Data analytics is the overall process of capturing and using data to produce meaningful information, including metrics and trends, that can be used to better understand events and help make better decisions. Usually the goal is to improve the efficiency and outcomes of an operation, such as a business, a political campaign, or even an individual (such as an athlete). There are four (4) prevalent types of data analytics – descriptive, predictive, diagnostic, and prescriptive.

1. Descriptive analytics – provides information about “what has happened”. Examples of questions answered by descriptive analytics include: How much are our sales this month and what is over year-over-year sales increase? How many website visitors did we have and how many signups?
2. Predictive analytics – provides insight into “what may happen” in the future based on the past. Examples of questions answered by predictive analytics include: Based on previous customer service call patterns and outcomes, what is the likelihood of a customer switching to another provider? Based on a customer’s profile, how much should we charge him for insurance?
3. Diagnostic analytics – provides information to explain “why something happened”. In addition to the direct data, this may also involve more indirect or macro data sources, such as, weather data, local or national economic data, or competitor data. And it may also involve forming logical theories about the correlation of events. Examples of questions answered by diagnostic analytics include: How effective was the marketing blitz and which channel had the most impact? Did the weather affect sales or was it the price increase?
4. Prescriptive analytics – provides insight into “what to do to make something happen”. Examples of questions answered by prescriptive analytics include: Based on the results of our test marketing blitz campaign, if we roll out the full campaign with adjustments to the channel spread, how many additional temporary customer service staff will we need to handle the increased volume without long wait times?

Descriptive analytics is the simplest and most common form of analytics used in organizations and is widely referred to as Business Intelligence (BI). There is widespread interest in predictive analytics but less than 50% of companies currently use it as it requires additional, more expensive skills. Diagnostic and prescriptive analytics have always been around because companies have always used information from descriptive analytics to hypothesize “why things happened” and make decisions on “what to do”. But it’s the automation of these types through new methods and the integration of more data inputs that is fairly new. The latter three forms are sometimes called Advanced Analytics or Data Science.

All the types of analytics will require some form of data integration and use some of the same data in an environment, but while descriptive analytics only needs data from the time periods being analyzed and usually from a narrower data set, the predictive, prescriptive and diagnostic analytics produce better results using as much data as is available from a wider timeframe and from a broader set of sources. There is overlap with the different types of analytics because the analysis of “what may happen” is driven by “what has happened” in the past and “why it happened”; and determining “what to do” will be driven by “what has happened”, “why it happened”, and “what may happen”. Companies on the forefront of data analytics will tend to use all four types.