Types of Data Analysis
By Adesile Ajisafe, PhD CEng MIMechE
At its core, data analytics is about answering questions and making decisions. And just as there are different types of questions, there are also different types of data analytics depending on what you are hoping to accomplish. Four primary types of data analytics:
Descriptive analytics is the most basic form of analytics. This type of analytics answers the question “What has happened?”. Descriptive analytics, analyses the data coming in real-time and historical data for insights on how to approach the future. The main objective of descriptive analytics is to find out the reasons behind previous success or failure in the past. It uses data aggregation and data mining to collect and organize historical data, producing visualizations such as line graphs, bar charts, pie charts. Descriptive analytics presents a clear picture of what has happened in the past, such as statistical modelling. Most big data analytics used by organisations falls into the category of descriptive analytics. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company at an aggregate level and describe the various aspects.
Diagnostics analysis is performed on internal data to understand the “why” behind what happened. This kind of analytics is used by businesses to get an in-depth insight into a given problem provided they have enough data at their disposal. Diagnostic analytics helps identify anomalies and determine casual relationships in data. Understanding why a trend is developing or why a problem occurred will make your business intelligence actionable. It prevents business team from making inaccurate guesses, particularly related to confusing correlation.
Predictive analytics is used by businesses to study the data to find answers to the question “What could happen in the future based on previous trends and patterns?”. When you know what happened in the past and understand why it happened, you can then begin to predict what is likely to occur in the future based on that information. Predictive analytics takes the investigation a step further, using statistics, computational modelling, and machine learning to determine the probability of various outcomes.
Prescriptive analytics is where the action is. This type of analytics tells teams what they need to do based on the predictions made. It is the most complex type of data analytics. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximise key business metrics. It basically uses simulation and optimisation to ask, “What should a business do?” Prescriptive analytics is an advanced analytics concept based on optimisation that helps achieve the best outcomes and stochastic optimisation helps understand how to achieve the best outcome as well as aiding to identify data uncertainties to make better decisions.
In summary, both descriptive analytics and diagnostic analytics look to the past to explain what happened and why it happened. Predictive analytics and prescriptive analytics use historical data to forecast what will happen in the future and what actions you can take to affect those outcomes. Typically, Forward-thinking organisations use a variety of analytics together to make smart decisions that help their business.