An agile approach is to identify smaller data governance initiatives based on strategic projects or business needs, and build from there.

Following these steps, the data governance program can be set up as a highly structured organization and set of defined processes with tools and templates, or it can be set up as a less structured team of individuals who work together to accomplish the goals and work through the roadmap.

The approach you decide to take should be one that correlates to your organization’s culture, data management maturity level, data governance objectives and desire for structure.

Can data governance be agile? Many organizations are now recognizing the need for data governance but are still struggling with the right way to structure it. Read more at: http://www.cio.com/article/3203410/data-management/can-data-governance-be-agile.html

Competition today is much fiercer than competition in yesteryears. In such a world getting a comparative advantage of machine learning would be beneficial. Though in their initial phase data analytics and machine learning faced criticism, the development of data structures and more accessibility helped to minimize criticism. Data is the source of information and the mining right kind of data would lead to significant results. There are four elements of data management, namely hybrid data management, data governance, data science and data analytics. Linking all the departments of a company is necessary to ensure free flow of information and accessibility of data. Read more at: https://readwrite.com/2017/06/21/competitive-advantage-machine-learning-dl1/