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If you want to use your data to make business decisions, you need to be able to trust it. Using a framework like DCAM is one of the best ways to achieve that. This article discusses the different aspects to consider, the traps to avoid and the benefits of doing it right.
Are you a Data Leader with a data landscape you don’t fully understand? Would you like to start or accelerate your data program, so colleagues can use your organisation’s data without worrying about whether it’s inaccurate or out-of-date or incorrectly sourced?
This article lays out the path to adopting a Data Management framework that drives business trust in data, to support decision-making that optimises revenue while minimising risk.
It may sound counter-intuitive, but in order to understand your current data landscape, you need to be clear about what you’re trying to achieve: your Target State.
And the easiest way to do that is to adopt an industry-standard Data Management framework, which sets out all the elements you need to master in order to manage your data well.
Do not be tempted to create your own framework!
If you’re new to Data Management, it can be difficult to be sure you’ve thought of all the different things you should be working on. And then which of those are the foundational elements that you should tackle first and which are more for later down the line. A framework gives you peace of mind that you’re doing the right things in the right order.
If you are fortunate enough to have surrounded yourself with really smart data people, you have undoubtedly encountered times when they have argued about the best approach to adopt. A framework gives all of your expert practitioners a common structure with which to align their experiences.
For both expert practitioners and new data team members, it’s important that they all understand and share a vision of the Target State (which is described by the framework). You must have everyone co-ordinated and driving for the same outcome.
Another expected benefit of adopting a Data Management framework is that you’ll be able to measure your current capability. Once this baseline level has been established, you can periodically remeasure it, to track your build to target. By formally measuring and monitoring improvement, you can deliver transparency and accountability to your stakeholders on the capability build-out.
By adopting a proven framework, you will accelerate your program and spare your organisation endless debate and effort.
By formally measuring and tracking improvement, you can deliver transparency and accountability to your stakeholders on the capability build-out
When selecting a Data Management Framework, look for four things – they are all important to your success:
1. Target State Vision
So you can be clear about the capabilities you need to build, in order to control your data and achieve trust in that data.
2. Training
So your team can be aligned with a common understanding of terminology and purpose.
3. Assessment
So you can measure your current capabilities, understand where your blind spots are, and drive your priorities.
4. Industry Benchmark
So you can understand how your business compares with others in your industry.
This is a situation where formality is important.
The best case scenario is that the organisation’s board endorses the approved Data Management capability framework, and even leverages the improvement of capability measurement scores as part of the compensation consideration of key executives – obviously the Chief Data Officer, but beyond that the Chief Operating Officer and the Chief Executive Officer. Better yet, the full C-suite. Every one of the Cs has accountability for the data that their function creates or uses. This compensation consideration can go deeper, to your data practitioners and even every finger on the keyboard. Everyone is critical to trusted data.
That best case is rare today, but where it has been followed, it has had tremendous success.
Without the board mandate, the Senior Data Executive must be the heroic evangelist in the organisation – promoting the value of the Target State Data Management framework and how it is used – to build trust in the data to achieve the highest priority business objectives.
There are a few different Data Management maturity models and frameworks out there. They all have strengths and weaknesses but we find DCAM particularly compelling because it has the 4 critical aspects listed above:
1. Target State Vision: the framework is the product of a global community of Data Management practitioners, who know what works (and what doesn’t!)
2. Training: accredited training is provided through a network of global certified trainers
3. Assessment: DCAM is designed as an assessment tool for measuring the capabilities defined in the framework
4. Industry Benchmark: every couple of years, the EDM Council holds a broad industry survey based on the DCAM framework to assess the average capability score
Which one will you choose?
Let’s look at how to establish meaningful Data Management goals. Ask yourself:
Meaningful answers to these questions begin with defining your goals. Ideally, you want these goals to be stated in a manner that aligns with the framework we discussed in Step 1.
Begin by listing out your business objectives and then do some strategic thinking about how your Data Management capabilities can support the achievement of those objectives. Wherever there’s a gap (i.e. when you know that a specific capability is needed to fulfill the business objective but you haven’t developed that capability to an adequate level), these become your goals. Objectively state them as Objectives and Key Results (OKRs) or Key Performance Indicators (KPIs) so that you can easily assess and track your progress towards meeting those goals.
