Quick Answer
The fundamentals of data analytics cover how raw data is collected, processed, and analysed to produce useful insights for decision-making. The four core types are descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive analytics (what action should be taken). Key tools include SQL, Excel, Python, Tableau, and Power BI.
Introduction
Data analytics is the process of examining raw data to draw conclusions, identify patterns, and support better decision-making. Whether you are working in business, healthcare, finance, or technology, data analytics is now a core skill that cuts across almost every industry.
This guide covers the fundamentals of data analytics — what it is, how it works, the four main types, the key concepts you need to understand, and the tools used in practice. If you are new to the topic, this is the right place to start.
What Is Data Analytics? A Simple Definition
Data analytics is the practice of collecting, organising, and analysing data to identify patterns, trends, and relationships that inform decisions. It combines statistical methods, programming, and business knowledge to transform large volumes of raw data into actionable insights.
Data analytics is closely related to data analysis, but the two terms are used slightly differently in practice. Data analysis typically refers to examining a specific dataset to answer a specific question. Data analytics is the broader discipline — it includes the tools, processes, and systems used to analyse data at scale and on an ongoing basis.
| Data Analysis | Data Analytics | |
|---|---|---|
| Scope | Examining a specific dataset to answer a specific question | The broader discipline — tools, processes, and systems for ongoing data examination |
| Timeframe | Usually a one-time or project-based activity | Ongoing and continuous |
| Output | A report, finding, or conclusion | A system of insights that supports regular decision-making |
| Example | Analysing last month’s sales figures to find the best-selling product | Building a dashboard that tracks sales performance in real time and alerts the team to anomalies |
The Four Types of Data Analytics
There are four main types of data analytics, each answering a different question about data. Understanding these four types is the foundation of data analytics work.
Here are the four types of analytics:
Descriptive Analytics
Descriptive analytics is used to find the answer to the question – “What has happened?”. This type of analytics uses historical data to understand what has happened in the past.
For example, An organization targets to achieve sales growth of 10% every quarter.
Descriptive analytics uses sales transactions (recording every order), summarizes these, and makes calculations to check if the “10% quarterly growth” is achieved or not. In descriptive analytics, we use summaries, charts, and pattern analysis to interpret the results.
Descriptive analytics can be used to answer questions like these:
- What were our sales figures last quarter?
- How has our website traffic changed over the past year?
- What is our average customer satisfaction level?
Diagnostic Analytics
Diagnostic analytics goes beyond descriptive analytics to explore the reasons why something happened. It does this by examining diverse datasets and looking for patterns and correlations.
Examining various datasets to acquire a complete picture of what transpired is common in diagnostic analytics. A retailer, for example, can examine sales data, customer feedback, and marketing campaign data to determine why sales fell in a specific month.
Diagnostic analytics involves formulating hypotheses about the root causes of events. These hypotheses can then be tested using further analysis or experimentation.
Predictive Analytics
Using past data, predictive analytics makes future projections. Finding patterns and trends in data is done by applying machine learning techniques and statistical algorithms. Many different factors, like consumer behaviour, sales information, and market trends, can be predicted by using predictive analytics techniques.
Based on historical data, it can predict future events such as customer turnover, demand for new products, or the chance of a natural disaster.
Prescriptive Analytics
Using the information from predictive analytics, prescriptive analytics makes recommendations that can be put into practice.
Prescriptive analytics not only forecasts future events but also makes recommendations for counteractions. This has the potential to assist businesses in making astute decisions regarding the allocation of resources, refining marketing strategies, and enhancing customer support.
Prescriptive analytics has diverse applications, providing recommendations in various fields, such as:
- Marketing: Which products should we promote? Which channels should we use?
- Sales: How should we allocate our sales force? How should we price our products?
