Tag: MDG

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The 4 key aspects of Customer Master Data Governance

Master Data Governance is a critical process that ensures the accuracy and consistency of the organization’s most important data assets.

Master Data Governance provides a framework for managing customer data across the organization and this is encompassed by four key aspects. policy, process, and controls and audit.

Master Data Governance is a process that ensures the accuracy and consistency of an organization's most important data assets. It includes audit, policy, process, and controls and provides a framework for managing data across the organization. By implementing Master Data Governance, organizations can improve data quality, increase data accuracy, and make better decisions. Customers also benefit from increased transparency and accountability.

Policy

Master Data Governance requires the organization to establish policies and procedures that govern the management of data across the organization. These policies and procedures ensure that data is accurate, consistent, and reliable. They provide guidance on how customer data should be created, updated, and managed.

The policy development process involves working with stakeholders across the organization to identify business requirements and develop policies that align with those requirements. Policies are typically developed for data creation, data maintenance, and data security and include aspects that relate to data quality.

Process

Once policies have been established, the next step is to apply the associated processes for ensuring alignment of work practices with those policies. The process development phase involves defining any necessary application workflows and gating activities for managing the change and creation of the data across the organization.

These workflows and procedures ensure that data is managed consistently across the organization and that data quality is maintained.

The process development phase also includes the development of data quality rules and metrics. These rules and metrics define the standards for data quality and provide a framework for measuring the effectiveness of the Master Data Governance process.

Controls

In Master Data Governance establishing controls to ensure that policies and procedures are being followed and that data quality is being maintained. Controls include both manual and automated checks and balances that help to ensure that data is accurate, consistent, and reliable.

Automated controls include data validation checks, data profiling, and data cleansing. Manual controls include data stewardship and data ownership roles and responsibilities.

Audit

Finally, Master Data Governance in Customer master data starts with an audit of an organization’s data assets to identify any gaps or issues that need to be addressed. The audit typically includes a review of the data sources, data quality, and data management processes. The objective of the audit is to identify any areas of risk and to develop a plan for mitigating those risks.

The audit process includes a review of existing policies and procedures to ensure they are aligned with business requirements. The audit team also reviews the data model to ensure that it accurately reflects the organization’s business processes and objectives.

Benefits of Master Data Governance for Customers

The end result of MDG implementation as a part of customer master data is improved data quality, increased data accuracy, and better decision-making. By ensuring that customer master data is accurate, consistent, and reliable, Master Data Governance helps organizations to make better decisions and drive business growth.

Customers ultimately also benefit from increased transparency and accountability. Master Data Governance provides the framework for managing data across the organization and ensures that data is managed consistently and in accordance with established policies and procedures.

Master Data Governance is a critical process that helps organizations to manage their most important data assets. The process includes establishing policy, applying process, and controls and providing audit to ensure that data is accurate, consistent, and reliable. By implementing Master Data Governance, organizations can improve data quality, increase data accuracy, and make better decisions. Customers also benefit from increased transparency and accountability. Master Data Governance is a vital component of any organization’s data management strategy and is essential for success in today’s fast-paced business environment.

To learn more about how Pretectum can support your business in the Customer Master Data Governance contact us.

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Objectives and Key Results (OKR) as a compass

So you’ve actually started governing and managing your data? You may even have implemented a master data management program. A fundamental question will be about the general health of your data governance and data management program. The best way to assess that is to look at your objectives and key results.

OKR’s are arguably a North Star for any data-related program. OKRs can be determinants of not just how your programs are running but also whether the measures that you have in place are proper and appropriate. Do they answer fundamental questions that you need to ask about your program and more particularly, your data?

The OKR’s themselves need to have a few ecosystem attributes in place in order to be effective though, among them, a regular cadence of assessment and reporting, friction-free change and calculation, and of course purpose. We’ll cover each of these off with a little more elaboration for you to consider.

Assessment and Reporting

Hopefully, you defined your OKRs at the start or midway through your project. Defining them at the start may even have been a prerequisite to getting executive sponsorship and investment.

Your first round of assessment of your initiative should start yielding results quickly, after-all you want to keep that momentum of support going. We think that a monthly checkpoint as a minimum is a good baseline interval.

Your communication plan should be polished and honed as you learn more and more about your data and your systems, but what you will also see, is that an evolving MDG and MDM program will elicit interest from more stakeholders and interested parties than you first thought.

Sometimes, just sometimes, an initiative like this will snowball into something much greater. So it is important to run those evaluations regularly and report on the key indicators and the trend.

You’ll want to report on a number of things but in keeping with most philosophies around Data Governance and Data Quality, you will report on data creation and change velocity where the data requests are coming from, and how well-aligned the requests and behaviors are with the overarching principles of your program. That means you will likely also want to report on business rule definition requests and changes too!

Friction-free change and calculation

OKRs are “living” things in the sense that they evolve over time. As the requirements of the business change, the OKRs change too. You’ll want to report with the most appropriate measures in place every time you call out progress.

For example, if you suddenly decide that keeping track of customer dates of birth is a key indicator of data quality because you use that to ensure that your data is compliant then you need to embark on a program to define, capture and maintain the date of birth and report on it!

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Setting date validation ranges

Your assessment cadence can also be most effectively achieved if the tools and methods that you use can be automated. This means that you should either rely on tools that allow you to do this against a schedule or you’ll need to script in ways to do the assessment that minimize the manual intervention that would be most commonplace when you set this up for the first time.

Data Quality assessment tools are aplenty and many of the MDG and MDM technologies on the market including Pretectum’s CMDM support in-depth data profiling and data quality assessment as fundamentals. Setting those up with key attributes as your OKR’s might be a good start.

OKR’s with purpose

We have to remember why we’re doing this in the first place. OKRs serve several purposes but the primary driver for defining and establishing is to create organizational alignment. The use of acronym FACTS is used to determine why so many companies use the OKR model. Benefits that are expected to flow are Focus, Alignment, Commitment, Tracking, and Stretching.

The MDG and MDM OKRs for data should also be visible to all company levels – because everyone is responsible for good data, everyone should have access to the OKRs. We’re defining these measurable goals related to the data and driving towards an examination of how they lead to specific outcomes for all areas of the business from marketing through sales, service, support and logistics, and potentially other groups too.

If you feel that you have happened upon or implemented some interesting, useful or distinctive OKRs around your master data management and in particular your customer master data management, we’d love to hear about them, and perhaps you could even contribute an article describing it, that we could post here with you as a guest writer.

Contact us to find out about the Pretectum CMDM.

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Defining success for Customer Master Data Management and Data Governance initiatives

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.