Data Analytics, Simplified

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Publish date:

October 28, 2022

Updated on:

June 11, 2024

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Data Analytics, Simplified


Data Analytics is an area of the technology world that is attracting increasing levels of attention. It can be a challenge to unpack what it is exactly, let alone what you could do with it. Data analytics has set the stage for a wholly different way of handling and managing people’s data, and it has also opened up new avenues of work. Where to begin when trying to understand Data Analytics in earnest?

You might just want to start with this article right here.

Data Analytics: Defining Terms

To start, let’s examine the different basic elements of Data Analytics. As a discipline, Data Analytics is a blanket term to describe activities relating to extracting raw data, processing it, and gleaning insights from it. As puts it:

“It comprises the processes, tools, and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.”

Why are so many words used to describe something that, at its heart, is actually quite simple? Because Data Analytics may seem straightforward - it is just taking data, analyzing it with different lenses, and then using the outcomes for business benefit.

As with anything to do with human behavior, though, it’s… complicated.

Let’s try and simplify this with an unpacking of what Data Analytics is all about.

Data Analytics: Use Cases

To what end are companies looking to gain data about their customers, and then pick it apart to notice trends and patterns within it? Oftentimes, it is about some business benefit, but it can have knock-on effects in other areas. The company is not, after all, only about its own benefit, but hopefully also about the benefit for other relevant stakeholders. Below are some use cases to demonstrate what Data Analytics can be used for, in practical terms.

Customer Experience

Trying to understand data coming from several sources, in different formats, with different points of reference, can be challenging at best - and impossible at worst. Data Analytics lends processes, tools, and platforms with which to get a more holistic view of disparate sources of information, according to this ScienceDirect article by Pethuru Raj. For instance, when trying to gain an understanding of a company’s success in satisfying its customers, it can be difficult to gain an accurate representation, unless the data behind it is streamlined somehow. Take it from someone who once used Excel to piece together three global companies’ customer satisfaction scores and make a cohesive picture to present back to their companies’ Board! While you can provide insightful, custom feedback to a company’s customer performance this way - it can be time and resource-costly.

The argument becomes one where you can let the processes of Data Analytics create a “ single, consolidated” view of the data for you - giving you an arguably better kick-off point, than several sources and types of data that need restructuring.

Market Analysis

According to the aforementioned ScienceDirect article, there are other motives for utilizing Data Analytics. For instance, to gain a good handle on market forces and where they might be headed, so you can set sail for similar waters. If you gather up large reams of data on market trends, you are more likely to be able to predict the next big thing to look out for, and how you can move the needle for your company, in that direction.

For instance, a tangible outcome could be a SWOT analysis - whereby Strengths, Weaknesses, Opportunities, and Threats are identified. This is an inroad into understanding market players and a company’s position, relative to them. Read more examples from our data analytics experts.


Another area where Data Analytics can prove useful is within the healthcare sector. By collecting data on patients, there are real, tangible information sources on their health status and their behaviors. This can help practitioners with their interventions, making them more precise and ideally, more effective. Among the benefits cited to patient experience are:

“proactive risk monitoring, improved quality of care, and personalized attention.”

5 oatmeal/granola cookies with white icing rest on top of each other in a stack

Data Analytics: 5 Types

The field of Data Analytics can broadly be classified into the below 5 categories. For an article that also gives an overview of Data Analytics and the top companies within it, feel free to refer to our article on the topic.

Descriptive Analytics - The ‘What’

This is the act of taking data and as the name implies, describing it. This is usually the first, most basic stage of parsing the data. According to Bi Connector, this often entails visualizations of the data for ease of understanding and making sense of it at the earliest stages.

Diagnostic Analytics - The ‘Why’

To start to be useful, the data must be able to tell us something about the ‘why’ of the matter. Why is the pattern described above happening? These factors can help to make better business decisions because if the pattern demonstrates that we are headed in the wrong direction for our business aims, we can course-correct.

A concrete example of how this can deliver business value in concrete financial terms comes from a case study by Accenture:

“Within three months, by harnessing AI and ML to detect and understand root causality in the CMP process, Semiconductor Manufacturing Analytics delivered significant business value for the client. The analytics concepts at the fab allowed for a way to reinvent a platform across the enterprise and leverage data as a competitive differentiator.”

