What Are the 5 Types of Data Analytics?

Descriptive analytics is one of the most commonly used types of data analytics.

As the name suggests, descriptive analytics aims at describing or summarizing data to answer questions such as – where, when, what, how many, etc.

Let us explain how.

Descriptive analytics typically works with large data sets. It breaks down these large data sets into smaller units. Using these small data units draws insights and trends to analyze past performances. This, in turn, guides managerial decision-making.

It is no wonder, then, that 90% of organizations around the world use data analytics.

Descriptive analytics is reactive and is usually conducted periodically to observe data trends over a fixed period.

All those quarterly performance reports or sales reports are the results of your data team running descriptive analytics.

Some uses of descriptive data analytics include:

• It helps businesses derive answers for past successes and failures
• Descriptive analytics works with large sets of data, hence it is instrumental in data mining, dashboards, and building advanced business intelligence systems
• Descriptive analytics helps detect outliers or anomalies in data – this helps businesses identify potential opportunities or risks
• Financial reporting & sales reporting, and quarterly performance reporting are all outcomes of descriptive analytics.

There are two types of descriptive data analytics:

Ad hoc reporting – This kind of analytics isn’t scheduled, planned, or previously designed. They are conducted at the spur of the moment to address a specific business question.

For example, you may conduct ad hoc descriptive analytics to answer questions like:

• What’s my social media following demographic break-up like
• Which region do I have the most buyers from
• Which age group mostly buys my products

Ad-hoc reports are usually a one-time analytical exercise and not periodic. Managers may wish to look into such statistics at a later date, again based on an ad-hoc query.

Canned reporting – These kinds of reports are curated or designed repeatedly to answer the same questions but in different periods. Some examples of canned reporting include monthly ad revenue, monthly sales reports, and quarterly revenue reports.

Descriptive data analytics is rightly considered wider compared to other quantitative analytics and is often a predecessor for more in-depth forms of analytics.

We all are curious to know what the future holds for us.

Businesses are no different – And that is where predictive analytics comes in.

Forecasting events of the future is at the core of predictive analytics. Analyzing consumer preferences, purchase patterns, and seasonal changes in demand for a product – all these are different predictive analyses.

Here is another way to look at it.

Predictive analytics derives trends, patterns, and indications from a data set. Analysts then use these patterns or trends to identify correlations or causation patterns between two variables.

These patterns are, in turn, used to determine what may or may not happen in the future, based on which many high-level managerial decisions are made.

For example, predictive analytics can analyze the chances of buyers purchasing a conditioner after purchasing a shampoo from the same brand. Analysts can draw correlation patterns to observe if one decision influences the other.

But how do businesses draw up these trends and patterns?

What are the tools used to derive inferences from data?

Let’s dig in a little deeper.

Predictive analytics uses various tools such as neural networks, regression, machine learning, predictive analytical models, statistical modeling, artificial intelligence, decision trees, and more.

Now the next big question – how reliable are these trends, inferences, or patterns?

The answer to this is simple.

The bigger the data set, the more reliable any clear patterns or trends that arise out of it. For predictive analytics to be successful, it is wise to use as big a data set as possible.

In some cases, analysts may even use outliers or wide deviation data points to arrive at a more defined trend or pattern.

Prescriptive analytics provides a business with all possible prescriptions or suggestions to achieve a desired outcome.

Prescriptive analytics is one of the strongest tools to face business uncertainties or risks in the future.

It helps businesses answer questions like – What if I do this? What if I try that? What is the result if you take route B and not route A?

For example, if a business aims to increase its bottom line net profit figures for the upcoming quarter – what would be the best way to achieve this result? Will reducing operating costs help achieve this, or should it boost production to increase sales?

That is the crux of prescriptive analytics.

How does one derive all the possible suggestions for a desired outcome?

Let us explain how.

Prescriptive analytics relies heavily on artificial intelligence and machine learning strategies and algorithms. With the advancement of technology, data analysts need the only program the ideal algorithm to run a prescriptive analytics exercise accurately.

These algorithms or models throw up various possibilities for a desired outcome.

Hence, it is fairly evident that prescriptive analytics relies heavily on AI and technology. It is only as successful or accurate as the algorithm you draw up.

Like descriptive analytics, prescriptive analytics is also the most accurate or reliable with a larger data set.

In an environment where businesses are constantly faced with economic, political, geographical, and ecological uncertainty, prescriptive analytics is a much-reliant form of data analytics.

The fact that the appointments of Chief Data Officers (CDO) in organizations jumped from 12% in 2012 to 63.4% in 2018 according to an article shows that it is an independent and pivotal business function.

All businesses make mistakes.

But, it is important to learn from those mistakes and move forward.

That is the role of diagnostic analytics. This form of data analytics helps understand if and why a particular event occurred and the reason for its occurrence.

Diagnostic analytics is more probing and penetrating in nature compared to descriptive analytics.

For example, suppose the sales in a company have dropped for a particular quarter. In that case, descriptive analytics will show you how, and diagnostic will tell you why, such as seasonal changes, regional laws, higher public holidays, etc.

So, how do data scientists examine large data sets to draw out a diagnosis for an event?

Let us explain.

Tableau, Power BI, data discovery, drill down, data mining, discovery & alerts are some diagnostic analytics tools.

There are two types of diagnostic analytics:

• Drill down: Drill down is a diagnostic technique useful to get more details on a particular topic. For example, if a particular region shows the lowest sales for a quarter, a drill-down may reveal that there was a curfew and political unrest there.
• Discover & alerts: This type of diagnostic analytics flags an issue before it occurs. For example, discovering this statistic is diagnostic analytics if this is the lowest sales quarter of the year.

Diagnostic analytics is a strong tool for businesses to identify the cause of outliers, downward trends, or positive upward swings and take corrective measures/continue with what’s working.

It is a widely used data analytics form involving data inputs and hypothesizing.

Cognitive analytics is a fairly newer type of data analytics. It involves tying together human thinking with data analytics.

Humans can relate two correlated data columns, draw inferences from data and recognize data sets that can impact each other. Cognitive analytics is all about enabling computers to do the same kind of thinking as humans.

But here is the question:

How does one make this possible?

By bringing together different types of technology and software, including machine learning, deep learning models, artificial intelligence, and automation.

Data is run through these technologies and a human-like thinking system to generate automated decision-making capabilities based on the trends and patterns from the data.

Cognitive analytics somehow summarises the evolution of data analytics.

Data analytics grew from hindsight to foresight and is now growing tremendously in automated decision-making through cognitive analytics.

Latest data analytics statistics reveal that over 90% of enterprises are starting to see value in data analytics. The U.S. Bureau of Labour estimates a 36% growth for data scientists by 2030.

The modeling and programming in cognitive analytics are tremendous – but so are the benefits.