the Veralytiq reference parallels the same one as described above, moving from creating a dashboard to analyzing KPIs to doing predictive, real-time and strategic BI exercises.
A Set Of Tools To Aid Business Intelligence Exercises
Appropriate equipment is needed for various exercises.
Again, Excel is appropriate for the novice as it offers formulas, pivot tables, charts and simple data analysis without the need to program.
Power BI can be helpful for creating dashboards, interactive reporting, data modelling and business visualization.
SQL plays a crucial role in data manipulation, data analysis, and data visualization, and it is widely used in industries such as banking, healthcare, and retail.
Python proves to be very useful for more complex analytics, machine learning, predictions, automation, and data manipulation.
The provided Veralytiq guide suggests that as things get more challenging, advance from Excel to Power BI to SQL and then to Python and cloud/streaming technologies.
The instructor will provide information on how to make the exercises more effective.
An exercise can’t just be constructed out of the data.
A need for good Business Intelligence is to start with a business question. Rather than having students make a dashboard, provide them a specific problem/decision to help them make the dashboard.
The exercise, for instance, might ask, “Analyze sales data” rather than “Analyze information about sales.
- What are the areas that need improving?
- Who are the customers that bring in the most money?
- What products are having a drop?
- Why were Customer Acquisition Costs higher on the rise?
- Who are the customers for whom the risk of churn is high?
Then the learner should be able to decide what metrics, calculations and visualizations are required.
This way, the exercise will be closer to a business environment.
A list of typical pitfalls in conducting a business intelligence exercise.A list of common pitfalls of conducting a business intelligence exercise.
There Are A Number Of Pitfalls That Can Diminish The Benefit Of BI
It is often a mistake to pay more attention to visual design. But a dashboard can be impressive, yet not answer a critical business question.
This is another wrong mistake – poor definition of KPIs. Metrics should be well defined with time periods, methods of calculation and the right data sources.
Data quality may also not be considered by learners. Inconsistencies, such as missing values, duplicate records, inconsistent categories and incorrect dates, can lead to false conclusions.
The other problem is to not report results. BI professionals must be able to communicate the meaning of the data to individuals who may not be familiar with SQL, Python, or Power BI, or statistical modeling.
Last, but not least, there should not be an end to the analysis! The best practice relates an insight to a potential business action.
Understanding how to access the data to measure progress with BI Exercises
Progress of the learners can be measured by observing if learner is able to perform more complex tasks independently.
Beginner should be able to ‘link a data set’, make simple visualisations, set up simple KPIs and use filters.
An intermediate learner needs to have a basic understanding of SQL aggregation, joins, segmentation, forecasting and interactive dashboards. An advanced learner should be able to automate reporting, utilize analytical models in Python, understand the analysis results, and present the analysis results. The expert should be able to create real-time analytics solutions and articulate how a BI program can help inform overall business plans. The provided Veralytiq learning framework is similar in level of difficulty from beginner to expert.
Use Data For Decision-Making And Business Intelligence Exercises
The ultimate goal of business intelligence efforts is to become decision makers with information.
A sales dashboard can show sales data that is falling in a certain area. Unusual increases in acquisition costs may be identified in the KPI analysis. A small number of customers can account for a large portion of the sales, as indicated by customer segmentation. Forecasting can assist in learning about the various possible revenue outcomes. Churn analysis can help detect customers that need to be focused on.
In every instance there is more meaning in the analytical result when it enables a person to understand what is occurring and to determine further investigations that may be conducted or further action that may need to be taken.
The Project Manager Template reference also introduces a series of business intelligence exercises and studies that are business intelligence driven – highlighting the connection between business intelligence analysis and actual business decisions.
Frequently Asked Questions
What is meant by Business Intelligence exercises?
Business intelligence exercises are hands-on activities that involve collecting, analyzing, modeling, visualizing and interpreting business data in a scenario to help learners apply these practices for decision making.
What are some of the examples of business intelligence exercises you can think of?
They can be used in creating sales dashboards, analyzing key performance indicators, detecting anomalies, segmenting customers, forecasting revenue, analyzing supply chains, automating financial reporting, market basket analysis, customer churn prediction and BI strategy simulations, to name a few.
What are some tools that can be helpful for business intelligence activities?
These are some of the most helpful tools: Excel, Power BI, SQL and Python. The exercises can begin at a simple level (Excel, Power BI), and more complex exercises can include SQL, Python, machine learning, and streaming technologies.
Is BI exercise for the novices beneficial?
Yes. The exercises can be set up in a progressive manner, starting with simple dashboards and KPI analysis and ending with SQL, forecasting, predictive analytics and strategic BI.
What benefits can business intelligence exercises have to decision making?
They enable students to learn how to take raw data and translate it into measurable insights, find trends and aberrations, analyze performance and relate analytical results to business questions and actions.
Conclusion
Business intelligence exercises are a great way to build the technical, analytical and strategic capabilities needed to make decisions in the 21st Century. From basic sales dashboards and KPI analysis to customer segmentation, forecasting, supply chain analytics, financial automation, predictive modeling, real-time monitoring and complete BI strategy simulations, learners can advance to the next level. The best BI practice isn’t the end result of creating the most complex dashboard or model. It’s all about knowing what the business question is, having accurate data, applying the right analysis tools, presenting the findings accurately and clarifying the meaning of the data to the decision makers. By practicing these business intelligence exercises, the gap between learning the concepts of business intelligence and application to realistic business situations can be filled.
Henry Miller is a tech strategist, software architecture specialist, and author, passionate about rendering difficult systems easy to experience and understand in the digital age. Through a diverse portfolio of experience in software engineering, cloud infrastructure and emerging technical paradigms, Henry has made himself heard as a savvy orator in a world of hype. Your writers, engineers, technology leaders, and eager architects will find his writing a bridge between theory and practice—providing them actionable steps to build adaptive and resilient software and finding solutions for challenges along the way.
