Top 5 Data Analytics Types: A Beginners Guideline

Wherever we look around, we find data. It may be documents, social media, news, feedback, or product descriptions. Hence, data is everywhere, and it is we, who make the most of it. A business can fail if you don’t analyze your content, so data analysis is a must! Apart from data analysis, it is a must to know about data analytics types so that you can use those methods to extend your business. By the end of this article, you will understand all about data analysis and data analytics types.


What is the Importance of Data Analysis?

Analyzing data by looking into the past helps us set records for new plans and attain what we desire. These patterns ultimately help us climb the steps and reach our goals. For example, you own a grocery store, buy from the companies, and sell it to the customers with some profit percentage.

If you own a grocery store and want to fix clients, then you get an idea to start keeping dairy products. As we all know, dairy products are perishable. Now, the challenge you face is that you can’t understock them due to fear of losing potential clients. You can’t overstock them as they might turn your profit into a loss if they expire.

Analyzing your customer footfall and demands, you can analyze the requirements and accordingly make an Excel sheet on MS Excel (or use various other software like Google Charts, Power BI, Zoho Analytics, Infogram, and many others) of the number of items you require per day. Data analysis is the most practical approach for all business owners out there.

“Data-driven companies are 23 times more likely to acquire customers than their peers. – Forbes”

  • Better decision-making – Once you have all the data handy, you can form decisions according to the demand and supply of products required during that day, month, or time of the year. By observing such trends, you’ll be able to market your products better, and as said, ” no publicity is bigger than mouth publicity”. Once you’re a trendsetter, people attract automatically!
  • Expanding your business- After analyzing what do you have? A clear picture of what is blooming and what is not. That might be the perfect way to gather knowledge and expand your business with some tips on Data Analytics Types.
  • Effect on efficiency- You now know what products are working out for you, hence, this is the perfect moment to analyze if there is 0.1% lacking anywhere. Yes, no business is ‘ever’ perfect, so this might be the moment to check out how to maintain or improve efficiency, either by increasing productivity or reducing cost.
  • Customer relations- Once you understand the needs of your customers targeting your business, it scopes for improvement and more potential clients.

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Who Requires Data Analysis?

Anyone interested in making a living on the business level requires basic knowledge of Data Analysis and Data Analytics Types. At a professional level, if you ignore the performance and continue with the old practices, you might never know what is fruitful for you. Heat and trial methods are necessary while in business.

  • Marketing Executives- Be it digital marketers or field marketers, everyone in this field is required to analyze the data and plan their next move according to the performance of the previous marketing campaign. Marketers are in direct reach of customers as they promote the company’s motto by branding and communicating directly with customers attached to your brand. You don’t want your customers to see the same or previously worked campaign. Change gives an attractive point of view to the viewers.
  • Product Managers- The actual providers of something different; they are the state-of-the-art for the company. They are liable to analyze the market, improve the products, and gather knowledge about the products in demand by potential clients. If they won’t analyze the data, how can they come up with unique ideas that are useful enough for the people? Even analyzing the feedback on your products plays a crucial role, you might not know why it failed till there is complete research on what people think of it.
  • Human Resource Professionals- HR serves as the bedrock of successful organizations. These professionals are directly linked with the surroundings at the office. For example, if a person leaves his job, there can be various reasons. To gather those insights and act accordingly for improvements to ensure peace in the minds of employees, that’s what a Human Resource professional requires that can just be ensured by analyzing!
  • Finance Executives- Professionals working in this sector are one of the most important ones for a company’s flourishment because they have the data that is in-respective to the profit or loss. This data, when analyzed, can explain what the profit from and what is not working out for the brand. If this data is left without much acknowledgment, there is a high chance that the company might never bloom.
  • Business Owners- With a better understanding of what is in demand and what is off track, it is finely important to gather insights on the products related to your business. To classify this into analyzing or working on Data Analytics Types, your 95% doubts on ” how to run a profitable business?” will clear.

Process of Analysing Data

Changing a raw material into a powerful insight for your company to grow is what data analytics can do. An easier way of decision-making and precisely using the information for your benefit is something not everyone is smart enough to think of. There can be many ways one can gather the information and interpret it.


However, the Flowchart is as Follows:-

  • Specific Information
  • Collection of Useful Data
  • Process the Data
  • Analyse
  • Interpret
  • Report and Final Conclusion

Specific Information

The step-1 for this process of Data Analysis is finding answers to specific questions related to your business field. These questions might be your thoughts to gain profit.

For example:-

  • How to grow the brand name?
  • How can we reduce the cost price without degrading quality?
  • How can we engage on social media?
  • How can we increase sales with the current marketing strategy?

