Data Analysts' Relevance Blog

By Andrew Tumwesigye
Data and Business Intelligence Analyst, Dimension Metrics

Early Exposure

Before becoming an analyst, my only knowledge of analytics was a few basic aggregation functions in Excel. These proved useful when analyzing research data, typically from about 100 respondents. That was the extent of my data analytics exposure.

Then, in 2019, while working as an agency banking consultant for a local bank, I frequently engaged with the digital finance team which regularly complained of poorly analyzed customer deposit data. To address this, the bank hired a consultant who demonstrated how to automate data processing through extraction, transformation, and loading (ETL). He also showcased dynamic charts that updated automatically as new data trickled in. I was fascinated. Determined to learn more, I befriended the consultant, who generously shared basic and intermediate Excel learning videos with me. That’s when my passion for analytics took off. However, as I sought more information, I quickly learned that analytics tools were quite many and analysts were required to know how to use a big number of them.

The Learning Journey

Starting in 2020, my enthusiasm waned due to COVID-19 and later because of my demanding work schedule. Data analytics didn’t cross my mind again until four years later, when project funding decreased and work opportunities dwindled.

After spending five months at home without work, the idea of data analytics resurfaced. This time, with ample free time, I decided to take a leap and study analytics. My research on what course to take began with online platforms like TikTok, Google, and YouTube. One course kept appearing as a recommendation for beginners: Google Data Analytics Professional Certificate. I enrolled in October 2023 and due to the ample time I had, completed it by December. Whoever said, "The hardest part about anything is starting," was absolutely right. Once I got going, I built momentum and continued learning, expanding my knowledge into business intelligence as well as database management.

Building my proficiency

Without a job in analytics, I decided to build a portfolio. My initial goal was to complete 10 projects using SQL, Excel, and R. However, as my fascination and proficiency grew, I ended up completing 20 projects. Encouraged by my progress, I increased my target to 30, then 40, and eventually 60. The knowledge and confidence I gained turned me into an analytics enthusiast.

Do organizations need analyst?

Mark Twain once said, "Data is like garbage—you'd better know what you are going to do with it before you collect it." I’d like to add: You’d better not waste time and resources collecting it if you lack the expertise to extract meaningful insights from it.

This is precisely why organizations hire analysts—to determine how data can be used effectively and to extract insights from what may initially seem like a puzzle. The other question, then, is not whether organizations need analysts, but whether they need data. Is data relevant only to big companies, or does it benefit smaller businesses as well?

In my view, any organization—big or small—that generates data can benefit from analytics. Consider a small grocery store that records customer purchases, including product type, quantity, and purchase date. If the owner does not analyze this data, how will they determine peak shopping days or the most popular products? With this information, the business can make smarter decisions, such as stocking high-demand items, discontinuing slow-moving products, and rewarding frequent customers.

Small businesses may be able to track some trends through direct observation, but analysis provides more precise insights. While analytics tools like Excel or Power BI are helpful, even a simple manual review of recorded data can be insightful. Medium and large businesses, on the other hand, generate vast amounts of data and cannot rely on intuition alone. They need analysts to identify trends and make data-driven decisions.

Take the telecom sector in Uganda as an example. The rise of mobile data usage gradually led to a decline in traditional voice calls. Consumers shifted to using small data bundles for chatting and making calls via internet-based apps like WhatsApp. Without analytics, telecom companies might have overlooked this shift, continuing to focus on voice services for older demographics while missing out on the data-driven habits of younger users, who form a large customer segment.

Can’t Accountants do the Job?

Some believe that accountants and finance professionals can perform the same functions as analysts. While there may be some overlap, their roles are fundamentally different.

Accountants focus on developing financial summaries, such as profit and loss statements. However, they do not analyze data to identify trends, predict future outcomes, or work with non-financial data.

For example, a school may want to analyze academic performance trends across different genders. This requires statistical analysis, which an analyst would typically perform. Similarly, an HR department may need to evaluate employee performance based on key performance indicators (KPIs), or a marketing team may want to analyze customer conversion rates—tasks that accountants do not typically handle. The list of scenarios requiring analytical expertise is endless.

Conclusion

Analysts play a crucial role in organizations by identifying trends and patterns within complex datasets. A well-trained analyst can be the difference between an organization's growth, stagnation, or even failure. The data is available—the choice to use it wisely is yours.