Agility in Data Governance

According to some studies, the Data Governance Market is projected to register a CAGR of over 20% in the next few years. More and more enterprises understand the importance of establishing a governance regime to enhance data analytics, comply with regulations, and ultimately make data-driven business decisions. However, whether you have built a data governance program or are developing one, you need to realize that data governance is a continuous and dynamic process; and agility is essential to its successful implementation.

Agile governance requires agile support of various technologies

One of the challenges enterprises are facing is the rapid adoption of new data sources and technologies, due to initiatives such as cloud migration. To establish or maintain a unified and centralized knowledge graph, enterprises need to ingest metadata from these new data sources or technologies at scale and at speed.

For example, if you are modernizing your infrastructure and building a hybrid cloud architecture, you need to quickly ingest metadata from the new technologies such as Snowflake, Redshift, Hive, Javascripts, and Python to integrate with the legacy tools. Failing to do so, you are creating new data silos, which is a huge roadblock for data governance. Agility in incorporating new data sources is a prerequisite to agile data governance.

Agile data governance means efficient propagation of trust

Enabling self-service is a core capability of a data fabric. Data governance helps harmonized data assets for easy discovery of and access to data. In addition, it builds trust in the data so that data users have confidence in the data they choose to consume. Agile governance propagates trust throughout the data supply chain. Through data lineage, a sophisticated data governance platform, can visualize trust related information such as data source, data owner, certification, quality, term-to-asset mapping, and user rating at every point in the data pipeline.

Collaboration! Collaboration! Collaboration!

Data governance is a collaborative effort. Agile data governance, in return, facilitates collaboration. With the help of AI/ML, an agile governance platform automates the process of establishing and enforcing rules and policies. Data stewards can “see” the policies and rules in a knowledge graph and easily update them to set up a dynamic regime for data access, control, and compliance. With transparency, everybody understands and follows the proper usage of data. Data producers, consumers, and the governance team are all on the same page. In this environment, collaboration becomes a cultural norm.

Orion’s Enterprise Information Intelligence Graph (EIIG) is a platform for agile data governance. It is capable of automatically ingesting metadata from 60+ technologies with zero impact on production systems. Besides building a fact-based catalog, EIIG automatically generates end-to-end data lineage across technologies and offers automated metadata analytics. EIIG can automatically tag assets based on rules. It also offers the ability to propagate bi-directionally the tags all the way to the end points through lineage.

Take GDPR compliance for example, EIIG is capable of identifying all assets that contain GDPR data much faster than any other solution in the market. Data stewards and compliance officers can visually identify such assets and gain traceability of these assets from source to target. Then, they can apply policies and rules according to GDPR requirements. In the same knowledge graph, data producers and users can visualize such policies/rules and generate and consume data in the manner complying with GDPR regulations. Data governance thus becomes more agile and collaborative.

About the author: Niu Bai, Ph.D. is the Head of Global Business Development and Partnerships at Orion Governance, Inc.