Achieving clear answers to these questions can clear up your thoughts for many profitable solutions. Gathering information about the questions that you can self-ask might build a whole other confidence in working towards your goal.

Collection of Useful Data

Looking for your source of information is highly important, and what is more important than that is to keep that information maintained so you can look it up at any time of requirement. Organizing that data into storage formats can give a lot of clarity and fore more create pressure to keep it up to date. Apart from the source of information you have, there might be some that you are unsure of. Hence, finding that information is also equally important, be it from different sources.

Process the Data

All the gathered data might not be something you need. Hence, after sorting your required data, you can start processing that data into data analyzing tools. Check out the areas where most of the errors occur and start editing.


Different Data Analytics Types allow you to work out ways that are best for your company’s questions or problems. The understanding and solutions derived from the analysis will be personalized for your brand.


After the complete analysis, when it’s time for interpretation, ask yourself a few questions before the final report.

  • Is there anything else that we should consider?
  • Is the information sufficient?
  • Does the data help solve all the questions?

Report and Final Conclusion

The Final Report Could Be Shared With Many Levels of People Like:-

  • The client
  • Business Leader
  • Supervisor

Hence, the Points to Remember for the Report Should Be:-

  • Easy to understand- If you are making a report for any level of people, it should be so clear that the person can easily rummage through the information.
  • Use of pictorial representations- Now, there is always a better and more precise understanding of pictorial forms such as pie charts, flow charts, excel sheets, or tables.
  • Summary- Always add a summary to the report for the reader to find out the potential information without reading the whole report again and again.

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Top 5 Data Analytics Types: A Complete Guideline For A Beginner

There Are Five Major Data Analytics Types:-

  • Descriptive Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis
  • Statistical Analysis


1. Descriptive Analysis

What Do You Mean by Descriptive Analysis?

Descriptive Analysis helps us to answer the question, What happened? It is one of the most used Data Analytics Types by any customer or company/brand. Descriptive Analysis allows us to look at the past, whether success or failure for a growing future. It resolves the questions such as

  • How many new clients did we join?
  • What was the profit in the last quarter?
  • What was the most profitable in-demand product?
  • What was the overall growth of the company?

The data-driven by these questions is already available in numbers, hence which makes it super easy to analyze and work for the betterment and profit. Performing this initial foundation of data analysis is highly recommended by professionals to move forward toward the advanced level. Examples of data analysis are sales reports, customer segmentation, and website traffic analysis.


2. Diagnostic Analysis

What is the Role of Diagnostic Analysis?

Descriptive Analysis and Diagnostic Analysis work hand in hand. While descriptive analytics describes what happened, diagnostic analysis tries to gather further information.

Diagnostic Analysis Explains:-

  • Why did it happen in the past?
  • Why did the sales go down in the third quarter?
  • Why was there an increase in customers by the end of the year?
  • Why are some products performing better than others?

Diagnostic Analytics is more accessible and hands down a great data analytics type to solve business problem questions elaborately. By understanding the behavior pattern, all the information is easy to understand. Performing this type of data analytics means that you have adopted modern analytics tools by taking the help of Artificial Intelligence.

For example, If one fails to identify why sales went down by 15% in 2023, there will be nothing to improve upon!


3. Predictive Analysis

What Does Predictive Analytics Explain?

As the name suggests, Predictive Analytics collects data from descriptive and diagnostic analytics to predict the happenings in the future according to the data found. The historical data consists of descriptive and diagnostic analytics data that is used to build predictive analysis.

Predictive Analysis Helps to Figure Out:-

  • Why is the Customer dissatisfied?
  • How to avoid maintenance or breakdown of machines/websites?
  • How much traffic can be lost because of it?
  • How to identify fraud?

To get started with Predictive Analysis, you should think of a recurring problem to solve, and know what you want to predict and what you want to achieve out of it.

After you have answers to these questions, start building up a rock-solid data preparation for which you want the predictive analysis. Finding the appropriate data is a major factor for performing Predictive Analysis.

That is why the companies who are thorough with their database without the trash information have more accurate Predictive Analysis.

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4. Perspective Analysis

What Role Does Perspective Analysis Play?

Perspective Analysis is a step up from predictive analysis. This type of analysis is like a complete guide on ‘how to achieve the solutions that you analyzed through predictive analysis’. When you think of a question, you might have multiple solutions in mind.

To figure out which option is the best to pave the path to success, one should use perspective analysis out of all the Data Analytics Types. For example, it can automatically help you set the price for the products in demand by the majority of people or find out the number of candidates required for a particular job.

