What are the four types of Data Analytics?

3 November 2021

What are the four types of Data Analytics?

The world of Data Analytics can seem never-ending, with various techniques, innovations, and tools appearing all the time.

With a landscape that continues to shift and change, understanding how best to apply analytics practices to your enterprise, or how to reach the next level of insights, can often be confusing and frustrating. To help, we’ve created a guide to the four core types of Data Analytics, these being:

  1. Descriptive Analytics
  2. Diagnostic Analytics
  3. Predictive Analytics
  4. Prescriptive Analytics

As each of these four types of analytics progresses, the value to a business increases with each step.

While the necessary beginnings of data-driven intelligence are often in descriptive analytics, which every enterprise wishing to succeed should implement, the later steps utilise Machine Learning and Artificial Intelligence techniques to add greater value and insight than ever. This is done by enabling a proactive, future-proofed approach, making greater the chances of success and growth.

Understanding descriptive analytics: What’s currently happening in your enterprise?

Any enterprise wanting to begin its analytics journey should begin with descriptive analytics.

Using traditional BI approaches such as data visualisations and more, descriptive analytics harnesses and compares historical data sets to provide enterprises with an overall view of their current stance and identify trends. These can vary in form, from changes in revenue to areas that require further improvement.

Some areas examined in descriptive data analytics include:

  • Year-over-year pricing changes
  • Month-over-month sales growth
  • The current number of users
  • Total revenue per subscriber

Diagnostic analytics: why are these elements happening?

While descriptive analytics uncovers what’s currently happening within an enterprise, diagnostic analytics take this one step further by drilling down into the why. In doing so, diagnostic analytics help enterprises understand why changes have occurred. This is done by using historical data to allow enterprises to either rectify changes to better refine operations or apply beneficial techniques in other areas for further optimisation.

Diagnostic analytics is usually performed using methods like data mining – which automates the analysis of wide ranges of historical data to find relevant information. Another technique commonly used at this stage is drilling-down: focusing on a certain facet of the data to dive deeper into specific intricacies.

Looking to the future with predictive analysis

Using Machine Learning techniques such as neural networks and multiple regression, predictive analytics allows businesses to assess and model future scenarios, improving operations further and predicting the outcomes of high-level strategic decisions.

The initial implementation of predictive analytics tools can be complex and often time-consuming based on the need to cater to ML setup and processes. However, once implemented, these capabilities can provide immense value to enterprises wishing to reach the next level of optimised strategy.

Predictive analytics is commonly used in situations such as:

  • Predicting audience engagement with particular social and marketing campaigns
  • Understanding the chances of profits when releasing a promotional sale
  • Predicting how a particular quarter will affect yearly revenue and loss

 Discover more about Machine Learning here.

Prescriptive Analytics – What can I do next?

Often seen as the opposite of descriptive analytics, prescriptive analytics examines and investigates information gained during the predictive stage, before suggesting an optimised course of action or strategy.

Just like predictive analytics, prescriptive analytics relies on the use of Machine Learning practices, which can make initial setup and implementation time-consuming and lengthy. However, the benefits that prescriptive analytics brings to an enterprise far outweighs the initial challenges.

Used in many unique sectors from emergency services to retail and finance, the use cases of prescriptive analytics are endless. Some examples include, to name a few:

  • Suggesting an adjustment of prices to ensure maximum conversions and profit
  • The greatest, and most successful procedure for a hospital patient
  • Proposing pathways to maximise profitability within incredibly risky ventures such as investing

For enterprises that want to shift to a prescriptive approach to analytics, a great option is to consider outsourcing implementation to specialists. Learn more about how we’ve helped previous clients reach enhanced insights in our case studies hub here.

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To learn more about how Data Analytics and Business Intelligence can provide access to previously invisible intelligence, as well as how we can help get you there, get in touch with us here.

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