Data Scientists vs. BI Analysts: What’s the Difference?

Business intelligence and data science often go hand in hand.

Both fields focus on deriving business insights from data, yet data scientists are regularly touted as the unicorns of big data analysis. Why is this?

The fact is, while many of the responsibilities, techniques and goals of analysts and data scientists closely match, major differences exist between the required skillsets — and expected outputs — for each job. Let’s explore a few of the most important ones.

BI Analysts Focus More on the ‘What’ than the ‘Why’ 

A BI analyst’s main task is to find patterns and trends in your business’s historical data.

That makes BI largely an exploration of past trends, while data science finds the predictors and significance behind those trends. Both views are ultimately valuable and complementary. The data aggregation and transformation BI analysts conduct puts data into a format that data scientists can easily repurpose when building models.

The typical analyst’s toolkit consists of software like BI dashboards — which make visualizing business performance easy (although dashboards lack the flexibility and capacity of code) — and programming languages like SQL to manipulate data and query databases. Using these tools, BI analysts can evaluate the impact of certain events on a business’s bottom line or compare a company’s performance to that of other companies in the same marketplace.

However, they are rarely required to forecast future business metrics with a high degree of accuracy, as that requires a more technical skillset.

Data Scientists Apply an Algorithmic Approach

Data scientists, on the other hand, have a toolkit of algorithms that they use to understand and predict a business’s performance.

Understanding that performance requires a more technical skillset based in statistics, machine learning and programming. In addition to languages like SQL, a data scientist is expected to know how to code in languages designed for mathematical analysis like R, Python or Scala.

Using those programming languages, a data scientist can create a framework that leverages historical data — as well as the data currently being created — to predict how much money a business’s customers will spend over a certain period, when those customers might defect, or other important metrics.

This framework, called a model, uses machine learning algorithms to identify relationships between features like average order value and customer age in order to predict outcomes like how much a certain customer is likely to spend. And the model can constantly “learn” from the data being inputted, so the outputs stay accurate and relevant — something a data scientist, not a BI analyst, would be tasked with monitoring.

Data Science Outputs Often Require Engineering Support

Businesses now drive decisions based on predictive algorithms, which requires seamless and scalable integration. The knowledge needed to perform such tasks is more of an engineering skillset than a data science one. In fact, multiple types of engineers are required to deploy a data scientist’s model across multiple applications.

For example, if a data scientist builds a model to make product recommendations to online shoppers, the output of that model would need to be integrated into a website. For that to happen, the right data needs to be in the right place to feed the model, and the model needs to be deployed behind an API — both tasks which usually fall to engineering.

As more organizations become more algorithmically driven, data scientists face an uphill battle in getting their results into production. On the other hand, BI analysts will usually make their analyses available to decision makers through a dashboard or report — a much less time-intensive process.

BI + Data Science = Better Together 

Ultimately, data science is best performed in conjunction with BI.

While both groups are working to understand the market and business trends hidden in large volumes of data, BI is the logical first step for companies starting to dabble in big data.

But if you already have a BI team in place and are collecting significant amounts of data, data science can help you predict the future of your business, refine your products and scale operations.