Data-driven organizations often use the terms "business intelligence" (BI) and "data analytics" interchangeably. They're not the same thing, but if someone asked you to explain the difference, what would you say?

Some people distinguish between the two by saying that business intelligence looks backward at historical data to describe things that have happened, while data analytics uses data science techniques to predict what will or should happen in the future. We think that's close, but there's more to it.

Business intelligence involves the use of data to help make business decisions, or as puts it, BI "refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of business intelligence is to support better business decision-making." However, one could say the same about data analytics.

To draw the line between business intelligence and data analytics, we think it's more useful to talk about what we want to accomplish. We can divide analytics into three categories: descriptive, predictive, and prescriptive.

Descriptive analytics takes data and turns it into something business managers can visualize, understand, and interpret. It provides intelligence into historical performance, and answers questions about what happened. Descriptive analytics reports are designed to be run and viewed on a regular basis. Examples include customer, operations, and sales reports.

Predictive analytics provides insights about likely future outcomes — forecasts, based on descriptive data but with added predictions using data science and often algorithms that make use of multiple data sets. The more data available, the better the predictions. Examples include sales forecasting, consumer credit scores, and retailers' suggestions for what you may want to read, view, or purchase next.

Prescriptive analytics offers advice about what actions to take. It examines possible outcomes that result from different possible actions and suggests which actions will have optimal outcomes. Creating prescriptive analytics requires advanced modeling techniques and knowledge of many analytic algorithms — all part of the job of data scientists.

Big data strategist Mark van Rijmenam writes, "If we see descriptive analytics as the foundation of business intelligence and we see predictive analytics as the basis of big data, than we can state that prescriptive analytics will be the future of big data."

So what's the difference between BI and data analytics?

Using these three categories, we can make a better distinction between BI and data analytics.

All descriptive analytics falls into the category of business intelligence. Some predictive analytics also constitute BI. After all, why look at analytics if you don't intend to use them to take action to enhance future outcomes? Prescriptive analytics, however, rises above BI into the realm of data analytics.

Where do we draw the line? Business intelligence relies on data that business managers work with. If they're trained in using visualization tools, such as Tableau, Microsoft Power BI, Looker, or any of a host of other options, they could create their own BI reports.

Data analytics requires a higher level of mathematical expertise. Data scientists take big data sets and apply algorithms to organize and model them to the point where the data can be used for forward-looking, predictive reports. It relies on algorithms, simulations, and quantitative analysis to determine relationships between data that aren't obvious on the surface. That doesn't happen with BI.

Rather than answering questions about what happened, data analytics tries to learn why things happened. Stitch co-founder and Talend SVP Jake Stein says, "Data analytics is about iteratively asking questions. The answer to any given question is often viewed only once and used to inform the next question on our way answering a fundamental business question or solving a problem."

Common ground for business intelligence and analytics

Business intelligence addresses ongoing operations, helping businesses and departments meet organizational goals. Data analytics can help companies that want to transform the way they do business. Both disciplines can benefit from a little data preparation.

Data analytics generally requires data modeling, in which raw data is collected, cleansed, categorized, converted, aggregated, validated, and otherwise transformed. Clean data is also helpful for BI.

Once the data is clean, it's stored in a structure and format that lends itself to reporting. Often that means the data is stored in a data warehouse — a columnar data store that, nowadays, often runs on scalable cloud infrastructure. The data in the data warehouse represents a single version of truth for all organizational reporting, for both BI and data analytics.

Both BI and data analytics call for an analytics stack founded on a data warehouse, with data piped in via an ETL tool. Stitch makes populating your data warehouse easy.

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Case closed?

Does this discussion settle the question? Not likely. No matter how we define it, people are still going to use terms however they like. So what if someone says, "Data analytics is how you get to business intelligence" or "Business intelligence encompasses data analytics"? What if they want to talk about "business analytics"? So be it. The point of both processes is to analyze data and create reports to improve decision-making — on that point, everyone agrees.

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