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The Data Maturity Journey

What is data maturity?

Data maturity is the extent to which an organisation utilises the data they produce. The more they do with their data, more data mature they are, and consequently the higher up on the maturity scale they are.

This means that an organisation that uses advanced Business Intelligence and analytics software to analyse their data can be considered to be far more mature than an organisation that relies on spreadsheets to carry out reporting.

You can think of the data maturity journey as a scale.

Take our data maturity quiz to find out where your business is on the maturity scale.

Data maturity quiz

Not sure what stage your business is at?

Take the data maturity quiz to find out!

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The five stages of business data maturity

Take our quiz to find out which data maturity stage your business is at, or explore the stages below and find out how to reap the benefits of progressing your business to the next stage.

STAGE 1 - Operational

 

Reporting is limited to tasks that are critical for business operations, with no formal BI & Analytics tools or standard in place to support this, and spreadsheets used as a primary means of reporting.

STAGE 2 – Descriptive

 

BI & Analytics are in their early stages of implementation and are used to report on activity.

STAGE 3 – Planning

 

Using tools like scenario planning, BI & Analytics are used not just to report on what’s happening, but to plan for the future.

STAGE 4 – Predictive

 

Data Analytics is used to predict what will happen five, ten, even twenty years from now and to pinpoint the key drivers of trends.

STAGE 5 – Prescriptive

 

Users no longer have to input variables into the system to predict future outcomes. Instead, Machine Learning and Artificial Intelligence make it possible to detect issues before they’re even considered.

Why your data is an asset

Data is everywhere.

And did you know that research by Deloitte shows that 96% of executives believe that using the right tools to get the most out of this data will become more important than ever over the next few years? (Deloitte, 2017)

Surveys show that 79% of finance departments have invested in analytics technology (Deloitte, 2017), whilst 80% of marketing decision-makers have made increasing their use of data and analytics a top initiative (Forrester, 2017), and 84% of companies believe that people analytics is essential to the HR function. (Deloitte, 2018)

This isn’t a fluke.

Data has become a modern-day asset to organisations, and using analytics technology to take full advantage of data is becoming an increasing priority.  

This brings us nicely to the topic of data maturity.

Why does the data maturity journey matter?

If you don’t know where you are, how can you know where you’re going?

Understanding your level of data maturity and choosing to advance in your data journey is essential in achieving your organisational goals… and many organisations are seeing the results in full-effect.

A survey by MIT and IBM reported that organisations with the high level of analytics had 8% higher sales growth, 24% higher operating income and 58% higher sales per employee (IBM, 2014).

On top of this, a study carried out by Deloitte, found that organisations that use people analytics to support business decisions see 82% higher three-year average profits than their low-maturity counterparts (Bersin by Deloitte, 2017). Whilst other research shows that companies that invest in analytics are paid back $13.01 for every dollar spent. (Nucleus Research, 2014)

Here are the four main benefits of progressing in your data and analytics maturity:

1. Better enablement of key strategic initiatives

2. Informed decision-making

3. Understanding of customers and employees

4. Ability to react to economic changes

(Deloitte – The Analytics Advantage)

Why our data maturity model?

After working with organisations of all different shapes and sizes, we’ve identified the characteristics that define the various stages of the data journey.

Our data maturity journey model provides a framework that helps organisations to advance in their data and analytics competency.

It allows organisations to reflect on the current state of their data quality, analysis methods, level of expertise, systems and technologies; before highlighting the issues they need to overcome in order to progress.

Then, by getting an organisation to clarify their future goals, the appropriate technology and data strategy can be applied to work towards making the desired outcome a reality.

Data maturity model references

Deloitte – The Analytics Advantage:
https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Deloitte-Analytics/dttl-analytics-analytics-advantage-report-061913.pdf

Deloitte – How CFOs can own Analytics:
https://www2.deloitte.com/content/dam/Deloitte/us/Documents/finance-transformation/us-how-cfos-can-own-analytics-091814.pdf

Forrester Global Business Technographics Marketing Survey 2016:
https://www.forrester.com/report/Global+Business+Technographics+Marketing+Survey+2016+Overview/-/E-RES137189

Deloitte 2018 Global Human Capital Trends:
https://www2.deloitte.com/content/dam/Deloitte/us/Documents/finance-transformation/us-how-cfos-can-own-analytics-091814.pdf

IBM, 2014:
http://www-935.ibm.com/services/us/gbs/thoughtleadership/peopleequation/

Bersin by Deloitte, 2017:
https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/audit/ca-audit-abm-scotia-high-impact_analytics.pdf

Nucleus Research, 2014:
https://nucleusresearch.com/research/single/analytics-pays-back-13-01-for-every-dollar-spent/

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