Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance.
What does the future of analytics look like in your organizations enterprise architecture? Does it include thinking about a two speed approach to analytics which includes both: An agile rapidly changing analytics platform for innovation (a lab) seperated from operations and broad enterprise audience usage A slowly moving systematic enterprise analytics platform (a factory)
If your organization is large enough, it probably has multiple data-related initiatives going on at any given time. Perhaps a new data warehouse is planned, an ERP upgrade is imminent or a data quality project is underway. Whatever the initiative, it may raise questions around data governance – closely followed by discussions about the
