31 January 2019
What is data maturity?
Once upon a time, long ago, the data produced by a business was seen as nothing more than another piece of nostalgic memorabilia.
Information across the business would be recorded and handed to the secretary who would file it away in a lonely corner where it would face the inevitable fate of collecting dust for the next quarter of a century.
But things have been changing over the past few years…
Firstly, the amount of data we produce has been rapidly expanding and it’s predicted that by 2020 there will be 43 trillion gigabytes of data created – an increase of 300 times from 2005.
On top of this, technology has been swiftly advancing and now the power of Business Intelligence (BI) and Analytics has made it possible to dissect complex data to draw meaningful results from it.
And finally, and perhaps the greatest change of all, is the attitude towards data. Rather than simply being filed away in case of emergency, data is increasingly being recognised as a modern commodity and this is changing many organisations’ approach to what they do with their data.
In short, the big data revolution is here.
2. What is Data Maturity?
But what does this have to do with the term “data maturity”?
Well, as the opportunities that data holds are opening up, organisations essentially have the choice as to what they do with the data that they produce, and this is exactly what data maturity refers to.
We can define data maturity as:
“The extent to which an organisation utilises the data they create”
It attempts to ask “how much value are you getting out of your data?”.
And an organisation’s answer to this question will determine their level of maturity.
3. The Stages of Data Maturity
We can think of data maturity as a journey. As you start to use more sophisticated techniques to analyse your data, you’ll start to progress in your data maturity journey.
The more an organisation use their data, the more data mature they are, and consequently the further along in the data maturity journey they are.
This means that an organisation that uses advanced BI & analytics software to analyse their data is considered to be far more mature than an organisation that relies solely on spreadsheets to carry out their reporting.
The stages of the data maturity journey can be defined as the following:
STAGE 1 - Operational
Reporting is limited to tasks that are critical for business operations, with no formal BI & Analytics tools or standards in place to support this. Spreadsheets are generally 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 across the business.
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 can even be used 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.
4. Benefits of progressing in the Data Maturity Journey
Deloitte’s recent study revealed that data mature organisations that embraced analytics had a competitive advantage over their less mature counterparts. Here are a few of the benefits they found:
- Better enablement of key strategic initiatives – Did you know that data mature organisations are better equipped to reach their strategic goals? A survey by MIT and IBM reported that organisations using more advanced analytics had 8% more sales growth, 24% higher operating income and 58% more sales per employee.
- Informed decision-making – Good decision-making is always driven by data. Data mature organisations are able to harness their data to make better-informed decisions. A survey by Deloitte found that nearly half of all respondents reported that better decision-making was a key benefit of unlocking their data using analytics. Another 16% reported that its greatest benefit was in better enabling key strategic initiatives. Whilst nearly two-thirds of respondents said that analytics capabilities played an important role in driving business strategy.
- Understanding of customers and employees – Having advanced data insights means that data mature organisations can get into the heads of their customers and employees to understand what truly makes them click. Hewlett-Packard had the issue of high management turnover which was leading to revenue loss due to the reduced productivity and the cost of having to re-recruit. Using analytics, they were able to identify the key risk factors that ultimately led to employees wanting to leave and allowed them to put strategies in place to prevent this from happening.
- Ability to react to economic changes – Many organisations are at the peril of changing circumstances, having to simply make the best of a bad situation when it comes around. Having technologies like predictive analytics at their fingertips gives data mature organisations the power to take a proactive approach to change rather than simply reacting to problems. This allows them to prepare for problems in advance and in some cases even prevent issues from occurring in the first place.
5. How to determine your level of Data Maturity
After working with organisations of all different shapes and sizes, we’ve identified the characteristics that define the various stages of the data journey.