Data Analyst or Business Intelligence Analyst 

By; Andrew Tumwesigye
Data and Business Intelligence Analyst, Dimension Metrics

To answer the question, do data analysts differ from business intelligence analysts, or are they just interchangeable terms? let me take you back to my journey as a digital financial services consultant, a path I started in 2016.

My work involved various responsibilities, but for this article, I’ll focus on research. On several projects, we would go out and collect substantial amounts of data, often recorded on interview guide forms. A colleague, more experienced with data analysis, would input the questions and responses into Excel and synthesize the data to extract insights, which he documented. Watching him work sparked my interest, and I wished to do the same someday.

Fast forward to 2022, I landed a property/rental tax project where one of my roles was to process incoming data. To do this, I manually entered the questions and responses into an Excel spreadsheet, assigning numeric codes between 0 and 5 for each response. Without knowing how to use pivot tables, I painstakingly counted the values to calculate totals e.g.,100 zeros, 50 ones, and so on. The process was inefficient, prone to error, and time-consuming, but I didn’t know a better way at the time.

By 2023, after a long career in digital financial services, I decided to pivot to something entirely different. Data analytics came to mind, driven by two factors. First, in my previous work, I noticed discrepancies between study findings and observed reality, which sometimes affected implementation. Second, I’d always been fascinated by data but had never dared to pursue it.

Over the course of a year, I earned five certifications in data analytics. I then began building a portfolio using online datasets from platforms like ChatGPT, Kaggle and Google’s BigQuery. Initially, I worked with small to medium datasets because they were easier to manage, but I soon realized the real challenge and value lay in analyzing large, complex datasets. These datasets often arrive in messy, ever-changing streams but hold critical insights for decision-making.

In the business world, it’s ‘eat or be eaten.’ Success depends on how quickly and effectively you can utilize available data. As a data analyst, I had the skills to analyze data, but I was unprepared for handling constantly flowing information. After some research, I encountered the terms business intelligence (BI) and Extract, Transform, Load (ETL).

ETL tools like Pentaho, Apache Kafka, Apache Airflow, Talend, and Stitch are designed to integrate data by extracting it from multiple sources, transforming it into a usable format, and loading it for analysis. These tools are powerful because they allow you to set transformation rules once, which are then applied to all future incoming data. This saves time and effort, eliminating the need to repeatedly reprocess data. To stay competitive, I enrolled in two online courses on Business Intelligence: one was Pentaho for ETL and Data Integration and the other Excel Power Tools for Data Analysis.

Initially, I struggled to differentiate between business intelligence and data analytics, even after countless online searches. I realized the best way to understand was by doing—and I’ve learned a lot through hands-on experience.

So, how do analysts differ from business intelligence analysts?

A data analyst primarily works with historical data to answer questions such as:

1.      What is happening? (Descriptive analytics)

2.      Why did it happen? (Diagnostic analytics)

3.      What should be done? (Prescriptive analytics)

4.      What will happen? (Predictive analytics)

On the other hand, business intelligence analysts focus on current or near-real-time data. They process information flowing in from online transactional processing (OLTP) systems, which is then extracted, transformed, and loaded into online analytical processing (OLAP) systems or data warehouses for analysis. Their primary goal is to determine what is happening right now (descriptive analytics). Also they present their findings in interactive dashboards.

In summary:

• Data analysts take a broader and less frequent approach, working across all types of analytics.

• Business intelligence analysts focus on constant monitoring, using ETL tools to analyze and visualize data streams in real time.

Both roles are vital, but their scope and focus set them apart.