Business intelligence and data analytics are essential to every business—from large corporations to small nonprofits. While the two terms might sound the same, they’re distinct assets that play unique roles in planning and decision-making for companies.
If there’s anyone who should know the difference between business intelligence vs. data analytics, it’s prospective business professionals (like business analysts, data analysts, and team leaders).
We’re breaking it down in this guide from the Alliant International University business experts. Below, we’ll define business intelligence and data analytics, compare their features and purposes, and explore how future professionals can hone their skills in both tactics.
For a definitive guide for business students and entrepreneurs alike, read on.
What is Business Intelligence (BI)?
Business intelligence is the process of collecting, synthesizing, and analyzing a company’s big data to draw conclusions about business performance.1
How a business intelligence analyst gathers business intelligence differs at every organization, but all companies generally use a similar procedure:
- Data collection – To analyze data, you have to have data. Businesses call upon their business analytics and big data experts to create raw data collection technologies, implement collection initiatives, and monitor collection. The professionals who do this could be IT professionals, data analysts, or both.
- Data compilation – Before data can be analyzed, it needs to be synthesized and organized by distinct parameters. This step might include creating data visualization graphics and visual aids to help stakeholders understand the data.
- Making connections – Perhaps the most important business intelligence function is interpretation: a BI analyst reads what the data says to come to a conclusion about a specific business practice.
Business intelligence provides insights into historical and current data. How might that type of business analytics be useful to an organization? Let’s examine a hypothetical:
- A company wants to track the sales impact of an upcoming product price change—will a price increase result in fewer sales?
- They work with their IT department and the data analyst to create a data collection method. Using a wide BI tool selection, they start tracking the number of sales for that item for four weeks before the price change and then for four weeks after the increase.
- After the eight-week study, data experts read the data and create a line chart to denote sales changes over time—before and after the price change.
In other words, BI uses this data to support business operations and improve strategic decision-making. It typically employs business intelligence tools to get actionable insights by organizing and visualizing structured data, making it easier for business users to make a data-driven decision in real time. Because of this a business can determine whether or not customers are willing to pay more for the same product through this process.
Types Of Business Intelligence
- Traditional business intelligence focuses on historical data and reporting, often using pre-defined queries and static reports to provide insights into past performance. It’s typically used by specialized teams within a company, like IT or data analysts, to generate reports that help businesses understand their operations. While effective, it can be slower and less flexible for real-time decision-making.
- Modern business intelligence, on the other hand, is more dynamic and user-friendly, allowing everyday business users to access real-time data and perform analysis with minimal technical knowledge. It often includes interactive dashboards and business intelligence tools powered by artificial intelligence and automation, providing deeper, more actionable insights.
What is Data Analytics?
Unlike business intelligence, data analytics takes the process above a step further—even into the future. What do we mean by that?
Instead of just analyzing current and historical data to observe past trends, data analytics often employs predictive measures to create.2 Data analysis is important since it helps the company grasp how data-driven decisions can significantly impact business strategy development.
- Forecasts and if-then scenarios for a variety of actions
- Action plans based on stakeholders’ decisions
Let’s return to the hypothetical above: the business intelligence team discovered that fewer customers purchased the product during the four-week period following the price hike. With that in mind, the data analytics team might create models to predict how sales might change after:
- A directed marketing campaign for that product
- A sale or discount code for that item
- A return to its original price
Using data from the study and perhaps past data analyses (related to discount code redemption among their customer base, for instance), data analysts can predict how customers may react to each of the proposed action plans above. Their various methods include data mining, statistical analysis, and data engineering.
This is a slightly more complex function than business intelligence; it requires more variable considerations, more in-depth models, and (most importantly) more data.
Types of Data Analytics
- Descriptive Analytics:
Think of descriptive analytics as a way to understand what has already happened in your business. It’s like looking at a report card for your company’s past performance. It summarizes data and finds patterns. It’s helpful for making strategic decisions because it shows what worked and what didn’t in your business operations. - Diagnostic Analytics:
It doesn’t just show what happened, but explains why it happened. If sales dropped, diagnostic analytics will help figure out why. Using tools from data science and artificial intelligence, a business analyst digs deeper into the data to find reasons behind trends. - Predictive Analytics:
Predictive analytics looks at past data and uses models to predict what might happen in the future. It helps forecast things like sales trends, customer behavior, or market changes to make better business decisions. - Prescriptive Analytics:
It combines everything—past data, predictions, and optimization techniques—to recommend the best moves for your business operations. Whether it’s deciding on a marketing strategy or streamlining a process, prescriptive analytics helps you make the right strategic decisions for success.
Comparing Business Intelligence and Data Analytics
Both fields involve analyzing business data to make better decisions, but they differ in approach, tools, and outcomes. With an idea of how these procedures help companies make data-driven choices, let’s compare business intelligence and data analytics in more detail.
