Growing your business requires great decision-making and advanced data analytics is a sure-fire way to improve your approach. There are four main types of advanced data analytics, and we’ll go into detail about each one so you can make better business decisions.
Descriptive data analytics is about answering the question of what happened. In other words, it uses this information to develop a clear idea of what exists within a company. As an example, data from a monthly revenue report could be used to know how a company has performed.
The descriptive analytics process works through business metrics being decided, relevant data being sourced, prepared, analysed, and presented. The advantages of this model are that it can be easily applied in day-to-day operations, is quick to report on and insights can be used to make improvements.
Yet descriptive data analytics has its limitations because it doesn’t go beyond the surface of the data, which is why other forms of analytics may be more suitable.
Diagnostic analytics gets to the heart of ‘why’ something happened and allows you to go deeper into your data. The process involves reading, scanning, filtering, and breaking down information to identify the specific causes behind problems and behaviours.
As an example, you may want to get a deep overview of all the issues that are impacting productivity in your company. By using diagnostic data analytics, you could conduct a survey of all staff working across different locations and filter out performance and attendance metrics to understand why productivity is being affected.
Business Information (BI) platforms are excellent diagnostic data analytics tools because they allow you to drill down into information, discover important insights and get to the crux of problems.
Predictive analytics is one of the most exciting forms of advanced data analytics models because it helps to determine what could happen in the future. It leverages the findings of descriptive and diagnostic analytics to predict trends and forecast possible outcomes.
For example, a predictive analytics model could help to identify customers that are likely to abandon a product by examining their past behaviour and historical data. Armed with this knowledge, a company could use this insight to change their marketing campaigns and reduce the likelihood of the customer leaving.
While predictive analytics provides a forecast, prescriptive analytics prescribes the right action to take in solving a problem. This advanced data analytics model also compares the most suitable course of action out of multiple choices.
An example of prescriptive analytics is a vehicle app making a countless number of estimations to identify the best route to a destination. It would consider vehicle speed, road layout, traffic congestion and more and then make the recommendation on the shortest route.
Within your company, you may use one or all these models to help you improve your decision-making and it’s all based on personal preferences. To define what works best, it’s useful to answer these questions:
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