What’s the difference between business intelligence and business analytics?

So . .. are you a BI guy?

I get that often and the answer is yes, yes I am. In actuality the answer is, “well . . . sort of. Not really. It’s more machine learning.” If I get into it there is a variety of questions that start with: “What’s the difference between …”

What’s the difference between business intelligence and machine learning? Even if you google these terms it’s hard to find a good definition. For sure you will find definitions, but not meaning and context for a never ending list of terms in a jargon rich field.

Quoting a Google employee, “Everything at the company is driven by machine learning.” What does that mean exactly? Is that big data? What about data mining? How does that fit in? Is this all just fancy jargon for old school econometrics and statistics?

In the next 3 min i am going to take on the job of getting you up to speed on what all these terms mean in relation to each other. It isn’t enough to have a list of definitions, you need to understand context. That is what I will give you here. Context.

What Business intelligence is . . . and isn’t.

When you think about Business Intelligence you might confuse it for Business Analytics. Business Intelligence runs the business. Business Analytics changes the business. Intelligence directs process. Analytics directs strategy. Intelligence focuses understanding for today. Analytics focuses planning for tomorrow.

BI is real time access to data. Reporting. BI identifies current problems, solutions, and enables informed decision making. Business Analytics explores data: statistical analysis, quantitative analysis, data mining, predictive modelling among other technologies and techniques to identify trends and make predictions. But the two areas are merging as evidenced by these headlines:

  • 5 Ways Machine Learning Can Make Your BI Better
  • Machine Learning: The Real Business Intelligence
  • Machine Learning: The Future of Business Intelligence.
  • Big Data & BI Trends 2017: Machine Learning, data lakes, and Hadoop Vs Spark

How Does  Machine Learning Relate to Business Analytics? What is it?

I’ve heard machine learning described as the brains behind AI. Machine learning is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed." I think of Machine Learning as a collection of pre-built algorithms for building models to predict future outcomes. Business analytics is about using those models on the execution side, putting insight into context and making things happen. In my last post I talked about the difference between data science and data savvy. Business analytics requires data savvy while machine learning is a component of data science.

Data Science?​

Data Science deals with structured and unstructured data. In principle, everything that relates to data cleaning, preparation and analysis lies within the scope of Data Science. Data science is interdisciplinary requiring training in statistics, computer science, and industry. Solo practitioners with specialization in all three areas are rare so it is common to have data science teams: a data savvy manager, an econometrician, & a developer trained in machine learning.

Traditional Research. If you know anything about analytics (or statistics) you are probably familiar with regression: “ordinary least squares”. If not I highly recommend reading the book, Freakonomics. Regression is a mathematical way of drawing a straight line that most closely fits a scatterplot of data.

Regression is the basis for econometrics which is squarely found in the arena of traditional research. As you can see on the venn diagram traditional research blends classical statistics with industry knowledge. The emphasis of traditional econometrics is to use statistical tools to determine causal relationships in data. An econometrician wants to be able to tell why something is happening in the data. They want to tell a story about why you see correlations. And they do that using different variations of the regression technique.

Software development. We all have apps that make our lives easier & more entertaining. A relative few are lucky enough to earn a living developing and/or supporting software, SaaS (Software as a Service). Traditional software development makes processes more efficient. Most of development exists around this. This field requires both Computer Science (coders) as well as industry knowledge. This space is marked by partnerships between clients and their SaaS providers. 

Developers will spend a lot of time and resources understanding their client's existing process to build solutions around industry best practices.

Machine Learning. Which brings us back to machine learning, which is probably not as familiar as software development or traditional research. When a developer uses machine learning, what does that look like? It starts with a dataset. As is the case with traditional research the first step is to prepare the data for analysis. Data prep, munging or data wrangling, as it is called is the most time intensive step. The second step is to separate the data into two parts. Two thirds to 70% of the data is used for training the model. 

One third to 30% is saved for testing the model. A machine learning modeler has a variety of tools at their disposal to build a model of relationships based on the training data. The modeler will then make predictions about the test data based on this model. The more accurate the predictions, the better the model.


At this point you should have a clear idea of what data science is: a blending of machine learning, traditional research, and software development to create predictive models. To contrast BI focuses on dashboards and reporting for the here and now. BI focuses on process. DS focuses on strategy. Data science requires a variety of advanced skill sets which makes data science teams quite common (and full stack data scientists quite rare). Business analytics on the other hand requires data savvy: a survey level understanding of data science topics with the purpose of putting these models to good use by executing on business goals.​

Among these topics a data savvy professional should be familiar with is an understanding of machine learning and the strengths and limitations of the more common algorithms used in machine learning. If you are interested in learning more, make sure to subscribe to my email list for data savvy professionals and get a copy of “Bull Doze Thru Bull.” In my next post we are going to explore these topics and get under the hood with machine learning.​


Data Savvy doesn't require you be a #machineLearning #dataScience expert. Turn your data dump into Actionable insight @ assume-wisely.com/bull