- Operations: How should we schedule our production? How should we manage our inventory?
| Type | Question It Answers | Example | Difficulty |
|---|---|---|---|
| Descriptive Analytics | What happened? | A sales report showing total revenue by region for last quarter | Entry level — most common starting point |
| Diagnostic Analytics | Why did it happen? | Drilling into the sales data to find that one region underperformed because of a supply issue | Intermediate — requires deeper investigation |
| Predictive Analytics | What will happen? | Using historical sales patterns to forecast next quarter’s revenue | Advanced — requires statistical modelling |
| Prescriptive Analytics | What should be done? | Recommending the optimal pricing strategy based on demand forecasts and competitor data | Advanced — requires machine learning or optimisation algorithms |
The Data Analytics Process: Step by Step
Every data analytics project follows a similar process regardless of the tools or industry involved. Understanding this process helps you work more systematically and produce more reliable results.
Step 1: Define the problem
The first step is to define the problem, which needs to be solved. Without a clear objective, the data analytics initiative is not going to be useful.
Defining the problem also includes identifying the data needed to address the problem.
For example, an e-commerce wants to identify the problems with the sales numbers. The sales volume is stagnant or very erratic.
Another example,
An organization would like to predict the demand for products so that it can ensure enough supply to meet the demands.
Step 2: Data Collection
The next step is to organize and collect the data for the project. The data has already been identified in the first step. The data can be collected from existing data sources (like orders, customers, etc.) or can be collected through surveys, interviews, market research, or observations.
Step 3: Data Cleaning
The next step is to process and clean up all of the data collected. Data cleaning includes correcting the errors in data, and removing duplicates and inconsistencies.
For example, See the table below. The date format in the second row is incorrect. This needs to be identified and corrected as shown in the “Corrected data” column.
| Data | Corrected Data |
|---|---|
| 19-Jan-2024 | 19-Jan-2024 |
| 24/Jan-2024 | 24-Jan-2024 |
The processed or cleaned data is then migrated to a location
Step 4: Data analysis
Once the data is cleaned, it’s time for analysis. Analysis may involve a variety of techniques. Using statistical or mathematical techniques to discover patterns, relationships, or trends are data analysis techniques. Software applications/platforms like R, Python, and Excel are used for data analysis.
Step 5: Interpreting and visualizing the data
This is the step that helps us understand – What does the data tell us? Once we (Business and Data Analysts) interpret the results, it’s important to create visual representations that are easy to understand.
Please note that stating the interpretations as text is not as powerful as showing it visually.
“A picture is worth a thousand words.”
Step 6: Data storytelling
The next step is to communicate the findings and insights to the stakeholders. Stakeholders could be non-technical or may not be able to understand the technical jargon or terms. Presenting the data in such a form that all the stakeholders can understand the make decisions.
Step 7: Measuring effectiveness and improvement
The final step is to validate the effectiveness of the solution. The measures are put in place so that actual data can be measured against the expectations. In case of a “not meeting the expectations” scenario, a root cause analysis can be conducted to find the problems, that need to be solved. This cycle will be continued till the expectations are met.
We have a detailed blog article on Data Analytics lifecycle phases.
| Step | Stage | What Happens | Common Tools |
|---|---|---|---|
| 1 | Define the Question | Identify the business problem or decision that the analysis needs to support. A clear question prevents wasted effort. | Stakeholder meetings, project brief |
| 2 | Collect Data | Gather relevant data from internal databases, APIs, spreadsheets, surveys, or third-party sources. | SQL, APIs, Excel, Google Analytics |
| 3 | Clean and Prepare Data | Remove duplicates, handle missing values, correct errors, and standardise formats. This step typically takes 60-80% of total project time. | Python (pandas), Excel, OpenRefine |
| 4 | Analyse Data | Apply statistical methods, build models, and identify patterns and trends in the prepared dataset. | Python, R, Excel, SQL |
| 5 | Visualise Results | Present findings in charts, graphs, and dashboards that communicate insights clearly to both technical and non-technical audiences. | Tableau, Power BI, Google Data Studio |
| 6 | Share Insights and Act | Present conclusions to stakeholders, recommend actions, and document findings for future reference. | PowerPoint, reports, dashboards |
Key Concepts in Data Analytics
Before you can work effectively with data analytics, there are several core concepts you need to understand. These concepts come up repeatedly across all types of data analytics work.