Perspective Analysis is a complete solution to all your problems during descriptive, diagnostic, and predictive analysis.


5. Statistical Analysis

Finally, what is Statistical Analysis?

Statistical Analysis is the statistical technique of summarising the problem. This is one of the very common data analytics types and is a more pictorial and easy-to-understand form of approach. And it can help you with the exact solutions such as:-

  • Features of data mainly to work upon.
  • The relationship of corresponding products.
  • The actual department that requires support.

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How to Become a Data Analyst?

Becoming a data analyst requires multiple skills like education, technical skills, and some experience in the related field. Some of the tips to guide you on the path of a successful data analyst are:-


Start by obtaining a bachelor’s or master’s degree in statistics, mathematics, or computer science. These programs offer a strong foundation for modifying your brain on the technical side.

Technical Skills

Enrol in a course to learn a new database language such as Python, SQL or MATLAB. Proficiency in any of these will open opportunities to get familiar with databases or analytical tools.


Gain experience by working on live projects where you will better understand the role. You can do this by earning an internship certificate or taking up freelance opportunities and later adding it to your portfolio to showcase your abilities.


Never think that you have complete knowledge, always keep learning because this is a field where you will require a constant touch of reading articles, new books, and a lot of critical thinking.

Attract Career Opportunities

After you are a complete pack of all the above-mentioned ways, start looking for entry-level jobs in different sectors like health, finance, hospitality, technology, etc. By showing your analytical side and problem-solving skills, you will be able to land any job that you desire.

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Future Scope of Data Analytics

The future of Data Analyst is one of the most promising in many sectors. As the demand for professionals in the technical problem-solving field is increasing, this can be a field with a bright future. There are chances for many opportunities in various sectors as of:-

  • Healthcare- Data Analysts can play a crucial role in the health industry as they can use different types of data analytics such as descriptive, diagnostic, predictive, and perspective as we have already discussed. By predicting a patient’s recovery and reducing the cost of services, there is a high scope for building a future here.
  • Finance- In the field related to finance, a data analyst can work as a savior. Saving people from getting stuck in fraudulent activities can be a major job handled by data analysts.
  • E-Commerce and Retail- Managing inventory or website traffic, a data analyst has enough technical and problem-solving skills in both cases. According to the demand, stocking in the essentials and optimizing the pricing strategy, is smart work.
  • Manufacturing and supply chains- In this sector, the analysts play an important role in managing manufacturing and supply efficiencies. They might manage the time and efficiency of supply chains, solving customers’ problems as well as increasing productivity.


Frequently Asked Questions

1.     What are the 4 major Data Analytics Types?

Predictive data analytics, Descriptive data analytics, diagnostic data analytics, and perspective data analytics.

2.     What do you mean by data analysis?

Data analysis is the collection of historical data that is used for the improvement and betterment of the business. By analyzing the data, a company can benefit from factors like better decision-making, expanding business, and enhancing efficiency.

3.     Is Data Analytics an IT job?

Data Analytics is not exactly an IT job, but it does require working with IT tools which helps them to gather insights regarding various concerns.

4.      Is Data Analytics a future?

Ans. Yes, the future growth of data analytics is for sure. With the generation of new data every day, all the sectors would require people to analyze what is the best solution to grow rapidly.

5.     Is a fresher good Data Analytic?

Yes, a fresher can be a great data analyst after having the proper educational qualification such as a degree or certification.

6.     Can AI replace Data Analytics?

AI might be able to replace many roles, but it cannot match the creativity and intuition of a human data analyst.


Data Analytics will emerge as one of the most scoped careers shortly. Researching critical data gives such importance to the applicant that can never be matched by any AI. Candidates with enough intuition and thinking capabilities with logical reasoning can make the most of it.

Through the different Data Analytics Types (Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Perspective Analytics, and Statistical Analytics), businesses can gain a deeper understanding of competitiveness, operations, market guides, and much more.

While descriptive analysis provides the answers to our whats, Diagnostic analysis gives deeper information to the whys. Predictive analysis, on the other hand, gives a glimpse into the future, and Perspective analysis works to make that vision happen.

Across various industries, Data Analytics Types can be used to benefit the understanding and requirements of the company. In the healthcare sector, a data analyst can do various things such as improve a patient’s recovery or optimize the machinery. Whereas in the financial sector, data analysts can help avoid fraud.

The retail industry has always been famous for maximum worker requirements, and job options like inventory manager, marketing plans, and supply chain manager will be available. That is what gives us enough clarity that data analysts will be in high demand because data is never-ending in our world!