Data Management for the sake of it is just one more overhead and is likely to be devalued. To avoid this, ask yourself how you need to manage the data to achieve the identified business objectives. Don’t lose sight of the fact that Data Management is not an arbitrary overhead – it’s the foundation for successfully running the business. You justify your existence by making sure the business can trust their data. When you pick up the phone on your desk, you expect to hear a dial tone. When you extract data for a report, you should have a similarly common expectation that it will be accurate, timely, complete, and available in an easily consumable way.
Data Management is NOT an arbitrary overhead – it’s the foundation for successfully running the business
Think in both the short-term and the long-term. You need long-term goals to build toward the establishment of your Target State. Still, you also need short-term goals to continuously deliver tangible value in achieving the organisation’s business objectives. If you focus only on the long-term big picture, you’ll lose the support of your business long before you can achieve your Target State. By contrast, when you take steps to ensure that your goals link directly to your current business objectives, you’ll consistently demonstrate the value of Data Management to your business partners. When they can trust the data, they will trust Data Management.
Make sure that your short-term and long-term goals are compatible. If you start taking shortcuts, you begin to compromise your ability to achieve your long-term goals – sustainable and scalable Data Management that can deliver against all the business objectives across the organisation.
Finally, be sure to align your goals with your Data Management framework. Always look at your goals through the lens of what Data Management capabilities must be established to fulfill each business goal – whether short-term or long-term. Be sure to build out those capabilities to facilitate the achievement of the short-term goals without compromising the achievement of the Target State. And continue to refer back to your established framework, leveraging that structure to measure and assess your progress.
Now we can consider how to use a Data Management framework to assess your current state and measure your progress as you move towards your Target State.
In Step 1, we mentioned the importance of having an assessment model aligned with your Data Management framework. Such an alignment is important because it allows you to assess the various capabilities that specifically define your framework. By adopting a methodology that addresses both of these functions (framework and assessment model), you can:
Let’s pause briefly and consider how Data Management Capability differs from Data Management Maturity:
You may have business reasons for operationalising incrementally, and so maturity is not always a meaningful measure of how quickly you can establish your Target State. When you measure capability, you are assessing your abilities rather than your accomplishments. In short, sustainable and scalable capability sets you up to achieve effective and efficient maturity.
When you measure capability, you are assessing your abilities rather than your accomplishments
Don’t rush into your assessment. A design phase is important because numerous decisions must be made about the structural aspects of the assessment:
1. At what level of detail will you assess?
For example, will you assess your Data Architecture capability overall? Will you break out broad capabilities of the Data Architecture function (such as defining metamodels, and establishing identification and classification criteria)? Or will you separately evaluate your ability to define data domains, establish criteria for defining business data elements, build-out taxonomies, and so forth?
2. Who will participate in the assessment?
Will you ask a handful of the program designers? Will you involve the “front-line” practitioners? What about the Data Governance organisation? Will you only speak to people with overall Enterprise experience, or will you drill down to individual Business Units?
3. How will you engage with your assessment participants?
Will you issue a survey? Will you interview people? Will you interview groups of people? Will you provide any training to the participants to ensure that they understand the methodology?
4. Will you ask for evidence?
Will you trust their answers, or do you want them to substantiate their scores with specific evidence? For example, if they tell you they have a process for doing X, can they show you the documentation and outcomes of that process?
There are several other decisions along these lines which must be made before you can begin an assessment. Several distinct factors, ranging from budget to intended assessment targets can influence your decisions, and that’s why it’s important to engage with assessors who are experienced with the assessment model you’re using. This experience helps ensure that:
We will examine that final point in more detail in the next Step: what the results mean, how you can build a gap analysis around them and how you can leverage an effective long-term plan for improving your scores. The score uplift is not for the sake of better numbers but for improving your Data Management capabilities and improving the business’s trust in your data.
If you chose the DCAM framework in Step 1, we have great news: you can use it as an assessment tool as well.
