One natural consequence of the influx of big data is that organizations are modernizing their infrastructures to do a better job of ingesting data from new sources, identifying data that has value, and leveraging that data. As they address those challenges and tap into the opportunity that lies within big data, information governance best practices are critical to their success. This includes areas like metadata, data quality, master data, data security and data lifecycle management.
The term “governance” conjures up an image of rigidity—rules and regulations, measurements and assessments. But yesterday’s data governance practices are not ideally suited to the new, more fluid environment. Rather, we need a new process that adapts easily to change and supports a continuous cycle of problem definition, solution implementation, and assessment that can lead to new problem definitions and new projects.
Are business policies and assessments of processes against those policies still needed? Absolutely. But different types of data, to be used for different purposes, might be assessed differently. Does a comment about a planned purchase gleaned from a social media site need the same level of scrutiny as a revenue report from an internal accounting system? Perhaps so or perhaps not, depending on the intended use of the information
A continuous-loop process, with phases based on planning, acting and assessing, and then planning again, has been defined and documented in a new white paper on the IBM Agile Information Governance Process. The paper details the six steps to governance, across three phases, and describes the information governance implications of several key types of projects.
The paper helps you answer questions like:
- How should I get started with data governance?
- How should we organize for success?
- What technology should we consider to accelerate our initiative?
Has your organization standardized on a process for data governance? We’d like to hear about it!