A data maturity model measures your organisation’s capabilities with data. The Cognopia model has 5 discreet stages; Initial, Repeatable, Managed, Defined and Optimising.
- The Initial level describes organisations that have no formal data improvement program in place
- The Repeatable level is where most firms are today. At this level, organisations have defined data problems and are trying to fix them, but have not yet embedded these behaviours throughout their organisation.
- The Managed level describes firms that have a formal data management program in place. They have identified and funded programs for data improvement, but issues exist in compliance and execution.
- The Defined level occurs rarely, with less than 7% of our respondents achieving this score. These organisations have well managed, well funded data improvement programs and are able to monetise their data assets.
- The Optimising level is the highest score, where data is managed as an enterprise asset and optimised across all business programs. No firms hit this top score, which is unsurprising given the difficulty of embedding “data-first” behaviours across large enterprises.
Read on to learn more about the Cognopia Data Maturity Model research findings from our 2021 report. We present these findings in conjunction with tips and tricks for how your organisation can improve.
About the Cognopia Data Maturity Model
Cognopia put together an online data management maturity quiz to help organisations benchmark their data maturity. In discussions with clients, we found that many had engaged consulting firms to create data maturity reports for their businesses. These consulting firms charged tens of thousands of dollars for this work. Their processes were invasive, with interviews, investigations and system reviews. However, when we compared the results for a major Healthcare provider from our 37-question, online quiz to that produced in a 6-month consulting engagement, the results were nearly identical. Measuring your data maturity should not be an onerous, expensive operation.
What our data maturity model measures
Our data maturity model measures 9 core areas of data maturity:
- Data Assets and Value – this measures the aspirations and data culture in a firm. Does your firm see value in data? How does it want to use the data under it’s control?
- Business Alignment – how closely aligned are the goals of the data team with the goals of the business? Is your data function delivering business value or are you heading in the wrong direction?
- Data Governance Organisation – do you have the right functions in place to deliver great data? If so, are they working effectively?
- Data Stewardship and People – have you got the right people, in the right roles to succeed? Are they actively engaged in their work?
- Governance, Risk Management and Compliance – how well are you managing the risks associated with bad data?
- Data Classification and Metadata – have you documented the data that your business uses? Have you eliminated ambiguity in your business?
- Data Management Capabilities – do you have the right processes in place to execute data improvements?
- Technology – have you adopted technology to improve your data? If so, is it delivering value or is it a waste of money?
- Capacity to adopt Change – are you changing behaviours in your business to improve your data? Are your colleagues on board with the programme?
What our data maturity model does not measure
There is no analysis in the Cognopia data maturity model around analytics or the adoption of AI. The reason for this is that it is very challenging to identify skills in this area using an online quiz. In addition, whether or not you have the right data analytics skills is a function of the decisions you need to make as a business. Too often we have seen firms that are armed to the teeth with reports, analyses and visualisations of data that deliver zero value to the business.
We are also unable to measure the data literacy of your business. Without running a large-scale quiz of key workers to identify their understanding of data, it’s not possible to give an accurate assessment. Most likely you will have opinions on this already. To create a more rounded data maturity model, you ought to investigate whether your team can use data. Do they understand the meaning behind statistics? Are they aware of the limitations of data?
Cognopia’s 2021 data maturity model research summary
The theme behind Cognopia’s 2021 research study into data maturity is aspirations vs reality. Across the organisations, we surveyed one of the most impressive things was the prevalence of optimism. Without fail, our respondents reported that their firm had high ambitions for their data. Data may finally have come of age and be viewed as an enterprise asset.
Unfortunately, the optimism needed to be tempered. When we dug deeper into the details, it was clear that aspirations were not yet being achieved. Reality bit hard, and many firms struggled to translate the lofty vision statements of executives keen to ride this new wave of opportunity into tangible reality.
This understanding of the state of data maturity in 2021 has presented us a fantastic opportunity to bridge this gap. As such, we have laid out the findings of our report alongside case studies of successes and failures, and strong recommendations on how to improve data maturity in your organisation.