| Concept | What It Means |
|---|---|
| Structured vs Unstructured Data | Structured data is organised in rows and columns — like a spreadsheet or database table. Unstructured data has no predefined format — like emails, social media posts, images, or audio files. Most analytics work starts with structured data. |
| Quantitative vs Qualitative Data | Quantitative data is numerical and measurable (revenue, temperature, count). Qualitative data is descriptive and categorical (customer feedback, product categories, job titles). Analytics often combines both types. |
| Data Cleaning | The process of identifying and correcting errors, inconsistencies, and missing values in a dataset before analysis begins. Clean data is essential for reliable results — “garbage in, garbage out” is a foundational principle of data analytics. |
| Statistical Significance | A result is statistically significant when it is unlikely to have occurred by chance. This concept helps analysts determine whether a pattern in data is real or just random variation. |
| Correlation vs Causation | Correlation means two variables change together. Causation means one variable directly causes a change in another. Confusing these is one of the most common errors in data analysis — for example, ice cream sales and drowning rates are correlated (both increase in summer) but neither causes the other. |
| Dashboard | A visual display that consolidates key metrics and data points into a single view, updated in real time or on a schedule. Dashboards are used to monitor performance and communicate status to stakeholders without requiring them to analyse raw data. |
| KPI (Key Performance Indicator) | A measurable value that demonstrates how effectively an individual, team, or organisation is achieving a specific objective. In data analytics, identifying the right KPIs is the first step before any analysis begins. |
Tools Used in Data Analytics
| Tool | Category | Best For | Skill Level |
|---|---|---|---|
| Microsoft Excel | Spreadsheet | Data cleaning, basic analysis, pivot tables, charts | Beginner |
| SQL | Database querying | Extracting and filtering data from relational databases | Beginner to Intermediate |
| Python | Programming | Data cleaning, statistical analysis, machine learning (pandas, NumPy, scikit-learn) | Intermediate to Advanced |
| R | Statistical computing | Advanced statistical analysis, academic research, data visualisation | Intermediate to Advanced |
| Tableau | Data visualisation | Creating interactive dashboards and visual reports for stakeholders | Beginner to Intermediate |
| Power BI | Business intelligence | Microsoft ecosystem reporting, connecting to Excel and Azure data sources | Beginner to Intermediate |
| Google Data Studio | Data visualisation | Free dashboards connected to Google Analytics, Sheets, and BigQuery | Beginner |
The foundation of data analytics is made up of these tools and technologies, which enable experts to gather, examine, and visualize data to make deft decisions. Whether you’re a data scientist, analyst, or business analyst, these tools satisfy a broad range of analytics requirements and guarantee accuracy and efficiency in deriving useful insights from data.
1. R and Python
Python is a general-purpose programming language and has become the language of choice for Data Engineers, scientists, and analysts. It has extensive libraries for data analysis and visualization.
R is also a powerful language suited for statistical computing. It provides extensive support for all types of statistical analysis.
Key Features:
- Extensive libraries for statistical analysis and machine learning.
- Versatility in handling and manipulating data.
2. Microsoft Excel
A ubiquitous spreadsheet engine with robust data analysis capabilities.
Key Features:
- User-friendly interface for data manipulation and visualization.
- Fundamental for basic analytics tasks and reporting.
3. SQL
SQL (Structured Query Language) is the de-facto language for retrieving, manipulating, and managing data. Data Analytics initiatives need extensive use of SQL at various stages.
Key Features:
- Highly versatile language that is easy to learn.
- Standard SQL is supported by most of the databases. So you can learn it once and use it everywhere.
4. Tableau
Tableau is an advanced data visualization software for interactive and intuitive insights.
Key Features:
- Seamless integration with various data sources.
- Rich visualization options for effective communication.
5. Power BI
Microsoft’s Power BI for creating dynamic and interactive reports.
Key Features:
- Real-time data analysis and sharing.
- Integration with other Microsoft products.
6. SAP Business Objects
Business intelligence suite offering a range of reporting and analysis tools.
Key Features:
- Comprehensive tools for business intelligence and performance management.
- Integration capabilities with various data sources.
Data Analytics vs Data Analysis — What Is the Difference?