There are a few different nuances to consider when structuring an assessment, but you can start simple and refine over time:
Year 1: Establish the baseline for the enterprise-level capability
Year 2: Evidence capability via artifacts and target specific levels of performance
Year 3: Introduce line of business assessments to enable comparison across the organisation
In Step 3, we talked about running your data capability assessment. We now focus on what to do with the results and findings of your assessment.
The key point is that you have a quantified baseline of your level of data capability, as measured in your assessment. You can now move away from the anecdotes and focus on the facts. You’ve probably experienced different perspectives on how well your organisation does everything, not just data. We all hear so many anecdotes and opinions, that sometimes we don’t know the facts. Having your quantified baseline allows you to be precise and show people the facts your assessment has surfaced. As a Data Leader, it allows you to be truly accountable and empowered to improve your organisation’s data capability and measure the improvement.
You must now do two things: share the result and set the targets.
First, you must share the baseline assessment result with your data stakeholders to understand your current capability. These stakeholders are likely to have contributed to your data capability assessment, so you are obligated to update them on the result. Apart from the assessment participants, you should also share this with any other data-related stakeholders who have an interest in how you improve your data, including upwards to executive and board level.
Tell the stakeholders what the result is, and that it is a baseline upon which to build. Communicate that you are starting a journey to more formally assess and improve your data capability. Also, emphasise that you will establish strategic, scalable data capability that will meet your organisation’s needs now and in the future and, importantly, be more valuable to your organisation. Remember to refer back to the business and data goals you set, as these need to be achieved as your data capability improves.
After you’ve shared the result, you must agree on what should be done next. In the next Step, we drill into building your data capability uplift roadmap but, before we do that, we need to frame this roadmap with high-level capability targets. The roadmap can then define the detailed steps of how to deliver the capability to meet these targets. You need to set capability targets that you can achieve incrementally (e.g. every six months), over a relevant period of time that meets the needs of your organisation, let’s say this time period is three years.
To set your capability targets over these three years, you need to review your detailed assessment results and predict how these will improve over time. You can then aggregate these detailed predictions to provide your capability targets. Setting targets is an art rather than a science, as no-one can really predict the future, but you can build a prediction (we’ve done it!) of how your capability will improve and set targets against this. Be pragmatic and carefully review the targets you set to ensure the capability delivered will meet your organisation’s needs. Remember that one organisation may focus its attention on capabilities that are less important to another. For example, data ethics may be a major focus in a business-to-consumer (B2C) organisation but less important in a business-to-business (B2B) organisation.
We strongly advise you to show your workings on how you defined your targets. You will need to share these targets with your data stakeholders, and, rightly, they will scrutinise you on them. Be prepared to explain how you defined them and incorporate their feedback as necessary. Remember that you are the Data Leader who is accountable for hitting these targets, so it is ultimately your decision.
You now have your baseline, which you’ve shared with your data stakeholders, and you’ve carefully defined targets over a time period that is relevant to your organisation.
If you chose the DCAM framework in Step 1, your initial DCAM baseline score might be in the ones or twos out of six, which means your level of (aggregate) data capability is not initiated or conceptual (i.e. you recognise the need for the capability, but you’ve not yet started developing it).
This low score assumes that you are starting out on your Data Management journey, but you still need to set targets for improvement even if your baseline score is high. If your score is at the top of the DCAM six-level scoring scale, then well done; you don’t need any improvement (but actually, we’ve never seen an aggregate score of six and therefore, we don’t believe you!).
The value of using a framework such as DCAM is that it allows for the comparison of scores to the benchmark scores published by the EDM Council. The structure of the framework has a natural progression which helps to structure the order of the priorities, but the business needs should also drive what is most important in your Data Management program.
In Step 4, we talked about setting your targets. We now focus on the data capability uplift roadmap that allows you to execute to achieve these targets.
A data capability uplift roadmap is a list of all the data capabilities – as per your chosen framework such as DCAM – sequenced to deliver incremental capability when you need it delivered. This roadmap aligns with your organisation structure so that your organisational units can benefit by using the delivered capability.
Let’s look at an example.
Big Bank Inc has hired a Chief Data Officer (CDO) named Sarah, and Sarah has been told that she is accountable for improving data over the next three years. The bankers complain that data is not fit-for-purpose, and they don’t even know what data they have or where it all resides. Sarah now needs to work out how to solve these problems and using a Data Management framework to assess her capabilities is a great way to do this.