Data maturity – All companies – 2021 – Repeatable
A score of 2.61 on our maturity assessment represents an average maturity of “Repeatable”. At this level, our respondents have moved away from ad-hoc governance of their data. They have begun work to build teams to manage, govern, use, and possibly monetise their enterprise data assets. This is still a work-in-progress at this level of maturity, and more needs to be done to embed the right behaviours across these enterprises to unlock tangible business value.
Data maturity model – Aspiration vs Reality
When measured on aspirational scores alone (those looking forward and measuring enthusiasm for data), our firms take the leap into the Defined level. Organisations want to treat data as an enterprise asset, they want to run their organisations on data.
Aspiration
Reality
Sadly, the metrics judging how well firms have operationalised the usage, management and governance of data show that this goal is still a long way off. The gap between aspiration and reality is 0.8 points – nearly one maturity level.
Every firm we spoke to is working hard to improve the way their organisation uses data. One major reason for the difference between aspiration and reality is a lack of sponsorship from key executives.
Neil Burge, Cognopia CEO
Data maturity model – 2021 detailed findings
Now it’s time to take a detailed look at the findings from our report. The subsequent sections draw out the story of data from our 2021 data maturity report. Read on. to find out more.
Is data viewed as an asset?
The first piece of good news is that 100% of our respondents stated that they viewed data as an enterprise asset. This should not be a surprise, as the bulk of the people completing the assessments were working in data-related disciplines. Let’s take a look at the breakdown of these responses:
What we discovered
The Good
23% of our respondents stated that data is seen as a driver of competitive differentiation for their business.
The Bad
Out of this 23%, only 1 in 5 could put both hard and soft values on their data. Many still viewed data as a cost.
READ MORE: How to successfully monetise data
Data Policies are lacking
47% of respondents state that they still lack centralised policies that govern the creation, use, storage and archival of their data.
- Without clearly articulated policies that govern how data is to be handled, it is highly likely that data is in disarray.
- Staff at all levels have to understand what their role is in creating and maintaining data that is fit for purpose. Without rules, it’s impossible to know what to do.
Private and Critical Data remains opaque
16% of organisations still have no policies in place to track or protect Critical Private Data in their organisation – in spite of growing regulations in the space. A further 35% of firms are in the process of trying to govern this essential data right now.
- Jurisdictions across the globe have been tightening their rules around what we do with Private data. GDPR and CCPA may have led the line, but every jurisdiction should expect to see fines and penalties increase if they misuse or mishandle personal data.
How do organisations value their data?
We discovered that, even in companies that felt their data was a competitive differentiator, many failed to place tangible value on it. In fact, the score for those that were most ambitious about using data as an asset fared worse than our average:
- In 35% of all responses, organisations stated that “data value was only ever considered in terms of costs”.
Competitive differentiation with data is a great aspiration. Data is your most unique enterprise asset. But if you only view its value in terms of cost, you’re leaving a lot of money on the table.
Neil Burge, Cognopia CEO
What we discovered
The good
9% of our respondents are able to place both “hard” and “soft” dollar values on their data. Great stuff!
The bad
A whopping 63% of respondents only consider the cost of data or only measure value for some projects.
How does your organisation track the value of its data?
Case Study: A major South African Life Insurance company
2.00 – the Repeatable Level
At 2.00, this Life Insurance group was exactly at Level 2 – the start of the Repeatable level.
Case Study Background
This major insurance firm approached Cognopia to run a data maturity assessment after 1 year of running their data governance program. Their overall data maturity after 1 year was 2.00 – just at the cusp of the Repeatable Level. Their detailed score breakdown is below:
The major challenges
A deeper analysis of their individual scores showed that there had been an over-investment in Technology that was still poorly adopted or used by the business. Whilst the firm had implemented Erwin for their data modelling and metadata management functionality, their progress in documenting Critical Data and building enterprise data glossaries was slow. The team were beginning to document PII data in order to meet local privacy requirements, but lacked master data definitions and had no processes in place to remedy data quality issues.
No business value
Digging deeper still, the firm stated that there was no data strategy in place. Data governance business cases were seen as irrelevant, leading to a lack of investment and interest from the people that should be governing the data. The value of data governance was only seen by some senior stakeholders, which meant that progress was slow and held up by a lack of business engagement.
