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.
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, 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.