Tag: #datagovernance

In our previous post, we discussed value measures, both absolute and relative and their ability to help leaders communicate the impact of data strategy investments. By assessing both value types while defining our data strategy, we create a compelling picture…

Our previous post focused on ‘lightweight governance’ – enabling engineering and product teams across an organization to execute with a high level of autonomy. This drives success by having access to context regarding policies and standards, shared technology tools for…

Previously we discussed the role monetization plays in creating new revenue streams for organizations and the demands it places on our data governance programs to ensure legal obligations are met and produced data products are of high quality. Early decisions when building your…

In our previous discussion we explored the application of data governance programs to legacy technology platforms. We explored methods to ensure high levels of integrity with data managed on these legacy platforms and what organizational investments allow us to reduce risk and…

In our previous post we discussed the governance requirements for creating, managing and deploying analytical models. Analytical models do not stand on their own in today’s complex data landscapes. They have unique needs that must be captured in policy, automated…