Organization refers to the people part of the enterprise and the part that I consider most important in the DG journey. This is the “Who” part of the framework. The success of any efforts including data governance is based on the readiness of the organization.
The need of Data Governance has been established at it has become one of the key initiative’s organizations are focusing on when it comes to managing the data. This blog talks about the differences in the Data Governance in Digital era when it compares to traditional Data Governance practices.
When we see entities in real world, we notice that there is a complex relationship occurs between the entities. Every entity type is unique and has multiple possible relationships. Graph databases solve this problem by providing ways to model the relationships in the database and that makes the insights very simple and easy
The key steps organizations can take to cross that hurdle/chasm and move ahead of the roadblock and prepare the foundation which will enable them to move along the curve
Organizations need leaner and agile structure to focus on the business outcome. Following are the key roles that should be part of the analytics organization which is focused solely on delivering business value
The key reasons for the need of good data lake structure are: 1) Security: need of role-based security on the lake for read access. 2) Extendibility: it should be easy to extend the lake after first round and more systems can be added 3) Usability: it should be easy to use and find the data in the lake and the users should not get lost 4) Governance: it should be simple to apply governance practices to the lake in terms of quality, metadata management and ILM
This article includes the kind of tools and methods that go along with maturity steps. I also want to introduce the concept of Chasm. Its essentially a bump or a gap in the journey of analytics maturity which takes a little more than usual effort to cross.