Understanding the need to adopt a Data Governance policy brings us to the parable “The Blind Men and the Elephant”, whose origin is in the ancient Indian subcontinent, from where it was widely disseminated.
It is the story of a group of blind people who learn and imagine what an elephant is like when they touch it. Each one touches a different part of the animal’s body, but only one part, such as the tail or the paw.
They then describe the elephant based on their limited experience and their descriptions of the elephant are different from each other.
In a traditional corporate environment the above parable helps us understand how each part of the organization sees data assets. Each one has its own vision and, at the same time, far from reality, as it only perceives data from its own context without having a vision of the entire landscape.
Business managers neglected “data governance” for a long time so far. On the other hand, they see their companies facing increasing data expansion and reduced resources to deal with it.
Past failed attempts have led them to question whether the path taken was worth it. However, every organization needs data that is reliable, compliant, secure, and suitable for analysis. The main purpose of active data governance is to provide just that.
Data are resources that demand a continuous refinement process. Likewise, efficient data governance must adapt and improve over time. A modern practice must consider agile DevOps thinking and be built on machine learning so that it gets better and better with less effort.
You need to have adequate capabilities that automate and manage policies, workflow, management activities, and more.
A data catalog represents a set of data about data and makes use of an essential component known as “metadata”. Metadata show pre-existing facts and are capable of describing the use of some attributes of the data to understand or even make use of that data from higher levels of abstraction.
A data catalog built from existing metadata structures needs to be simple and at the same time able to bring different stakeholders together in a collaborative process. If built and rightly implemented, it will naturally become a strategic component capable of identifying relevant data, in addition to interpreting and correlating them around a business need. At the same time, it democratizes access to relevant data and provides the effective implementation of a “data culture” that drives business results and reveals the truth around issues such as: