An illustration of analytics' evolution
Source: Gartner

Analytics is the science of producing insight from the patterns in complex data to make better decisions. It relies on statistics, predictive modeling and computer science.

Analytics is not a new concept for organizations. It has been a relevant topic for organizations since the mid-90s. Starting with sales and customer data, businesses applied analytics to their decision-making processes. Analytics is evolving with advances in machine learning. While analytics in the past focused on descriptive and diagnostic approaches, modern analytics is predictive and prescriptive.

What are the different types of analytics?

Analytics can be split into 4 categories. While automating descriptive analytics was possible since the early days of computing, machine learning and AI enable companies to automate issue diagnostic, outcome prediction and prescription of next actions.

  • Descriptive Analytics (What happened?): Showing what is actually happening based on given data; often usually via dashboards and reporting tools.
  • Diagnostic Analytics (Why did it happen?): Analyzing past performance to determine not only what happened, but why it happened.
  • Predictive Analytics (What could happen?): Describing what scenarios are likely to occur, often in a predictive forecast.
  • Prescriptive Analytics (What should we do?): Making suggestions about what should be done and their basis.

Descriptive analytics and visualizations

The most basic analytics is descriptive analytics, which answers the question of what happened in the past with a given data set. Almost every organization uses this type of analytics, which involves arithmetic operations, mean, median, max, percentage, etc. It gives organizations helpful insights about actions in the past, yet, causes of the problem may not be provided by descriptive analytics. That’s why data consultants don’t recommend companies to implement descriptive analytics only.

Diagnostic analytics

It provides answers about why something happened in the past. Companies that use diagnostic analytics are more likely to understand the causal relationships between actions that can be considered as a deep insight into the root causes of the events. It involves techniques of data discovery, data mining, and correlation while using statistical expressions such as probability, likelihoods and distribution of outcomes for the investigation.

Predictive analytics

At this stage, organizations get insights about possible results of their actions. Predictive analytics answers the question of what is going to happen in the future with data programming tools such as R, Phyton, IBM SPSS and RapidMiner. Predictive analytics provides and validates predictive models by using machine learning algorithms like random forests, learning, testing data. It expects the future will reflect a pattern similar to the precedent data. Organizations usually consult data scientists and machine learning experts to handle data for predictive analytics.  

Prescriptive analytics

Analysts try to find out what action to take or how to take advantage of future opportunities with a given data set. Prescriptive analytics is not fail-proof, even though it uses advanced machine learning algorithms and business guidelines to analyze possible decisions and potential influences of those decisions. Success of prescribed analytics is highly dependent on how well your model includes external data besides your organization’s internal data set.

For more info on analytics, feel free to read our comprehensive article on analytics.

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