This is a sixth blog entry in a blog series highlighting the critical nature and the importance of executive sponsorship for data governance initiatives. In last few entries, I explored need to understand the KPIs, goals behind those KPIs and necessity to get your hands on actual artifacts used by executives in reviewing these KPIs and their goals.
My approach has been very simple and straightforward: data governance initiatives need to absolutely be able to demonstrate impact on top and bottom lines by helping executives improve on the KPIs which are used as means to achieve higher profitability, lower costs and compliance. The process of garnering executive sponsorship is a continuous one. Visibility of data governance organization, its impact across the board; helps in establishing awareness and understanding of how data governance initiatives help organizations. This visibility and awareness makes it easy to maintain ongoing executive sponsorship.
Once you, as a data governance team, have clearly understood KPIs, goals behind those KPIs and have access to the artifacts used by executives, it is time to go back to the technical details. At this stage it is extremely important to map which systems, business processes automated by those systems and data is either directly or indirectly responsible for the outcome of those KPIs. This process of mapping dependency between KPIs, systems, business processes and data can be somewhat automated using metadata management repositories. It is important to capture this information using tools and technologies so that this information can be readily available and shared with other teams and systems. Technology solution will also facilitate change management, impact analysis in future. The lineage and the metadata I am talking about here, go beyond technical metadata and gets into the realm of business (process and semantic) metadata as well.
This dependency information will come in very handy in establishing scope, definition of the efforts being planned towards specific data governance initiative/project. When collecting information about the systems, business processes automated by those systems and data, it is important to capture relevant information with long-term, repeatable perspective. Information such as:
1. System name and information,,
2. Landscape information (where is it being installed/managed/housed, which hardware/software are being used? touch points with other systems etc.)
3. Ownership and responsibility information from both business and technology perspective. (Which technology teams are responsible for managing, changing and maintaining these systems? Who are the business stake holders who approve any changes to the behavior of these systems? etc.)
4. Change management processes and procedures concerning the systems and data.
5. End-users/consumer information (who uses it? How do they use it? When do they use it? For what do they use it? In).
6. Any life cycle management processes and procedures (for data, systems) which might be into existence currently.
7. Specific business processes and functions which are being automated by the systems?
Many a times, some of this information might already be present with the teams managing these systems. This exercise should identify presence of that information and make a note of that information. The point here is not to duplicate this information. If the information does not exist, this exercise will help capture such information which is relevant not only for the data governance initiatives, but is also usable by system owners and other stakeholders.
Goal of this step/answering this question is to baseline information about systems, business processes automated by the systems and data. This information is going to help in subsequent stages for establishing, change management processes, defining policies and possibly implementing and monitoring policies around data management/governance.
From this phase/question data governance initiative starts transitioning into nuts and bolts of the IT systems and landscape. In next few blog posts, I will be covering various aspects which data governance team should consider as they start making progress towards establishing official program and start working on it.
Previous Relevant Posts:
Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?
Data Governance Litmus Test: Know thy KPIs
Data Governance Litmus Test: Know goals behind KPIs
Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?
Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?