Types of Data Analytics

The main purpose of big data analytics is to help an enterprise understand its needs and preferences. Organizations must make smarter decisions for better business results. The next important step after the big data created over time as a result of connecting with different systems / devices working in an enterprise is to start using analytics. Big data analytics is not a one-size-fits-all general strategy; it can be thought of as identifying and applying different types of analytics that can be used to get advantages for the enterprise.

Let’s explore the types of data analytics to help you figure out where to start, what types of analytics applications can accelerate business growth, and what will make a difference to your enterprise…

Descriptive Analytics

‘Descriptive Analytics’, describing what has happened over a period of time, is the most basic form of definition.

In short, it can be thought of as the “What Happened” question. It answers the questions of your enterprise such as “Is the number of scraps decreased?, Is the number of production more this month than before?” It analyzes incoming data, and  in this way, it allows you to have visions using real-time and historical data about what kind of strategy you can follow in the future.

The purpose of descriptive analytics is to find the reasons behind success or to reach the reasons for failure in the past. An entity can only anticipate future effects from its past actions. Therefore, descriptive analytics are used to describe the overall performance of the company at an aggregate level.

Predictive Analytics

By analyzing historical data, it allows an entity to set goals for what may happen in the future, make effective planning, and have more perspectives and suggestions for the future.

Predictive analytics, based on your company’s past trends and patterns, makes it easier to answer questions such as: ‘What could it be? Could it happen in the future?”

The purpose of predictive analytics is NOT to tell you what will happen in the future. It cannot do this. In fact, no analytics can do that. It only allows you to get probabilistic predictive analysis of what might happen in the future.

It analyzes with various statistical and machine learning algorithms. Since the accuracy of the predictions is based on probabilities, the accuracy rate is not 100%. Algorithms take data to make predictions and supplement the missing data with the best possible predictions. Organizations should design an analytical and effective business strategy that can develop statistical and machine learning algorithms to take advantage of prediction.

Predictive analytics can be categorized in the following ways:

  1. Predictive Modeling – If so, what is next?
  2. Root Cause Analysis – Why did this really happen?
  3. Data Mining – Identifying related data
  4. Prediction – What if current trends continue?
  5. Monte Carlo Simulation – What could it be?
  6. Pattern Description and Notifications – What kind of action should be taken to correct this situation?

 

Suggestive Analytics

It can highlight issues in precise language and help an entity understand with data support. It highlights factors and observations that will create prescriptions for business problems that may ocur, and it advises on possible outcomes.

It identifies data uncertainties with techniques such as machine learning, simulation, and optimization to ask the question “What should an entity do?”. In this way, it helps to achieve the best results and understand how to achieve the best results, enabling better decisions to be made. Based on its descriptive and predictive analytics results, it identifies data uncertainties and recommends actions against a possible scenario.

Detective Analysis

They are analytical applications that are performed by asking the question ‘why’ to understand what is happening by looking at the collected data. Detective analytics helps to identify anomalies and identify relationships in data. Entities planning to gain in-depth knowledge on a particular problem using this type of analytics must have sufficient data on hand. For example, instead of looking at the general situation, entities can focus on a specific product and examine in detail with detective analytics why they cannot meet the expectations on the basis of this product.

Day by day, more and more businesses are starting to realize that the use of big data has become one of the most important competitive differences. At this point, entities cooperating with DIGITERRA may reduce operational costs and increase their service quality by turning to the right type of data analytics applications that fit their structure and meet their needs.