This is the final part of our data maturity model series, where we look at the need for change management in data governance. Simply put, unless you change the behaviours of your business, you will not succeed in data governance. You will not improve data maturity, as nobody will do anything differently than they do today.

I’ve lost count of the number of times money is thrown away on technology because the hard work of changing behaviours seems too hard. 

As a recap, here are the 5 levels of data maturity our report identifies:

  • 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.

Let’s see how we can change our own behaviour and use that to influence and improve our data maturity more broadly. By embedding change management in data governance we will ensure the success and longevity of our program.

TABLE OF CONTENTS

Chapter 1: The need for change management in data governance
Changing behaviours
How does HR support data governance?
What we discovered about training data teams
How does HR support Change Management in data governance?
What we discovered about HR and change management in data governance
Bring everyone along for the ride
Track change related KPIs and metrics!
What is measured is managed (and improved)
Chapter 2 – Case Study: A major Pan-Asian Private Healthcare Group
Case study background
Great expectations
Building the team
Building on a good start
Program not project
Where does Change come in?
Chapter 3: Cognopia’s 2021 data maturity report summary
Data maturity – All companies – 2021 – Repeatable

Part 1: Cognopia Data Maturity Model 2021 findings
Part 2: Improving Data Maturity
Want a copy of the full report?

The need for change management in data governance

Let’s start by considering typical data value chains in our business. First, data is captured (either by our staff or machines, or shared from a partner, or purchased from a third party). We then leverage this data in one of our critical business processes, before it is passed to a data consumer (which again, could be a person reading a report, a customer, or another system). 

 

Data value chain
Map the critical data flows in your business

Let’s assume that data in this value chain is poor quality. Where do we fix that? The typical process to achieve this is as follows:

  1. Talk to the data consumer to understand what they need to achieve their objective (what decision are they making, and what level of data quality is required to make that decision effectively?)
  2. We measure data quality during the critical data flow, and we fix any errors that we find 
  3. We trace the errors back to the source (Data Creator) and we work out why they are created in the first place, then fix those gaps

Changing behaviours

Point 3 requires us to change the behaviour of a person or system. In order to achieve that, change management is critical.

Sometimes the issue is actually with the data consumer – they expect something that is not achievable. Again, we need to change their mindset in order for us to succeed. 

Let’s look at what we found when we investigated how well this is being handled today.

How does HR support data governance?

We know that Technology is not the answer to governing data properly. The first step to achieving excellence is getting the people and processes right. HR departments play a crucial role in helping to find the right people, developing the right skills and experience, and in funding training initiatives to build up any missing capabilities in the organisation.

What we discovered about training data teams

The good: 39% of respondents fill roles with profiles from both business and IT, providing some training to learn the job.

The bad: Just 2% of respondents are bringing in external expertise to ensure their data governance program is leveraging “best practices”.

GET HELP: Affordable data governance training

19% of the respondents say that HR is “not relevant” to their data governance rollout. Unless you’re working in a firm with no department responsible for setting job scopes, arranging training, and defining KPIs, it’s hard to see how this could be the case. What is clear is that teams using just IT resources perform substantially worse than the top performers who use business and IT roles, provide policies and training, and bring in external expertise to ensure best practices are followed.

Here’s the data maturity if you bring in external expertise to help change behaviours:

And here’s the data maturity for firms that rely on their internal I.T. teams to do the job:

Invest in success

Those firms that just use IT roles score 2.42, 0.19 points further down our maturity scale than the average score of 2.61. The firms that are bringing in external expertise and actively training their staff score 3.17 on average, a whopping 0.75 points higher than their lower-performing peers.

GET HELP: Affordable data governance training

How does HR support Change Management in data governance?

One critical role for HR is in supporting change management. Data governance is an exercise in change management. For those that dislike this term, or think it’s fluffy, consider this:

  • Unless you change the way your staff capture, share, update and use their data you will never make any progress.
  • If you’re not willing to invest in making changes to behaviour, do not waste money investing in technology or in creating new processes; they will fail.

What we discovered about HR and change management in data governance

The good: 28% of respondents say their people understand the need for change and participate in change initiatives.

The bad: 12% of survey participants report their employees are reluctant to change and actively fight new initiatives.

Bring everyone along for the ride

58% of respondents employees will begrudgingly accept and tolerate change. This is not exactly a throat-roaring endorsement of the need to do things differently.

But who can blame them? Too many data teams demand behaviour changes, or worse, impose new data capture rules on teams without notice or training.

If you want to get people to change behaviour, you must create a plan to execute against this.

Track change related KPIs and metrics!

77% of respondents said that their firms do not track change related metrics and KPIs, or do so in a poor fashion. Consider that these people work with data. The teams we talk to are pushing for their businesses to become more data-driven, yet they fail to track metrics measuring how well their own program is changing data behaviours. The irony of this behaviour should not be lost. See the impact on maturity scores:

Track Change Related KPIs

What’s a Change KPI?

