Every day, millions of purchases, bank transactions, website clicks and app sign-ups take place. Each of these actions leaves a trace — in other words, it creates data. But raw data on its own says nothing. The field that turns it into clear insights and decisions is data analytics. In this article we explain in plain language what data analytics is, what a data analyst does and who this field is a good fit for.
What is data analytics?
Data analytics is the process of collecting, cleaning, analyzing and visualizing data to answer questions and support decision-making. Simply put, data analytics uses numbers to answer: "What is happening, why is it happening, and what should we do next?"
For example, an online store wants to know why sales dropped last month. A data analyst examines sales, advertising and customer data and finds that the drop happened mainly among mobile users and coincided with an update to the checkout page on the mobile site. That is already a basis for a concrete decision.
Types of data analytics
Analytics is usually divided into four levels based on the question it answers:
- • Descriptive analytics — What happened? For example, last quarter's sales report.
- • Diagnostic analytics — Why did it happen? For example, the reasons behind customer churn.
- • Predictive analytics — What might happen? For example, a demand forecast for next month.
- • Prescriptive analytics — What should we do? For example, which product is most profitable to discount.
An entry-level data analyst works mostly at the first two levels and moves on to forecasting and recommendations with experience.
What does a data analyst do?
A data analyst's day-to-day work typically follows these steps:
- • Clarifying the question — talking to the business side to pin down what really needs to be known
- • Collecting data — from databases using SQL, from Excel files or other sources
- • Cleaning data — removing duplicates, errors and gaps (often the most time-consuming part of the job)
- • Analyzing — finding trends, relationships and anomalies
- • Visualizing — presenting results as charts and dashboards in Power BI or Tableau
- • Presenting findings — explaining the numbers in language management understands and making recommendations
An analyst's value lies not just in calculating numbers but in turning them into decisions.
Where do data analysts work?
Analysts are needed in almost every field that works with data:
- • Banking and finance — risk analysis, customer segmentation, fraud detection
- • Retail and e-commerce — sales analysis, inventory planning, customer behavior
- • Telecommunications — subscriber churn, service quality
- • Marketing — measuring campaign effectiveness
- • Healthcare, logistics, public sector — process optimization and reporting
Who is data analytics for?
People move into this field from many different backgrounds. It is an especially natural choice for:
- • Economists, finance professionals and accountants — you already work with numbers, and analytics tools take that skill to the next level
- • Marketers — anyone who wants to measure and optimize campaign results
- • Engineers and mathematicians — logical thinking and a math background are big advantages
- • Office professionals who use Excel a lot — Excel skills are one of the best starting points for analytics
- • People who want to move into IT without becoming programmers — analytics is technical, but more accessible than software development
Traits that help you succeed:
- • Curiosity and a habit of asking "why?"
- • Attention to detail
- • Logical, structured thinking
- • The ability to explain findings in simple terms
- • An interest in how businesses work
Who might data analytics not suit?
If working with numbers and spreadsheets tires you, checking the same data again and again wears down your patience, or you are drawn only to creative, visual work, analytics may feel difficult. A large part of an analyst's work consists of "invisible" tasks that require patience, such as cleaning data.
Data analyst vs data scientist vs BI analyst
- • Data analyst — analyzes existing data and answers business questions. Main tools: Excel, SQL, Power BI, Python.
- • BI analyst — focuses more on building reporting systems and dashboards.
- • Data scientist — builds predictions with statistical models and machine learning; requires deeper math and programming skills.
Many data scientists started their careers as data analysts.
Frequently asked questions
Do I need to know programming for data analytics?
Not at the start. You can do a great deal with Excel and SQL. At the next stage, Python makes your work much easier and broadens your career options.
Do I need to be strong in math?
Knowing basic statistical concepts such as mean, median, percentages and correlation is enough. Advanced math is needed mostly in data science.
Which tools should I start with?
The most common path is Excel, SQL and Power BI, followed by Python. We cover this in detail in our article Which Tools Do You Need to Learn to Become a Data Analyst?.
Conclusion
Data analytics turns data into decisions, and today it is in demand in almost every industry. If you enjoy finding the story behind the numbers, think logically and like explaining your conclusions, data analytics could be a promising career path for you.
Want to start data analytics with hands-on training? In the IDTech Academy Data Analytics course, you'll learn to analyze real data and present your results. Learn more about the course →