{"id":2900,"date":"2019-11-06T23:43:13","date_gmt":"2019-11-07T04:43:13","guid":{"rendered":"http:\/\/www.dcamportal.org\/?p=2900"},"modified":"2020-03-12T16:07:17","modified_gmt":"2020-03-12T20:07:17","slug":"2017-current-state-research-on-the-designation-and-management-of-critical-data-elements","status":"publish","type":"post","link":"https:\/\/staging.dcamportal.org\/fr_ca\/2017-current-state-research-on-the-designation-and-management-of-critical-data-elements\/","title":{"rendered":"[2017] Current State Research on the Designation and Management of Critical Data Elements"},"content":{"rendered":"

Critical Data Elements Interim Report<\/h1>\n\n\n\n

Best Practice Scribes<\/strong><\/p>\n\n\n\n

Mark <\/a>McQueen<\/a>, EDM Council, Senior Advisor-DCAM
Michael Atkin, EDM Council, Strategic Advisor <\/p>\n\n\n\n\n\n\n

The issues surrounding the designation and management of critical data elements ranked as the number one priority in the 2017 benchmarking study<\/a> conducted by the EDM Council. Because of its importance, we formed a working group<\/span> of interested members. We conducted 14 in-depth interviews with member firms to frame the issue. We organized our basic research to define the challenges financial institutions are facing with definition, lineage traceability, governance, and quality management. This is the interim report. Members are invited to join the working group<\/span> as we move this activity from framework to recommendations about best practice.<\/p>\n\n\n\n

\n\n\n\n

What is a Critical Data Element<\/h2>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

Criticality is determined from the perspective of the data consumer<\/span>. The highest priority candidates are those that have an impact on a regulatory report, financial calculation, performance indicator or risk measure. One of the biggest challenges is the lack of consistency<\/span> in distinguishing a granular data<\/span> attribute from a derived or calculated business measure. Many firms are using the same nomenclature to describe logical concepts, business objectives, calculation processes, derived elements, and physical expression. The concepts described above are all real and important “things” – but they are not the same thing – and by calling them all “critical data elements” we add confusion to an already difficult task.<\/p>\n\n\n\n

How are CDEs Designated<\/h2>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

Defining criticality is dependent on understanding the business processes associated with how the data is used in the real world. Many organizations start with a “candidate CDE<\/span>” that originates from the data requirements capture and verification process<\/span>. This process<\/span> is frequently the result of an internal “data forensics” and “negotiation” mechanism between the data consumer<\/span> and the data producer<\/span>. The forensics (or discovery process<\/span>) is driven by the knowledge of subject matter experts\/business analysts. It is dependent on an analysis of data flow<\/span>. Tracing the flow of data is relatively straightforward to envision but compounded by the challenges of fragmentation with multiple repositories, incomplete inventories, hard to obtain business knowledge and the plethora of end user spreadsheets\/local data repositories. Organizations are using their formal governance structures to approve the designation of criticality and to reconcile gaps in understanding about granular data<\/span> inputs versus business process<\/span> outputs.<\/p>\n\n\n\n

CDE Implications<\/h2>\n\n\n\n

The designation of a data element<\/span> as critical means it is governed by organizational policy<\/span> and standards. This translates into increased data management rigor being applied to the attribute. The following were common themes across the universe of firms involved in the initial discussions:<\/p>\n\n\n\n

  • Definition<\/strong>: Designation as a CDE<\/span> reinforces the importance of alignment to precise meaning for all front-to-back applications, all business processes and all derived formulas that consume the attribute. The CDE<\/span> must be understood in context of how it is used. Locking down meaning, unraveling front-to-back nuance, managing misuse of data elements and preventing users from changing meaning to fit the requirements of applications remain the top challenges to address.<\/li><\/ul>\n\n\n\n
    • Lineage<\/strong>: Unraveling technical and business lineage requires significant effort. The forensics of data flow<\/span>, rationalizing across IT systems and unwinding transformations are accomplished based on interviews between producers, consumers and “armies of consultants” that all need to collaborate to stitch the baseline together. Not only is lineage complex, it can be inhibited by the lack of standard<\/span> approaches, confusion on terms\/meaning, questions about value and incomplete levels of expertise. The translation of the “tangled web” of business processes and outputs into data precision is not always obvious (particularly for legacy products). Lineage is compounded because it is done at a specific “point-in-time” and not aligned with the realities of dynamic and interconnected business\/IT environments. Financial institutions are using various starting points (i.e. unravel reports, trace from logical model<\/span>, reverse engineer technical systems) to align data with precision.<\/li><\/ul>\n\n\n\n