What is measured is managed (and improved)

Firms tracking change KPIs score 0.41 points higher than the average maturity and 0.87 points higher than those that are not tracking KPIs at all.

Of the 30% of respondents that had staff willing or enthusiastic to change behaviours around data, the average maturity score was 3.13 – 0.52 points above the average.

 

Changing behaviour is hard. It takes time. Your staff have a job to do, and your changes will tip people off-balance. Set and track KPIs to see how your program is working, and nudge people to change gradually.

Neil Burge, Cognopia CEO

Case Study: A major Pan-Asian Private Healthcare Group

1.81 – “Initial” Level

At 1.81, the Healthcare Group was classified at the Initial Level. The specific category score breakdown is listed below:

Case study background

This Healthcare Group had just started out on their journey to govern their data when we ran this analysis. The firm had appointed some new hires to lead a data Centre of Excellence, and Cognopia helped baseline their capabilities before they officially launched their data governance function.

Great expectations

Unsurprisingly, the new team had high expectations and aspirations for the value in their data. The long-term plan is to monetise data capabilities, hence treating data as an enterprise asset is going to be essential to unlock the value their data holds.

Building the team

As a newly-minted data governance team, the data governance organisation was not yet fully-fledged. Risk management policies were ad-hoc, as were data management capabilities. Any efforts to standardise data or manage data quality were run in pockets, isolated in individual business operating units or individual hospitals.

Building on a good start

Cognopia recommended developing a data strategy and data governance business case aligned to the overall strategic ambitions of the Group. This will ensure there’s strong business alignment and hard financial benefits for achieving the goals in the data CoE. Longer-term their plan is to build a formal data governance framework and roll out specific, measurable and pragmatic data improvement actions across their most important data use-cases.

Program not project

The road ahead is a long one, as their roadmap lasts over the next 5 years. By beginning with the right end in mind, this group has set themselves up to succeed and avoided common pitfalls their peers are experiencing.

Where does Change come in?

These guys got in touch with us at exactly the right time. By engaging before there was any real project kickoff we were able to embed change from the outset. KPIs could be established that tied the real business value to the work of improving data, which means every stakeholder understands what’s in it for them.

Cognopia’s 2021 data maturity report summary

This research has been compiled by myself and my colleague Cliff Wong over the past year. We have spent many enjoyable hours talking with data professionals, walking through the findings and their specific responses, and bringing new ideas to the table to try and help. We thank each and every participant for their responses and their time, and we wish everyone the best in their own data journey.

Data maturity – All companies – 2021 – Repeatable

With the participants we talked to, the average data maturity was 2.61. This is our “Repeatable” level, and it shows that progress is being made. Some of the biggest challenges identified in this research are:

  1. Data initiatives are being kicked off without detailed planning, requirements and business cases. For long-term success, this must be changed. It’s a cliche, but “Failing to plan is planning to fail”, and many firms walk blindly into this trap.
  2. Executive level engagement in data governance matters. There’s no way around it. If your senior executives pay lip service to the data initiatives, so will every other employee. Data practitioners must learn how to talk to executives and excite them about this mission. We need to place a dollar value on this work, otherwise, it will continue to be underfunded and deliver limited value.
  3. Data strategies are poorly understood. Many firms think a data strategy is a shopping list for tools and technology. This could not be further from the truth. As a community, we need to build dynamic, ambitious yet feasible plans to use data to achieve major business goals. If we do that and execute violently against these plans, we will drive up the interest and engagement in data activities across our organisations.
  4. Technology has not hit prime-time… yet. I was pleasantly surprised to see that the overall technology scores for our respondents are beneath the average maturity score. Companies may be spending their budgets more wisely. It is essential to get the people and process side of managing data working before you throw money away on a tool from Gartner’s Magic Quadrant.
  5. We need to invest more heavily in people. HR departments are absent from this data conversation, and this needs to change. Start by appointing the right people to the right roles. I’ve talked with countless “Heads of Data Governance” that have never governed data before they began. They’re then dumped into the role with no training, no support, and no budget. Is there any surprise when they deliver no results?
  6. Track change KPIs, or set new KPIs for the people running the data program. As a community we wax lyrical about the need to use data, so let’s start using some ourselves! Prove that the work we do matters. No one will do that for us.

I hope you enjoyed reading our report as much as we enjoyed putting it together. I would greatly appreciate anyone that takes time to complete our maturity assessment so we can compile another report in 2022. Click here if you’re interested in helping. You’ll get a nice maturity report for your time!

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!

MISSED PART 1? Read it here: Cognopia Data Maturity Model findings 2021

MISSED PART 2? Read it here: Improving data maturity