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Data analytics maturity

Let’s start to introduce the concept of data analytics maturity by looking at how it relates to the NHS case study.

The diagram below helps you to identify your level of decision-making maturity. It shows the journey from simple reporting to identifying opportunities that will optimise your processes. The speech bubble above each box highlights what question you are able answer at each stage.

Image showing questions and related data processes.  Data: What data do you trust? Reporting: What happened?  Analysis: Why did it happen? Monitoring: What's going on now? Predicting: What could happen?  Optimising: How do I do it better?  Innovating: Where next?Central elements of the Data Analytics Maturity Model (Click to expand)

As you travel from left to right, the complexity of the solution tends to increase, as does the value it offers your organisation.

If we apply this to the NHS case study you can see that as the maturity increases, so does the value.

Image showing Data Model  answers for NHS. Reporting - know which patients expereinced delayed discharges. Analysing - which datapoints increase liklihood of delay. Monitoring - which current patients are likely to become delayed. Predicting - which interventions will reduce delay. Optimising - make real time interventions to stop patients experiencing delayed discharge. Data model questions applied to the NSS case study (Click to expand)

Your data analytics mapping

Based upon what we have discussed, take the Data Analytics Maturity Canvas in your workbook, capture some notes on your ability to deliver value at these stages of maturity.

Some questions to consider are :

  • what is the best (and worse) practice in your organisation?
  • where are your stakeholders’ level of maturity?
  • is there a range of maturity across your organisation?

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This article is from the free online course:

Introduction to Data for Business Leaders

The Data Lab

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