Customer Master Data Management is strategic

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Putting a price on data is a tough task to perform if your business is not “in the business” of selling data. The real question though, is does your organization recognize its arguably most valuable data asset – customer master data, as a valuable resource? The true value of that data more than likely hides in how you use it.

Customer data holds enormous economic potential if managed well and appropriately leveraged. The challenge though is that many companies struggle with proper handling and syndication of their data. Further, they don’t think about their customer data strategically. They don’t take what they have and convert the data that is in hand, into actionable information. Worse, they may not be managing their customer data in any meaningful way, they may have data all over the place in systems, spreadsheets and even email.

By implementing some data management best practices around customer data, these same businesses could be thinking more strategically especially if they are ensuring that their data is accurate and aligned with their business objectives.

The practice of data governance has many facets. Master Data Management is just one of those and certainly, your master data management practice doesn’t have to cover all of these, particularly if the maturity of a given organization is not appropriate to an over-investment in policies and procedures, but there are some basics that you could and should consider.

Data governance includes all of the activities relating to the planning, implementation, development, and control of the information generated by an organization.

In other words, data governance is the development and realization of all the aspects that you might consider for data definition, creation, collection, storage, access, quality, change management and data distribution. Data governance also covers both the master and transactional data.

Pretectum sees data governance and the broader discipline of data management as covering many areas including data security, sharing, integration, architecture, MDM (Master Data Management), RDM (Reference Data Management), Insights and Intelligence, change management, storage, retention and retrieval

The best data is the data that is available and usable at exactly the right place and time, in the right format. While that is easily said, achieving this goal is a little more challenging depending on the maturity of your business practices, technology, the characteristics of the data itself and the nature of your industry.

There are key critical strategies and best practices that are generally accepted for improving the way companies manage their data, consider these aspects.

Forget the Rule of Thumb

We use the term frequently, and many businesses actually run their data governance program, if they have one, using the “Rule of Thumb”, but this approach is downright wrong.

At best, “Rule of thumb” is what you might consider an approximate method for data governance, sure, it is based on practical experience, but it is tribal in nature and really only as good as the individuals and the experience that they have with the maintenance of the program.

Having its roots in seventeenth-century trades where weights and measures were almost non-existent and certainly not measured and prescribed, quantities were measured by comparison to the width or length of some sort of loosely defined measure. A thumb, for example often being used as the baseline. These rough swags are fine when you don’t need more control and precision but unfortunately with the growing interest in compliance and meeting regulatory policy, managing data in a loose way is no longer acceptable.

Apart from any business rules you might have in place, there may be a raft of additional rules that are imposed by regulators specifically because of the nature of the data.

Having a robust approach to data governance provides some assurance that data meets the needs of the business and the legal obligations of the environment in which the business operates.

Data governance programs have several hurdles to overcome in order to be part of the DNA of the way the business views and deals with its data. There are cultural challenges, organizational politics and the simple mechanics of basic data control and management.

The technology aspects may be the least of your concerns if you have the right expectations defined in terms of requirements and the right level of complexity and flexibility in a given selected vendor.

The Pretectum CMDM affords you a number of different ways to meet your organizational objectives for Customer Master Data Management as part of a larger data governance program. Reach out today to learn more.

OKR’s for MDM and MDG

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Objectives and Key Results ( OKR ) are how you measure the effectiveness of the pursuit you have of some organization goal.

Master Data Management ( MDM ) is the umbrella term used to describe the people, processes, and technology that your business uses for the collection and management of master data, including the definition of schemas, business rules, sources, and targets. In our case, we’re most particularly interested in the customer master. What, when, and where.

Master Data Governance ( MDG ) is another umbrella term that is often used to describe the technology, people, and processes that revolve around the handling, onboarding, syndication, and control of the master data itself. This process is also considerate of MDM but is more concerned with the process aspects. Who, when, and how. We think of MDG as a subset of MDM focused principally on coordinated handling and control.

Standardization of the way data is created and the key attributes of the data object are key to ensuring that the master data that you have for customers, in particular, is correct, proper and aligned with your defined business objectives.

Pretectum sees the collection process as necessarily a part of the responsibility of any MDM. MDG’s role in this is ensuring that the right players and participants are involved and that they cannot engage in tasks that are out of alignment with the rules of the system. MDM and MDG don’t have to be separate systems, though sometimes they are. In the context of Pretectum’s CMDM you get MDM and lightweight MDG as a single unified capability.

Rules for governance and management

At the highest level, this means that the customer master makes use of a common vocabulary across all business units in relation to how the customer master is described. This also means that all sources and targets in relation to that data use a common set of descriptors also. This is really only possible if you make use of a unified repository that is leveraged to describe that data. Consider what kind of vocabulary items you need for your business, who defines them, who maintains them and how they are communicated and leveraged. Vocabulary objects could be part of your OKRs.

The second set of characteristics of a standardized approach to data collection and distribution (Data syndication in Pretectum speak) is full data lifecycle management, which basically means the delivery of a degree of understanding on the lineage of data in relation to sources, but also a full auditable history of events related not only to the data itself but also the metadata that constitutes the descriptions in that vocabulary. Consider the key results that you might want to leverage to assess MDM standardization practices in your organization. Proof of sustained and consistent standardization might serve as a great OKR.

The third aspect is one in which you have a clear definition of all the entities involved in the data lifecycle management. This includes, systems and people but also describes their roles. responsibilities and data ownership, stewardship, and curatorship roles. This needs to be formally described in order to assist in decision-making and triage of issues. Assessing consistency in your people, object, and organization definitions is a commonly measured attribute and one that is often considered a good DMO (Data Management Organization) OKR.

Why do we do all this?

The answer quite simply is that if you don’t have these three essential traits in your customer data management approach then your business may be flying blind with heaps of customer master data that are not being appropriately, or cannot even be used, to maximum effect for the business.

These are the ideals though, and at the same time, you need to look to the relative customer master data management maturity that your organization has. Sometimes, the technology is really not going to help and an overbearing level of process management and control will actually impair the effectiveness of your data management efforts.

Your approach, therefore, needs to be tempered by something practical and pragmatic, something that recognizes that data management is often a journey. In the end, your business’ faith and confidence in its customer data, should be influential and informative but at the same time, your business needs to have greater ambition for how and what customer data is collected and how it is leveraged.

Contact us to learn more about how you can consider Pretectum’s C-MDM to elevate your business OKR’s around MDM and MDG.