Data analytics explained in plain words
Data analytics is the practice of studying the information a business already collects, such as orders, payments, website visits, support tickets or machine readings, so that people can understand what is going on and decide what to do about it. A data analyst is the person who does that study. They gather and clean the data, look for patterns with tools such as Excel, SQL and Power BI, and explain the result in a report or dashboard that a manager can act on.
So what does a data analyst do, in one sentence? An analyst turns a business question into a number, a chart or a recommendation. A sales head asks why orders dropped in one region. The analyst finds the right tables, pulls the figures, checks them for mistakes, compares the region with the others and reports back with something like "the drop comes from two pin codes where deliveries slowed down". That sentence is the product. The tools only help you reach it.
Two honest limits are worth knowing at the start. Analytics can only explain what the data captured, so it cannot say how customers feel if nobody recorded it. And the title is used loosely. One company's Data Analyst mostly builds weekly reports, another's mostly writes SQL, and a third's builds dashboards all day, so read the duties in a job description and not only the title.
The four kinds of analytics, followed through one grocery delivery app
Analytics is usually split into four kinds, and each answers a different question. Picture a grocery delivery app in Hyderabad whose evening orders have slipped, and watch each kind at work.
Descriptive analytics says what happened
It summarises the past. Orders per hour, average basket value and delivery time for each area come from a query or a pivot table. Most day to day analyst work sits here, and it is the base for the other three kinds.
Diagnostic analytics asks why it happened
It hunts for causes by splitting the numbers by area, time, product and customer type. Perhaps the slip comes from two neighbourhoods after 7 pm, when a rider shortage stretched delivery times. A pattern is a clue and not proof, so a careful analyst checks it with the operations team.
Predictive analytics estimates what is likely next
It uses past patterns to forecast, for example how many orders to expect next Friday. It leans on statistics and sometimes machine learning, and it appears more in experienced analyst and data science work than in first jobs, though every analyst should understand it.
Prescriptive analytics suggests what to do about it
It recommends an action, such as adding riders in the two slow areas during the evening peak, and estimates the effect. In most companies this is an analyst's reasoned suggestion backed by numbers, and the manager makes the final call.
How one business question becomes an answer, step by step
The analytics lifecycle sounds abstract until you follow a single request through it. These are the six stages, in the order they normally happen.
Turn the request into a question that data can answer
"Tell me about evening orders" becomes "which areas and time slots lost orders compared with last month, and by how many?" Agreeing the question, the date range and the meaning of an order first saves days of rework.
Find and collect the data
Order records may sit in a database, rider logs in a spreadsheet and campaign details in a marketing export. The analyst gathers them with SQL, file imports and connectors, and notes where each figure came from.
Clean and check the data before trusting it
Duplicate orders, blank delivery times, dates typed in two formats and test orders all need fixing. Cleaning is unglamorous, but confident answers built on dirty data are the most dangerous kind.
Analyse and explore to find the pattern
Group, compare and chart the numbers. A pivot table, a SQL GROUP BY query or a short Python notebook can each do it, and the choice depends on how large and messy the data is.
Present it so the reader can act
One clear chart, one plain sentence and one recommendation beat twenty tables. A dashboard suits numbers people will check every week, while a short written summary suits a one-off question.
Follow up and keep the numbers alive
After the rider schedule changes, the analyst tracks the same KPI to see whether it worked. A recurring report turns a one-time answer into an ongoing view of the business.
Who an analyst works with and what lands on the manager's desk
An analyst rarely works alone. Business managers and team leads bring the questions. Data engineers and database administrators build and look after the tables an analyst reads from. Developers and product managers explain how the app records events. Finance and operations teams check whether the numbers match what they see on the ground, and data scientists take over when a question moves from explaining to predicting. A good analyst spends more time talking than beginners expect, because a number nobody understands changes nothing.
What the manager receives is usually one of six things: a dashboard that refreshes on its own, a recurring weekly or monthly report, a one-off analysis of a single question, a set of clearly defined KPIs such as repeat purchase rate or average delivery time, a short note on data quality problems, or a plain recommendation with the evidence behind it. A machine learning model is not on that list, which is one difference between analytics and data science.
Where different people start on the road to data analytics
Analytics rewards curiosity and patience more than any one degree. Here is how the starting line looks for four common backgrounds.
A commerce graduate who already reads accounts and invoices
Your business sense is the part that is hard to teach. Add Excel, SQL and Power BI, and aim first at Reporting Analyst or Business Analyst roles.
An engineering student choosing between coding and analytics
You can move quickly through SQL and Python, but give equal time to business questions and dashboards. Analytics is judged by the clarity of your answer, not by the cleverness of your code.
A support or operations executive who lives in daily MIS sheets
You already know what a business number feels like. SQL and a BI tool are your natural next layer, and your own workplace data can become the first project.
