Since 1999, David Loshin, president of Knowledge Integrity, has developed technical and management methodologies for instituting data quality, master data management, data standards, and data governance in many different industries, including financial services, banking, insurance, health care, manufacturing, pharmaceuticals, and government agencies. Please note that the views of our guest bloggers do not necessarily reflect the views of Kalido.
There has been concern among senior managers at many organizations that source data do not fully meet the requirements of enterprise business applications, or worse, that there few or no processes have been put in place to even connect the two. In turn, this has been a significant driver to undertake data governance initiatives. Data governance programs typically begin with the formation of a data governance council. While this seems to be the logical starting point, efficacy, participation, and management support can wane in the absence of well-defined policies and processes. I refer to this as a “data governance gap” – the delta between intention and action – and it occurs when the prerequisites for data governance policies, practices, and procedures are not established prior to the creation of the council.
The data governance gap can be avoided if the data governance council’s first order of business is understanding and using best practices for managing data policies and operationalizing data governance processes. Collaboratively defining data policies in the context of current state business process, data, system and organization models, and aligning them with business objectives is essential to implementation success. This process can be accelerated by leveraging existing investments in data models, process models, data quality rules and related software through an open framework. If necessary, external advisors can be engaged to help draft an initial set of data policies.
Operationalizing data governance processes involves orchestrating comprehensive data governance processes such as data policy creation, change management, communication, implementation, issue tracking, and remediation. Data governance program performance can be measured and improved by tracking and reporting key operational metrics relating both to the data and those held responsible and accountable for those data.
Lastly, repeatable processes and executional rigor can ensure broad compliance with defined data policies that are correlated to business information requirements.
For more of my perspective on this topic, download the Knowledge Integrity white paper “Operationalizing Data Governance Through Data Policy Management” and watch my video podcast.