AI – from single purpose to Generative and General Purpose, will impact every business. Whether they use it or their competitor uses it, AI is pervasive. Let’s address one area of the AI journey which is essential for decision-makers to understand – the type of analytics that frame decision-making processes.
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Let’s briefly review the types of analytics that provide leaders with direction and how they may impact your decision-making process.Â
Descriptive Analytics:Â The most basic focus of this analytics category is to describe what happened. Think of charts depicting sales trends for the past five years, reports, and even dashboards where users query a database for values showing what occurred during a specific period or location. For instance, a user may ask what the annual volume shipped for a particular food commodity over the last five years was.
Diagnostic Analytics:Â With this category, we inquire about why something happened. Applied to the example below, we break down the components of the yearly volume and can overlay another variable, such as weather.
Predictive Analytics:Â Building on the available information, we create statistical models in this category and project what may happen by entering possible values into equations. This type of analytics involves discovering correlations among variables and requires significant data to develop and test models for accuracy.
Prescriptive Analytics:Â Building on knowledge acquired from other categories, this type of analytics goes as far as recommending courses of action using advanced analytics and large amounts of data.
Data Analytic teams rely on different categories depending on the business need and the challenge. Defining the problem - the hypothesis - is as important as selecting the analytics category to generate the necessary insights. Collaboration between the business functions seeking answers and the analytics teams who create the models to provide the answers is paramount for success.