4 Types of Data Analytics

Data analytics can be classified into 4 types based on complexity & Value. In general, most valuable analytics are also the most complex.

1. Descriptive analytics

Descriptive analytics answers the question:  What is happening now?

For example, in IT management, it tells how many applications are running in that instant of time and how well those application are working. Tools such as Cisco AppDynamics, Solarwinds NPM etc., collect huge volumes of data and analyzes and presents it in easy to read & understand format.

Descriptive analytics compiles raw data from multiple data sources to give valuable insights into what is happening & what happened in the past. However, this analytics does not what is going wrong or even explain why, but his helps trained managers and engineers to understand current situation.

2. Diagnostic analytics

Diagnostic Analytics uses real time data and historical data to automatically deduce what has gone wrong and why? Typically, diagnostic analysis is used for root cause analysis to understand why things have gone wrong.

Large amounts of data is used to find dependencies, relationships and to identify patterns to give a deep insight into a particular problem. For example, Dell - EMC Service Assurance Suite can provide fully automated root cause analysis of IT infrastructure. This helps IT organizations to rapidly troubleshoot issues & minimize downtimes.

3. Predictive analytics

Predictive analytics tells what is likely to happen next.

It uses all the historical data to identify definite pattern of events to predict what will happen next. Descriptive and diagnostic analytics are used to detect tendencies, clusters and exceptions, and predictive analytics us built on top to predict future trends.

Advanced algorithms such as forecasting models are used to predict. It is essential to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization.

For example, HPE Infosight can predict what can happen to IT systems, based on current & historical data. This helps IT companies to manage their IT infrastructure to prevent any future disruptions.



4. Prescriptive analytics

Prescriptive analytics is used to literally prescribe what action to take when a problem occurs.

It uses a vast data sets and intelligence to analyze the outcome of the possible action and then select the best option. This state-of-the-art type of data analytics requires not only historical data, but also external information from human experts (also called as Expert systems) in its   algorithms to choose the bast possible decision.

Prescriptive analytics uses sophisticated tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage.

For example, IBM Runbook Automation tools helps IT Operations teams to simplify and automate repetitive tasks.  Runbooks are typically created by technical writers working for top tier managed service providers. They include procedures for every anticipated scenario, and generally use step-by-step decision trees to determine the effective response to a particular scenario.