Energy & Technology

Data Analytics and Data Analysis: What Are the Differences, Similarities and Innovative Applications?

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Data analytics and data analysis. Are these two in-demand tech skills related? Do they have the same meaning? What are the clear distinctions between data analytics and data analysis? And just how do I spot the difference between data analytics and data analysis? Let us find out.

Data Analytics and Data analysis are two terms that are used interchangeable even by data professionals and since interest is being drawn more towards data now, questions are being raised concerning the distinction between both terms. Data analytics or data analysis, which is the correct term for which process?

Let us begin by defining our terms and understanding the basic concepts of both data analytics and data analysis.

What is Data Analytics?

In that article, data analytics is defined as; “the collection of data and application of certain techniques or tools, statistical or logical, for the evaluation of data to identify a certain trend or simply just to make sense of provided / collected data in order to reach a conclusion of interest.”

In other words, it is simply just a business concept introduced to optimize decision making and profits. The sole aim of data analytics revolves around making findings from data sets easy to understand and easy to apply in business decision making. 

How does Data Analytics work?

Actually, they are several stages involved in data analysis. They are:

  1. Data discovery and formation
  2. Data preparation and processing
  3. Designing a model
  4. Building the designed model
  5. Result publication and
  6. Measuring of effectiveness

Okay, these steps might sound a little bit technical but they are quite simple. Did I mention that there is no standard number of steps for data analytics? With experience, a data analyst may skip a step, combine two or more steps and he/she may even choose to add. Oops! Let us not deviate from our topic. 

What then is Data Analysis?

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If we Imagine Data analytics as an ordered and delivered pizza, then one slice of pizza can be used to illustrate data analysis.

Data analysis is a subcomponent or a single step in data analytics. It comes together with a whole bunch of other steps to generate a suitable conclusion for making predictions, making business decisions and in general, optimizing business operations.

 Data analysis involves the investigation of a particular dataset –from the past – to gain understanding and insights on what has been previously recorded. This would include data processing, cleansing, modelling, questioning etcetera; to basically identify a trend that could be introduced in other stages of data analytics to draw out a conclusion necessary for optimizing business operations and identify the best actions to take based on analysis.

Data analysis presents data in a form that is not easily understandable by business individuals so therefore the need for data analytics that presents data in an interactive and easily understandable form cannot be over emphasized.

What Techniques are used in Data Analysis?

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There are a lot of techniques utilized in data analysis, but the most popular ones are

  • Text analysis.
  • Statistical analysis.
  • Descriptive analysis.
  • Diagnostic analysis. 
  • Predictive analysis.
  • Prescriptive analysis.

What is the Relationship between Data Analytics and Data Analysis?

As earlier mentioned, data analytics is a tech skill most companies and industries are beginning to look out for. This is because with data and the inferences drawn from data, one man can surpass the other. The more data one has and the more he understands and employs it, the more he optimizes his business.

But before one can make decisions from data, one has to first understand it. This is the process of Data Analysis which includes data processing, cleansing, questioning, modeling etc. After analyzing data and drawing insights, the results from data analysis are introduced in other tools, systematic or logical, to draw conclusions, make predictions and take practical business decisions. Now that is Data Analytics.

3 Clear Differences between Data Analytics and Data Analysis

  1. Data analysis is a subset of data analytics and not the other way round.
  2. Data Analysis draws inferences from datasets from the past WHILE Data Analytics makes future predictions based on results from data analysis.
  3. The output from Data Analysis is not clearly presented for everybody’s viewing pleasure most often, only skilled persons can interpret results WHILE Data Analytics produces interactive outputs that are readily understandable and user friendly.

 What tools are required for Data Analytics and Data Analysis?

The most common tools required for data analytics and data analysis are:

  1. Tableau
  2. Microsoft Excel
  3. SPARK
  4. Node XL
  5. Microsoft Power BI
  6. R analytics
  7. Python
  8. Google Analytics

Practical Applications of Data Analytics and Data Analysis

Data analysis and analytics has been introduced into various sectors of the world today for the purpose of business planning, decision making, for drafting budgets etc. these practical illustrations would help you further understand the concept of data analysis and analytics and how deep it is penetrating into our everyday lives.

Health Sector

One easy to grasp example is the use of data analytics in the health sector. The health sector is one that generates tons of data daily and when data can be sorted out and cleaned, it can be utilized to make accurate treatment choices for certain illnesses. Imagine a certain uncommon disease has fifteen methods of treatment, but only one out of all fifteen treatments can work on a particular patient and for another patient, another treatment works. 

If a new patient walks in, instead of trying treatments over and over again on the patient to see which would work, I could just run data analytics on the existing data to determine which treatment would likely be the best match for the patient based on his test results.


Another Example is in the transportation sector. Assuming I want to set up a new transportation company in a town in a developing country, I have to first collect data on the percentage of the population that actually patronize public transportation services like buses and taxis and what percentage of persons prefer their private rides. With this analysis, I can make decisions on the number of buses, taxis and other transportation services to introduce to the particular areas.

Amongst Data Analytics and Data Analysis, the most important happens to be data. This is because without data, these two terms cease to exist.

Search Engines

Let us look at another example. Search Engines. Popular search engines that we make use of everyday, if you are reading this post, it means you made use of one. These search engines – Google, Yahoo, Bing, Duckduckgo etc – utilize data analytics in generating a data set from the data (keywords) you searched for. They search through vast numbers of published articles and sites and decides the best matches to your search query and these are displayed for your viewing pleasure in matters of milliseconds. 

In the energy industry, data analysis and analytics are also applied in smart-grid optimization, automation, energy optimization, energy distribution and lots more applications.

These are just a handful of examples. A lot more applications are out there and a lot more are coming up with new advancements in technology. Check out this article: Augmented reality careers: explained for beginners

 I’m sure by now you understand the concept of data analytics and data analysis as well as the differences and similarities that exist between them, their applications, tools and techniques. Leave any questions you may have in the comment section and we will attend to them.

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