Author: Joey Jablonski

In our previous post we discussed the five phases of data governance programs. These include define, design, build, transition and measure. These phases allow us to plan work, execute, and understand a program’s impact while adjusting future prioritization and maximizing…

Many organizations hear “third-party data” and jump to the consumption of external data for augmenting their in-house generated and curated datasets. But there’s another aspect of third-party data, which is providing it back to others for their consumption. This is…

In a previous post, I outlined how designing data governance programs can deliver business value. In this post, we’ll look in greater detail at the life cycle of a typical program.     Data governance programs thrive on established and…

As organizations embark on their Data Governance journey, many struggle with justifying the costs with program outcomes that have traditionally been viewed as required but not delivering significant business value. This often pits data governance programs against product initiatives in…

Many organizations experience a stall during their data transformation journey. The stall is caused by a lack of skills and shared language across the organization needed (to) experiment and rapidly shift approaches so they can optimize their nimbleness. This stall…

  The IT world often speaks in terms of production (PROD) and non-production (NPROD) which can cover an endless set of functions including user acceptance testing, quality assurance, development, validation or staging. This separation is often used to denote environments…

Events over the past two years have highlighted the many complexities and unknown dependencies in global supply chains. As different countries shut down, re-opened and shut down again, it caused a cascading effect on the manufacturing of complex semiconductors, consumer…