The terms data analytics and data analysis are often used interchangeably, but they have distinct meanings in professional practice. Understanding the difference helps you communicate more precisely and choose the right approach for each task.
Data analysis is the process of inspecting, cleaning, and modelling a specific dataset to discover useful information. It is typically a project-based activity with a defined start and end. Data analytics is the broader field — it encompasses the tools, methods, and ongoing systems used to analyse data continuously across an organisation.
Think of it this way: every data analytics system involves data analysis, but not every data analysis task is part of a larger analytics system. A business analyst running a one-off report on customer churn is doing data analysis. A company building a real-time customer behaviour monitoring platform is doing data analytics.
How to Get Started with Data Analytics
If you are new to data analytics, the best approach is to build skills progressively rather than trying to learn everything at once. Here is a practical starting point:
| Step | What to Learn | Resource or Tool to Use |
|---|---|---|
| 1 | Learn Excel for data work | Microsoft Excel — pivot tables, VLOOKUP, basic charts. Most entry-level data roles require Excel proficiency. |
| 2 | Learn basic SQL | SQL is used to query databases and is required for almost all data analyst roles. Start with SELECT, WHERE, GROUP BY, and JOIN statements. |
| 3 | Learn data visualisation | Practice creating charts and dashboards in either Tableau Public (free) or Power BI Desktop (free). Focus on telling a clear story with data rather than technical complexity. |
| 4 | Work on a real dataset | Use a public dataset from Kaggle or Google Dataset Search to practise the full analytics process — from data cleaning through to a finished chart or report. |
| 5 | Consider a certification | Once you have basic skills, a certification validates them for employers. Google Data Analytics Professional Certificate and Microsoft PL-300 are good starting points. |
Want to build practical data analytics skills? Techcanvass offers two focused courses for business analysts — Power BI course covering data modelling, dashboards, and stakeholder reporting, and IIBA-CBDA certification preparation for analysts looking to formally validate their data analytics competency.
Why Data Analytics Matters: Real-World Examples
Data Analytics has applications across industries and functions. It can be used to improve performances, optimize processes, provide insights, and predict forward-looking insights.
Here are some examples of how Data Analytics has helped organizations.
Credit Scores
The credit score is used by banks and financial institutions to decide whether the loan can be extended to the customer. It is a statistical analysis performed by lenders and financial institutions based on historical data of the customer.
It has become the most widely used score for checking the creditworthiness of a customer.
Typically the credit score (FICO score) is in the range of 300 to 850 (It’s 300 to 900 in India). The FICO score is calculated by taking into consideration – Payment history, Credit Exposure, Credit type and duration, and other factors.
The readmission rate was reduced by 40%
Let us take the example of UnityPoint Health. At UnityPoint Health, predictive analytics helped in predicting the readmission risk for each patient.
The hospital scored every patient for readmission risks. Using these results, the hospital was able to predict and prevent a patient’s readmission within thirty days through the early treatment of the symptoms. In less than two years, this hospital was able to reduce readmissions by 40%.
Fraud Detection
Transaction fraud is a challenging problem for Banks and Financial Institutions. Fraud detection represents a set of proactive measures undertaken to identify and prevent fraudulent activities and financial losses.
Fraud detection involves the use of statistical analysis and Artificial Intelligence. Any fraud detection system also faces a challenge because of constantly changing fraud patterns and fraudsters’ tactics.
Reference: Fraud Management in Banks
Optimizing Inventory
Inventory Optimization is achieved through prescriptive analytics. If an organization is using multiple distribution channels for selling its products, it’s extremely difficult and complex to determine the optimal inventory strategy. The solution lies in inventory optimization using prescriptive analytics.
Ref: Analytics in Supply Chain Management
Conclusion
Data analytics is a broad discipline but the fundamentals are consistent across industries and tools. Once you understand the four types of analytics, the data analytics process, and the core concepts covered in this guide, you have the foundation to begin working with data in a meaningful way.
The next step is to choose a tool to practise with — Excel or Power BI are the most accessible starting points for business professionals — and apply these concepts to a real dataset.