Indeed, this resulted in saving costs to the tune of 10 million USD. So Data Analytics is nothing to be scoffed at.

Predictive Analytics - The ‘When’

Here, too, it is all in the title. As puts it,

“Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Predictive analytics is often considered a type of ‘advanced analytics,’ and frequently depends on machine learning and/or deep learning.”

Predicting future outcomes is worth its weight in gold. If you can predict the future, then you can place bets that are in your favor. It follows, then, that you’d be able to set the course of your business according to the winds of fortune.

Prescriptive Analytics - The ‘What Now’

The prescriptive side of analytics speaks to what recommendations are in place to deal with the earlier stages. This means, taking action based on what has been found from the data, and basing decisions on them. Often this will also include machine learning techniques, as well as algorithms.

Cognitive Analytics - The ‘Edge’

There are approaches within Data Analytics that mimic human intelligence, according to Ulster University. Using some of those techniques mentioned above, like AI, machine learning, and algorithms - adding in semantics and deep learning - you’ve got yourself the perfect cocktail of machines being able to act like humans, making sense of data as we would.

Does it give you the ick, or are you seeing dollar bills flying in front of your eyes? Either way, it seems to be an area that will only grow as AI techniques get more sophisticated, offering enhancements to machine-led data analytics that will circumvent the need for human analysts - a topic we referenced at the start of this very article! See how neatly that rounds off our list of 5?


To gain a full picture of the field - it might be worth mentioning a startup that is addressing concerns that some users of digital platforms have. That is issues around data privacy and security. Gener8 is a startup founded by Sam Jones, a founder on a mission to monetize the data analytics approaches used by big corporate organizations and provide a portion back to the users who provide it. A Peter Pan of the digital world, if you will. The logic follows; if users are providing data, which may lead to purchasing decisions or retention with a given merchant, then it makes sense for them to profit as much as the company to which they are providing their data. Right? If this all sounds confusing, head on over to the Gener8 website to learn more.

If you are interested in Pangea vendors, and how they might help you with Data Analytics, take a look at our search tool. Some of the companies that might be able to help in this endeavor, specifically in the data visualization area, are Tech387, Kalmia, and Pixion.


Q1. What are the 5 types of data analytics?

The 5 types of data analytics are as follows: 1) descriptive, 2) diagnostic, 3) predictive, 4) prescriptive, and 5) cognitive. These 5 types are complementary to one another, and the first step tends to be descriptive, giving an account of the data in question. After this first step, more complex action can be taken, including diagnosing what may be the causes for certain patterns identified, followed by predicting and recommending next steps. The newest category of data analytics is cognitive, and describes how technologies such as AI, deep learning, semantics, and algorithms are applied to mimic human analytics capabilities.

Q2. What is data analytics with examples?

Data Analytics offers opportunities for making impacts on business objectives, including cost savings (see Accenture case study above), efficiencies, brand value, and more. An example would be using data analytics to understand the customer experience of a given company, aggregating and analyzing large datasets to provide a comprehensive view of the customer base. Once done, a picture of the current customer experience levels can be developed. This can then inform interventions made to improve said experience.

Q3. What is data analytics for beginners?

Data Analytics is, simply put, the process of extracting data, processing it, and performing analyses on them to be able to provide insights on the data for business benefit. In a basic way, it means taking information about your users or customers and trying to understand what they need or want. By acting on this, you are more likely to hit the mark and thereby create benefits for your company, both in terms of reputation and in terms of finances.


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Naina Kaul

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As a Management Consultant with a background from Accenture and Baringa Partners in London and currently in a freelance capacity, Naina is well placed to build a startup. Her Psychology degree from University College London enables her to leverage person-based insights to generate community, foster support and rally for a cause she truly believes in: making sustainable transport the new normal.

With a passion for cycling from growing up in Copenhagen, she is an advocate for making cycling through winter a viable alternative to cars and public transport - capturing a moment for winter cycling that has been covered by both The Guardian and The New Yorker. Her lifestyle products for bikes with heat functionality and design are as much a personal expression for your bike as they are a push out of the door during winter when cyclists find themselves lacking motivation.

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