You’ve decided to implement master data management as part of a data governance initiative?
Most would agree that you’re embarking on a good journey, however, it is one where the technology and principles of good data governance need to be considered as secondary to the overall business objective.
As with many of these kinds of initiatives, it is easy to get bogged down in the minutiae of implementation when really you need to be considering factors like operational changes, the aspects of social and cultural acceptance and of course the cost of execution.
Few would argue the point that data governance is an additional cost to running the business so any meaningful investment pushes you to consider what the potential return of investment might be that you would have associated with such an initiative. To know the potential ROI you need to know what the value of your data is and what the cost is to your business of not running a programme or not having master data management.
Working out the value of the data is no mean feat. The best measures may not be as obvious and self-evident or even discoverable as you might like. Part of the reason for this, is that you may never have had to work out the value of your data ever before. Further, actually working out the value of something is typically work undertaken by accounting and in terms of your data, they may not regard the data as an asset at all. So, a big part of the activity around reorienting the business around being passionate about data governance and data management has to also be focused on education.
Interestingly, accountants may be able to help you with working out the value of the data, after all, part of their work is working out customer revenue and lifetime value. This definition of value should be part of the justifying rationale for your program. Ascribing value not only justifies budget but also serves as a motivator for endorsement and support and advocacy for adoption and support.
One of the most common reasons that Data governance and data management initiatives fail, is due to a lack of executive sponsorship or general acceptance by the business. The best way to get that sponsorship is to talk about the dollars and cents that support the initiative. Since it is likely to also be a recurrent technology cost for something like the Pretectum CMDM cloud solution, you’ll want to ensure that the initiative doesn’t simply get labelled as yet another potentially expensive IT project.
Defining functional requirements
Business stakeholders will have some inkling of what they expect a resilient program to provide in terms of data quality and insights. Your IT partners will also have some clear expectations regarding integration, reliability, security and scalability. A combined group of decision-makers and advocates will work collaboratively on the overall strategy.
Since no one will likely want to be associated with something with unclear deliverables and outcomes, the decision-makers will likely conceive of a number of Objectives and Key Results (OKR). IT will want a successful cutover, transition or implementation and all those dependent on the MDG will want clarity on their roles and responsibilities.
Any particular business may have its own distinct objectives and measures of success but here are some of the more common ones that you might want to consider:
- Define a matrix of roles and responsibilities for those who work with the data.
- Define why you want to or need to have more control over the data that you have, what are the goals that your initiative will attain.
- What’s the value to the business of achieving those goals, it could be less sales friction, better collections, better customer retention or customer service etc.
- Nominate individuals, roles or groups for overarching decision-making on rules and policy around data – remember everyone ‘owns’ the data.
- Clearly define the data that you want to put governance and control around.
- Assign subject matter experts within your organization to have data dispute, arbitration and decision-making capabilities for the data that you care about.
- Try to ascertain the baseline data quality of the data that you have nominated.
- Work out what the values and characteristics would be of that data for it to be optimal for your business (i.e. start defining business rules to measure the data against).
- Define and maintain a glossary of terms and alias for describing the data and the characteristics of the data.
- Determine a cadence for evaluation of the data you care about .
- Establish an escalation or triage and remediation process for issues identified on the evaluation cycle.
When you are evaluating solutions to help with the implementation of MDG and MDM consider whether you’re looking for broad functionality or tight specificity according to your project’s definitions.
If you already have a set of clearly defined buying criteria for your Customer Master Data Management needs then contact us and let’s see if we are aligned with your needs.