AI Keeps Getting Smarter, So Why Do Data Analytics Skills Still Matter?

The development of artificial intelligence (AI) has made all kinds of study and work activities more practical. From summarizing information and processing data to assisting with analysis, a lot of work can now be finished faster.

For students, AI can help with the learning process and with working on projects. For professionals, the technology can simplify work and automate some repetitive processes.

But the more processes AI can help with, the more important our own ability to understand and evaluate the information it produces becomes. That ability is one reason Data Analytics skills remain relevant in the AI era.

“AI can automate work, but analytical thinking is still needed to make the right decisions. In the AI era, Data Analytics is no longer just a technical skill; it has become an essential need in business,” said Yovita, Program Manager at DQLab.

So how can Data Analytics help us in studying and at work?

AI Can Help Process Data, but the Results Still Need to Be Understood

AI can help process data and find patterns faster. The results of that analysis, though, still need to be checked so that the conclusions drawn fit the context of the problem.

For example, say a set of sales data shows an increase in transactions over a single month. At first glance, that looks positive. But did the increase happen because of a successful promotion, a change in price, or something else?

To understand the cause, we can compare the sales data from before and after the promotion, look at the change in the number of transactions, and evaluate other factors that may have affected the result.

The same applies when students work on a project or a case study. AI can help with the analysis, but understanding the data and explaining the reasoning behind a conclusion remains an important part of learning.

This is where Data Analytics comes in: helping us process data, find patterns, and produce insights we can use to answer a problem.

Data Analytics Skills Are Relevant Across Different Fields

Data Analytics isn’t only needed by people working as Data Analysts or Data Scientists. The ability to understand data can be applied in all kinds of jobs.

For example:

• Marketing: analyzing campaign performance and understanding audience behavior.
  Finance: monitoring transactions and evaluating spending.
  Human Resources (HR): analyzing recruitment data and employee trends.
  Business and Sales: tracking sales and identifying growth opportunities.

For professionals, this ability can help with evaluating work and supporting data-based decision-making.

For students, meanwhile, learning Data Analytics can be a first step toward understanding how data is used in the working world. The skill can also be practiced through case studies and simple projects that can later be developed into a portfolio.

So even if the field you’re in is different, an understanding of data can still be a useful thing to have.

You Don’t Have to Be Great at Coding to Start Learning Data Analytics

Interested in learning Data Analytics but have no coding experience or IT background? Don’t worry, you can start with the basics and build up gradually.

You can get to know data processing using Excel, learn SQL for working with data from databases, then explore Python or visualization tools as needed.

Beyond understanding the tools, it’s also important to practice reading patterns, asking the right questions, and explaining your analysis in a way that’s easy to follow.

These abilities can also help you use AI more purposefully, because you’re not simply relying on the results you’re given; you understand how to evaluate them too.

If you want to start building that understanding, you can take advantage of the DQ Scholarship from DQLab, which offers one month of free access to Data Analytics learning.

Come explore Data Analytics skills and the learning material that goes with them through this link: https://bit.ly/DQMEDIA

By DQLab

English translation by Levina Chrestella Theodora

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