Data Intelligence

Data Catalog and Governance

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:
a. What data do we have at our disposal?
b. Do I have the rights to access this data?
c. How do I do that?
d. Who owns this dataset?
e. Can I trust their quality?
A data catalog implemented from an active approach becomes a key component for implementing a “data factory” as it will contain and connect rich metadata related to data sources available across the organization.

Active Data Quality

An active approach to data quality observation allows teams to discover, prioritize, and resolve data issues collaboratively. An integrated strategy needs to be able to bring all the data together, resulting in analytics everyone can trust.

This requires engaging the right people and getting them involved by setting alert preferences based on role, team or subject matter experience. And go beyond, enabling an organization to ensure that its data is always aligned with business expectations. Furthermore, being able to define what is expected and what is important for each stakeholder, being able to monitor whether the data is up to the task or to the goal.

To achieve it companies need to break once and for all with the so-called “historical data silos” and build trust in your data, bringing together the right people who collaborate in data curation with the objective of creating a proactive and permanent posture and not only in specific situations and reactively to solve an specific problem.

An effective strategy will also allow you to bring people together to identify the underlying issues that affect your data and will cause problems in the future.

Data Privacy

Organizations need to be aware of the challenges that evolving regulations bring and be able to respond to new requirements through the use of a scalable and enterprise-wide platform, at the same time compliant with different regulations, including CCPA, LGPD and GDPR.

You need to be able to support compliance at scale by cataloging corporate data, operationalizing privacy policies and performing impact analysis in a centralized location.

A correct approach discovers and classifies sensitive data assets at scale. In addition, it actively tracks changes in the data environment, provides insights based on usage patterns, and provides alerts informing users of new policies and impacts in their workflows.

You need to be able to understand all corporate data and related assets to provide a unified view of potential risks. This is the way for companies to proactively identify, manage and mitigate risks.

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