4 Types of Analytics

Analysis has been the hot cake in the room. Every second person is either speaking about analysis or reading about it. It may be inquisitive for the naïve learners what is it all about Analytics, and what can be done by it. In broad terms, Data analytics has been divided into four types.

1.      Descriptive – What is happening now?

2.      Diagnostic – What happened and why?

3.      Predictive – What might happen?

4.       Prescriptive – What actions should be taken?

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1.      Descriptive Analysis

Descriptive Analysis provides the analyst with a view of key metrics and measures within the company. It answers the question of what happened. For instance, a healthcare provider will learn how many patients were hospitalized last month; a retailer – the average weekly sales volume; a manufacturer – a rate of the products returned for a past month, etc.

Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only, and prefer combining it with other types of data analytics.

2.      Diagnostic Analytics

On the assessment of the descriptive data, diagnostic analytical tools will empower an analyst to drill down and in so doing isolate the root-cause of a problem. It allows to drill down, to find out dependencies and to identify patterns

Let’s look at the examples from different industries: a healthcare provider compares patients’ response to a promotional campaign in different regions; a retailer drills the sales down to subcategories.

3.      Predictive Analytics

Whether it’s the likelihood of an event happening in future, forecasting a quantifiable amount or estimating a point in time at which something might happen - these are all done through predictive models. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Its accuracy highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization.

The variability of the component data will have a relationship with what it is likely to predict (e.g. the older a person, the more susceptible they are to a heart-attack – we would say that age has a linear correlation with heart-attack risk). These data are then compiled together into a score or prediction.

4.       Prescriptive Analytics

The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. Prescriptive analytics uses sophisticated tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage.

The prescriptive model utilizes an understanding of what has happened, why it has happened and a variety of “what-might-happen” analysis to help the user determine the best course of action to take. An excellent example of this is a traffic application helping you choose the best route home and taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic constraints. A prescriptive analysis is typically not just with one individual response but is, in fact, a host of other actions.

Few areas of implications are mentioned below:

Descriptive Analysis:

·         pattern of students who failed the examination

·         causes of deaths of soldiers

Diagnostic Analysis:

·         reason for high hospital admissions

·         impact of fuel price on vegetable price

Predictive Analysis:

·         how long will the employee stay in the company

·         amount of sales next year

Prescriptive Analysis

·         allocation of resources for optimum utilization

·         allocate the vehicle to route for employee pickup and drop

Source: https://www.scnsoft.com/blog-pictures/business-intelligence/4_types_of_data_analytics-01_1.png

REFERENCE

https://www.scnsoft.com/blog/4-types-of-data-analytics

https://insights.principa.co.za/4-types-of-data-analytics-descriptive-diagnostic-predictive-prescriptive

https://www.kdnuggets.com/2017/08/top-stories-2017-jul.html