Better Decision Making for Your Business With These 4 Types of Data Analytics
As far back as the 17th Century, the author John Dryden wrote of the need to dive deeply into a subject to find real meaning.
Back then data analysis was not something people had in mind but it was still widely recognised that gathering information was essential in order to find deeper meaning. Time has moved on and today there are 4 key types of data analytics that can help your business and we will look at each of these in turn:
This type of analytics describes what has already happened by taking raw data from a variety of sources to give insights into past events. The main drawback of this form of analytics is that although they will indicate what is right or wrong, they cannot explain why this is the case and therefore are always combined with other types of analytics in order to gain a much broader insight.
These analytics diagnose what has already happened and to do this they are often measured against other types of data. Diagnostic analytics focus on patterns and look into a particular issues as deeply as possible to gain insights into cause effect. To be successful with this type of analysis a company must keep extensive and detailed data. If they fail to do so the necessity of extensive data gathering for each event and for each metric proves extremely time consuming.
As their name suggests this type of analysis allows a prediction to be made about potential future events. This takes a look a descriptive and diagnostic analytics and highlights tendencies, exceptions and clusters of events to forecast future trends. It relies heavily on good quality data and must be continuously optimized. As a result it is very proactive, enabling the comparison of risk, cost and cash flow analysis to make predictive decisions of future trends.
This type of analysis prescribes what actions should be taken to remove problems or take advantage of trends, either current or predicted. This is a key form of data analysis requiring internal historical data compiled in combination with external information. It uses the most advanced tech; machine learning for example, alongside algorithms and rules. It is a complex form of analysis which makes it more complicated to manage so an in-depth cost/benefit analysis needs to be carefully considered before committing to implementing this type of analytics.
How Does This Apply to Real Life Businesses?
Most companies have a need for one or the other of these forms of data analytics at different stages of their development moving through data gathering, predictive and prescriptive analysis. Most companies recognise the need for descriptive and diagnostic analytics but recognise that to be proactive the predictive and prescriptive forms are often more relevant. Current trends indicate that companies are turning more and more to advanced data analysis and choose to adopt this once the benefits start to outweigh costs.