Creating a Common Language - The Data Management Capability Model
One of the greatest challenges to any data initiative is establishing a common understanding of the data management context across the stakeholder community - it is essential if you are to share a mutual interpretation of scope, requirements and outcomes. However, ’data’ and ‘data management’ are notoriously hard to define and can mean different things to different people. Invariably, this is less about proving that any one definition is right or wrong - it’s more often a case of ensuring that stakeholders are all on the same page, all using a common language.
Let’s consider an example. In it’s simplest interpretation, Data Quality may be understood to refer to the ad-hoc manual analysis and cleansing of data content held in end-user repositories and spreadsheets. In a more complex interpretation, Data Quality may be understood to incorporate multiple disciplines from requirements capture, rule definition and measurement to assurance, issues management and remediation. The scope of data may be qualified by a definition of business purpose and data risk appetite, certain disciplines may be automated and assurance processes may involve multiple individuals across the organisation with accountabilities and responsibilities defined under a data ownership model. Both the simple and complex definitions are valid but are very different in terms of their delivery scope, requirements and outcomes.
The broader the data initiative, the more diverse the stakeholder community and therefore the greater potential for misunderstanding and conflict. That is particularly relevant when implementing enterprise strategic data initiatives that may involve many, if not all, business units, departments and/or functions. The Data Management Capability Model is a tool that can be used to define a common language for the data management function.
The Data Management Capability Model represents a high level definition of the data management capabilities, functions and disciplines as well as the structure that defines how these functions and disciplines relate to one another. It uses terminology that is understood equally by the business and data practitioners. It will therefore include definitions of key functions such as Data Governance, Data Protection & Ethics, Data Security, Data Architecture, Data Operations, Data Engineering, Data Insights, Data Culture & Literacy as well as disciplines such as data strategy, assurance and performance measurement.
The Data Management Capability Model establishes a common language for:
Functional scope to ensure consistent interpretation and execution of strategic demand
Roles, accountabilities and responsibilities in order to build out data-related job families
Collective derivation, adoption and embedding of policies, frameworks and standards
Common measures of success and performance as well as common practices for maturity assessment and benchmarking
Functional requirements for tools and services
Can I not use one of the industry standard models for this purpose, such as DAMA-DMBOK, EDM Council DCAM, TOGAF or ODI Data Skills Framework? Certainly, that’s a great place to start. Each of the industry standard models has its own strengths and weaknesses. If any one of these models proves to be the perfect fit for your business, then you should adopt it. However, by their very nature, industry standard models tend to be generic, adopt the data practitioners perspective and invariably miss specific business context. Whatever Data Management Capability Model you adopt, it should be fit for purpose and suitable for adoption by the entire stakeholder community.
Quaylogic has proven experience in implementing enterprise data strategic initiatives and can provide you with the right advice to ensure that your organisation is equipped with the right Data Management Capability Model to secure stakeholder engagement and delivery success.