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Types of data analytics
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Types of data analytics

What are the different types of data analytics?

Data analytics involves processing and performing statistical analysis of existing data sets. Data analytics can be of four types depending on the type and scope of analysis being conducted on the data set. These are:

  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics

The four types of data analytics

1. Descriptive analytics: What happened?

Descriptive analytics uses historical data from a single internal source to describe what happened. For example: How many people viewed the website? Which products had the most defects? This type of analytics employs simple mathematical and statistical tools, such as spreadsheets, instead of complex calculations to create visualisations, like bar charts or line graphics to describe a data set. Used by most businesses, descriptive analytics forms the crux of everyday reporting, especially through dashboards.

2. Diagnostic analytics: Why did it happen?

Diagnostic analytics is a form that dives deep into historical data to identify anomalies, find patterns, identify correlations, and determine causal relationships. Though diagnostic analytics can be performed manually, the rise of big data has pushed analysts to employ machine-learning techniques for the analysis. Unlike humans, computers can process vast amounts of data, recognise patterns, detect anomalies, and expose ‘unusual’ events. They can apply analytical techniques from a portfolio of algorithms to identify drivers of change and determine causation.

3. Predictive analytics: What might happen next?

As an organisation increases its analytical maturity and embarks on predictive analytics, it shifts its focus from understanding historical events to creating insights about a current or future state. Predictive analytics lies at the intersection of classical statistical analysis and modern artificial intelligence (AI) techniques.

By employing predictive analytics, organisations identify the likelihood of possible outcomes, which can guide them on the best course of action. Predictive analytics is used in many sectors, such as the aerospace industry to predict the effect of maintenance operations on fuel use, and the manufacturing industry to predict future requirements and optimise warehouse stocking accordingly. Simple predictive models can be created using tools such as spreadsheets or Tableau.

4. Prescriptive analytics: What do I need to do?

Prescriptive analytics is the most complex type of analytics. It combines internal data, external sources, and machine-learning techniques to provide the most effective recommendations for business decisions. In prescriptive analytics, a decision-making process is applied to descriptive and predictive models. This leads to finding a combination of existing conditions and possible decisions that are likely to have the most effect in the future. This process is both complex and resource-intensive but, when done well, can provide immense value to an organisation.

As well as identifying and programming each decision, data scientists developing prescriptive models need to prevent missteps by ensuring that all possible outcomes are considered. After deploying these systems, they must repeatedly test these models to ensure they are making meaningful recommendations.

In summary, as we move from descriptive to prescriptive analytics, each model offers increasing value to an organisation. But, at the same time, these analytics increase in complexity.

Illustration showing the increase in difficulty and value for the different types of analytics. Descriptive analytics is the least valuable the easiest to implement. Prescriptive analytics is the most valuable but also the most difficult.Click to enlarge

Adapted from: Gartner’s Analytic Value Escalator [1]

References

  1. Gartner’s analytic value escalator [Image]. Gartner; 2012. Available from: https://www.flickr.com/photos/27772229@N07/8267855748/
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