First, she selects a suitable framework, such as DCAM, then runs a baseline capability assessment, as detailed in the Steps above. She gets the results and then defines targets for improvement over the next three years. The assessment and targets are needed, but they don’t allow Sarah to build or improve their capability to reach those targets. Hence, she needs to define the roadmap with the right level of executable detail to achieve the targets.
Big Bank Inc has two business lines, Retail and Commercial, plus Operations, Finance and Risk support functions. Sarah builds the roadmap so that her central data team can build and implement capability and then get it used by the business lines and support functions. Let’s say the business strategy prioritises the Retail and Commercial business lines, so her roadmap ensures that Retail and Commercial are supported to use the capability and data is improved in these two areas first. Every step delivered in her roadmap will be improving the data capability assessment score towards the defined targets.
Your data capability uplift roadmap can be complex, especially in larger organisations with multiple business lines, regions, etc. You should use a proper planning tool to define and manage your roadmap, whether an old-school waterfall project planning tool or more agile Kanban-type tools. We expect spreadsheets will be involved somewhere, but Excel is not a planning tool, and should not be used for complex roadmap management.
Ensure you align your key milestones to your targets and periodic capability assessments. This sounds obvious, but you must show incremental progress to keep leadership on your side and provide value to your organisation. If you deliver a key milestone the week after a target or assessment date, no one will know about it, and you may lose the recognition for achieving key data milestones.
When we discussed targets in Step 4, we emphasised that, as a Data Leader, you are accountable for the targets, and you don’t need consensus from all your stakeholders. On your roadmap, you must have stakeholder buy-in where that stakeholder is delivering something in your roadmap. For example, your peer in the Retail business line must agree to your roadmap and take accountability for delivering their milestones. You can’t impose this upon them and expect them to deliver as you may not achieve your targets, and you risk failing against your accountabilities.
Finally, ensure your board and executives have a high-level understanding of the data capability uplift roadmap and how it aligns to the data and business strategy. This understanding is critical to highlighting valuable outcomes in your business lines (e.g. Retail and Commercial in the above example). They need to feel like you have this under control, so share the appropriate level of information with them.
You now have your roadmap, which you’ve shared and agreed with your data stakeholders.
In Step 5, we talked about the data capability uplift roadmap that allows you to execute to achieve your targets. As a Data Leader, how do you deliver the required near-term results that excite your business leaders while also building sustainable and scalable Data Management capability?
We now focus on executing the roadmap and achieving improved capability while diligently delivering against the business objectives driving the need for Data Management.
To manage execution, you must ensure you have strong program management skills on your team and celebrate the achievement of outcomes, not activity.
The capability uplift roadmap defines your journey and sets your milestones. These milestones must be linked back to the business objectives and Data Management objectives. Underneath your roadmap is project plans with resource requirements from all stakeholders. Use your governance structure to provide authority, obtain support from all required stakeholders, and hold the stakeholders accountable for your mutual success.
In DCAM, the second of the eight components is the Data Management Program. The component is the set of capabilities required to organise, fund, and manage the successful execution of the Data Management function. Within these capabilities is the creation of the program management office (PMO) activities. There is an acknowledgment of the wide range of business, data and technical stakeholders required to manage data.
These PMO capabilities are critical to the success of your Data Management function and achieving the roadmap milestones and the objectives linked to the milestones.
There are two sets of outcomes that you must deliver: data outcomes and Data Management outcomes
There are two sets of outcomes that you must deliver: data outcomes and Data Management outcomes.
As mentioned in Step 1, exciting your business comes from delivering data the business can trust to make decisions that optimise revenue while minimising risk – what they need to achieve their business objectives. These are the data outcomes. The data and how they use it to achieve their business objectives matter to the business.
The Data Management outcomes are needed to ensure that data is trusted by the business and meets their business needs. However, these outcomes are only meaningful to the data practitioners.
Both sets of outcomes should be planned, monitored, and celebrated, but the audience for each is different when achieved.
A formal communication plan around the roadmap is required. As just stated, you must know your audience and tailor your message for each audience.
Drive to achieve a strong data culture one success story at a time.
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