Focus on Historical vs. Future-Oriented Analysis
While you might have noted this difference above, let’s return to the time element of both of these analytical procedures:
- Business intelligence primarily analyzes current or past data to get a feel for current or past business performance. These data may help them establish trends that inform the predictive process of data analytics.
- Data analytics, while interested in current and historical data, often seeks to predict how certain actions (or inactions) would change business performance.
In short, data analytics is positioned to answer questions about the future—perhaps not with absolute certainty, but with the weight of data trends behind their analyses.
Data Sources and Processing
Let’s return to our hypothetical above to explore the types of data each process relies upon.
During the business intelligence task, analysts depended on sales data over time—two variables. Tracking this was relatively simple: all the team had to do was record sales of that item within the time window.
But to predict how customers might respond to a price change after a marketing campaign, a discount, or a price decrease, data analysts must rely on wider sources to consider all relevant variables. These sources could include:
- Data from the company’s marketing department about past campaigns
- Information from the finance department about how discounts impact the bottom line
- A log of price changes for similar items from their competitors
Note that the last item above is external—it’s gathered by observing another company’s behavior. Data analysts often rely on data from various channels, including sources outside of the company. But even external data matters during the predictive process and data analysts try to forecast trends using as much data as possible.
Goal-Oriented vs Exploratory Analysis
In our hypothetical, remember that the goal of the business intelligence study was to answer a single, simple question: “Did more or fewer customers purchase this item after we increased the price?”
And that study had a simple answer: fewer customers purchased after the price increase.
Notice that the question that guided the business intelligence process was quantifiable, answerable, and concrete. But data analytics questions are often less goal-oriented and more open-ended:
- What if we start marketing this item to a new segment?
- What if we run a discount for this item?
- What if we decrease the price?
- What trends aren’t we seeing in the data that we have?
While business intelligence is goal-oriented, data analytics is more exploratory and seeks to find probable patterns. There’s rarely a concrete answer to any question that starts with “What if,” data analytics seeks to flesh out the possibilities using the available data. Questions made in a business intelligence context typically have an answer, whereas questions made in a data analysis context usually lead to more questions.
Business Reporting vs. Problem Solving
Business intelligence processes typically result in reports generated directly from internal company data. These are critical to business decision-making: reports show how well a specific variable performs.
Business intelligence studies don’t seek to solve a problem; they just want to create an objective, observational report.
Data analytics, on the other hand, is more directly related to problem-solving. When faced with fewer sales after a price increase (per our hypothetical above), the data analytics team faces questions about how to fix it.
That said, both projects are key for data-informed decision-making. A business intelligence data analyst can help leaders take action (or remain consistent) on:
- Internal company policies
- Standard operating procedures and processes
- Tool and tech asset performance
- Hiring and human resources matters
While all of these are broad, data analytics typically informs more micro-level decisions like:
- Product or service price changes
- Tweaks to a marketing campaign
- Adding or removing a customer service channel
- Changing company branding
While business intelligence is primarily concerned with decisions based on current and past performance, data analytics considers how future changes might impact the business.
Discuss how BI is primarily focused on generating reports for informed decision-making. Explore how Data Analytics is more problem-solving-oriented, addressing specific business challenges through data-driven insights.
How Alliant Helps MBA Students Develop These Skills
So is business intelligence the same as data analytics? Business intelligence solutions are invaluable for tracking performance and making informed business decisions based on past data. Data analytics goes beyond reporting past data and focuses on generating deeper insights to predict future outcomes. With these nuances in mind, how can tomorrow’s business leaders learn more about data analytics, business intelligence, and other critical functions of a successful brand? The first step for most leaders is quality training.
Luckily, there are higher education programs available that can support business-oriented careers:
- Master’s in data analytics (MS)
- Master of business administration (MBA)
- Master’s in healthcare analytics (MS)
- Master’s in information systems technology (MS)
- Doctorate in business administration (DBA)
- Doctorate Organizational in leadership (PhD)
When choosing between graduate programs in business fields, prospective students should consider the following:
- Future goals – What do you want to change if you already have a bachelor’s degree and a growing career? Which degree track will help you achieve your goal?
- Experience level – Many employers seek a combination of education and hands-on experience for leadership and advisory positions like business intelligence and data analytics. So, if you’re a little behind the curve on field experience, consider graduate programs that offer hands-on learning opportunities.
Luckily, a higher education option for future business leaders looking to take the reins on their careers is Alliant International University.
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Pairing expert faculty with field experience helps us produce candidates who are strongly prepared for today’s business environment. And if you’re ready to grow, we want to help you start your higher education journey on the right foot.
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Sources:
- Craig Stedman. “Business Intelligence.” TechTarget. https://www.techtarget.com/searchbusinessanalytics/definition/business-…. Accessed February 19, 2024.
- Jake Frankenfield. “Data Analytics: What It Is, How It’s Used, and 4 Basic Techniques.” Investopedia. August 9, 2023. https://www.investopedia.com/terms/d/data-analytics.asp. Accessed February 19, 2024.