A marketing or sales professional who wants numbers behind the plans
Campaign and pipeline analysis is a real branch of analytics. Learn to pull and summarise your own data, and marketing or sales analyst roles come into view.
What you need to know to work in data analytics
You do not need everything on day one, but this is the ground the work covers.
- The four kinds of analytics and which one fits a given business question
- Excel for pivot tables, lookups such as XLOOKUP, data cleaning and quick charts
- SQL for pulling and joining data in relational databases such as MySQL and PostgreSQL
- Data cleaning habits, meaning how to spot duplicates, gaps and odd values before analysing
- Basic statistics such as averages, medians, spread and correlation, and when an average misleads
- A BI tool such as Power BI or Tableau for dashboards people can filter and drill into
- KPIs and business metrics, and how to choose ones that match a goal
- Python with Pandas and Matplotlib for larger or messier data, useful as a later addition
- Plain writing and speaking, because findings must reach people who do not read tables
Data analytics job titles and the pay range Skill IT publishes
The same core work sits under many titles: Data Analyst, Reporting Analyst, BI Analyst, Business Analyst, Insights Analyst, Product Analyst and Marketing Analyst among them. Analysts are hired by banks, retailers, logistics firms, hospitals, IT services companies and startups, because nearly every business has numbers it needs to understand.
On pay, Skill IT publishes only two broad indicative figures. In India, the typical entry-to-mid range for Data Analyst, Reporting Analyst and BI Analyst roles is around ₹3.5L to ₹8L a year, rising with certifications and project experience. For equivalent data analyst and BI analyst roles in mature international markets, the range is around $50K to $90K a year. Both vary by company, city, specialisation and experience, and neither is a promise. To check current numbers, read recent job listings, talk to people in the role and compare the full cost to company on any offer.
How Skill IT Education prepares you to work as a data analyst
The Advanced Data Analytics Certification Program at our Madhapur centre follows the same path as the lifecycle above. It is support and preparation, not a promise of any outcome.
Nine modules that follow the analytics lifecycle
The 130 hours of core curriculum begin with the fundamentals of data analytics, including the four kinds of analytics, then move through Excel, SQL, exploratory analysis, visualisation, BI tools, reporting, KPI tracking and a domain capstone.
Labs that end in something you can show
Every module closes with lab work or a project, such as the Analytics Problem Framing Brief, the Excel Dashboard Build and the BI Dashboard Build. The programme includes a minimum of five portfolio projects.
Two months on reporting, dashboards and business analytics
After about three months of structured learning, the internship gives real-time exposure to reporting, dashboarding and business analytics work, where the ideas above meet real deadlines and real stakeholders.
A profile that shows your analytics thinking
We help with your resume, GitHub and LinkedIn so a recruiter can open a project and see the question, the method and the result. The programme also prepares you for certifications such as Google Data Analytics and Microsoft Power BI Data Analyst Associate.
Practice interviews and hiring partner introductions
Mock interviews rehearse how you explain your work, and placement support runs through our hiring-partner network. We assist with the search, and every hiring decision stays with the employer.
Quick answers about data analytics and the analyst role
Short answers to the questions people type most.
What is data analytics in simple words?
Data analytics is examining the information a business collects, such as sales, visits or complaints, to find patterns and decide what to do next. It answers what happened, why it happened and what is likely next, using tools such as Excel, SQL and Power BI.
What are the four types of data analytics?
Descriptive analytics says what happened, diagnostic analytics explains why, predictive analytics estimates what is likely next, and prescriptive analytics suggests what to do about it. Entry level analyst work sits mostly in the descriptive and diagnostic types, with forecasting added as experience grows.
Is a data analyst the same as a business analyst?
Not always. A data analyst works mainly from data, using SQL, Excel and dashboards to answer questions. A business analyst leans towards requirements, processes and stakeholder needs. The titles overlap a lot in practice, so compare the duties listed in each job description.
Do data analysts have to write code?
Usually a little. SQL is the code analysts write most, to pull data from databases. Python is a useful addition for large or messy data, and Excel and Power BI need formulas. Analyst work rarely involves building software, so heavy programming is not the core skill.
Where do data analysts get the data they analyse?
From company databases queried with SQL, exports from business software such as billing or CRM systems, spreadsheets kept by teams, and website or app tracking. Data engineers often maintain these sources, and the analyst checks the quality before using them.
Where to read next about data analytics and the analyst role
Start with the programme page if you want to see the syllabus behind this role. The guides below go deeper on the day to day work, the route in and how analysts differ from data scientists.
Ask one small question of a real spreadsheet tonight
Reading about analytics only takes you so far. Open any table of numbers you care about, such as your monthly spending or a cricket score sheet, and ask it one question you can answer with a pivot table. If you would like help choosing a sensible first month, the admissions team is happy to talk it through.