Align your data strategy against business strategic goals. This enables you to justify investments in data that directly support critical business objectives. Link the dollar values together to get executives interested.
Neil Burge, Cognopia CEO
Engaging the Business in Data Governance
Data governance is a business function. The challenge with this is that many business people here “data” and think “I.T.” Without their engagement, we cannot prioritise our work and ensure that it delivers real business value.
The first step to solving this problem is to build out a data strategy. Not a wish list of technology, but a strategic document that defines clearly what problem we are facing with data and what we are going to do to overcome it. Let’s have a look and see what our research found about this.
Who has a data strategy?
A data strategy can help your organisation leverage data as a competitive differentiator. Done right, you can increase corporate performance and help your executives achieve their goals. Unfortunately, many data strategies are created in the IT department with limited business engagement. This leads to “strategies” that are wish lists of technologies, or carbon copies of Gartner’s most recent data recommendations.
What we discovered
The good
11% of our respondents had a data strategy that was being actively used or directly tied to their strategic aims. These are the leaders.
The bad
Companies without a data strategy scored 0.53 points lower on our data maturity scale than those with a strategy in place.
Strategy is lacking
14% of our respondents had no data strategy and no plans to get one. Of these respondents, 50% had stated they believe “data to be a strategic enabler”.
- You cannot enable any other business strategy with data unless you have a strategy to do so – a data strategy.
- Whilst the latent potential in data is high, data will only ever be a cost unless you work out how to use it to generate value.
- Many data professionals struggle to gain access to or understand their enterprise’s strategic ambitions and goals.
Strategy is a work in progress
53% of our participants stated that they are in the process of drafting a data strategy now. 33% already have some sort of data strategy in place. This is good news. It shows that the firms have identified the gap between their aspirations and reality and are actively planning to bridge it.
- A good strategy identifies and diagnoses your core problem or objective.
- What is your business struggling with?
- What is it striving to achieve?
- What strengths do you have that can overcome this struggle?
- What weaknesses exist that must be acknowledged?
- Armed with your diagnosis, you can craft a set of overarching principles that guide your business forward.
- How should data be leveraged to solve your problems?
- This step provides the direction in the absence of discrete policies
- Finally, your data strategy should lay out a sequence of coordinated tasks and activities that will turn the strategy into reality.
- How will you execute against your goals?
- What order do you need to perform these tasks in?
- Do you need to change the way data is created, stored and used before it can help?
- Who will help you, and do they need to be trained before they can be of help?
READ MORE: Data Strategy FAQ – 28 Answers you need to succeed
How engaged are senior stakeholders in data governance?
Data governance is essential if you want to unlock the value of data. Unless you manage your data well, it will not return a value to you. It’s a bit like trying to sell your old car – if you drove it until the wheels fell off, don’t expect as much money back as if you took care of it like it was a child. Unfortunately, the words “Data Governance” seem to scare senior stakeholders away. The very people that are essential to the success of a data initiative are absent when it comes to making crucial decisions.
What we discovered
The good
Respondents with the most engaged senior stakeholders scored 3.43 – 0.82 points higher than our average score.
The bad
Those with the least engaged senior stakeholders scored 2.27, 0.34 points below our average score.
The difference between engaged and disinterested senior stakeholders
Engaged
Disinterested
Stakeholders see no value in data governance
54% of firms state that their senior stakeholders are either not interested or poorly engaged in data governance. When we look only at the companies that place the least value on their data, this figure rises to 73%
- When you fail to link the value of data to the initiatives in your business, senior stakeholders walk.
- Most companies we spoke with were pushing a “Data First” agenda, however, few had aligned their agenda with the focus on the executives they needed to support them.
- Unless your work directly supports a priority item for your senior stakeholders you will fail to gain their support and attention.
Want a copy of the full report?
No problem, click here to get your 51-page PDF copy, including our full analysis, another full case study, and the recommendations you need to succeed!
READ MORE: Cognopia 2021 Report Part 2 – Improving Data Maturity
