By Rohan Malkhare, Business & Technology Architect, of BCLC
Enterprise data today consists of diverse sets of information coming in from varied sources such as IoT, streaming and external feeds along with the more traditional forms such as transactional, configuration and administrative sources of data. Making proper and authorized use of all the data as well as making it available to the right people at the right time and for the right purpose requires an enterprise-wide collection of policies and processes designed specifically to oversee data management. This collection of enterprise-wide policies and processes typically comprises of what is ubiquitously known these days as the data governance framework.
Much of the online literature currently available on data governance frameworks is replete with vague and buzzword-filled monologues without any cohesive and concise representation of the actual value to businesses. Moreover, contemporary literature often focuses on representations of data governance frameworks in a legacy context rather than reflecting the governance utility to align with modern trends.
In this article, we will look at the seven most important value additions that businesses today can clearly acquire by investing in developing a robust data governance framework.
Framework definitions of access allow organizations to create secure control, collaboration and visibility into who has access to what pieces of information. Businesses these days are served not only by their own employees but also by many different contractors, partners and vendors that all need access to parts of the enterprise data and also need to collaborate with each other. Automated processes running in various internal and external environments also need proper access to parts of the enterprise data.
Having access definitions as a part of the framework allows for security, collaboration and visibility of data to co-exist in an appropriate balance amongst the various people and process roles that serve the company.
Compliance requirements within the framework allow for all sensitive data within an enterprise to be managed and organized in ways so that the organization is able to not only fulfill business rule requirements but also legal and regulatory needs. With the regulatory and legal regime constantly evolving across various jurisdictions, one of the key functions of the data governance framework is to keep track of the latest changes and incorporate them in the governance context of enterprise data structures.
Data security is a set of practices, processes and controls designed to protect enterprise data. The data governance framework identifies the sensitivity and importance of various datasets and recommends appropriate security measures to protect those datasets.
Unauthorized disclosures as well as tampering of enterprise data has a huge cost for the organization and the data governance framework plays a strategic role in analyzing and creating guidelines for data security.
Data quality reflects the degree of completeness, consistency and accuracy of enterprise data. Low quality data leads to many costly consequences including bad strategic decisions, broken customer relationships and bloated data systems.
Data governance frameworks set extensive guidelines for data quality dimensions as well offer training and empowerment to data owners to set quality goals for their individual datasets.
Data intelligence refers to the analysis of information to extract actionable value and meaning as well as promote enhanced data-driven decision making across the organization.
Data governance frameworks provide guidelines around the five critical aspects of analyzing and presenting data intelligence: predictive, decisive, prescriptive, descriptive and diagnostics. These five aspects help form the foundation for decision-making by making it possible to see bright spots, future opportunities as well as current and potential problem areas.
Along with these guidelines, the framework also recommends tools that make it easy to implement these five pillars of data-driven decision-making.
Businesses typically have massive amounts of duplication of data in on-prem and/or cloud-based data centers which lead to significant storage costs that yield no value. The framework simplifies data storage and recommends rules for creation of data sets so that redundancy is maintained _only_ as required by business needs and/or architectural requirements.
Beyond profit, most modern enterprises seek to contribute to society with a certain set of social purposes that are embedded in the core mission of the company. This may typically be one or more of social causes such as reducing the carbon footprint of the company, ensuring equitable distribution of opportunities in the community, giving back to veterans and others.
The data governance framework can serve the social mission at a foundational level by identifying and mandating the collection of cause-specific datasets across the enterprise in a compliant manner.
We conclude our brief article with the hope that readers can now get better clarity on the concrete business value of a data governance framework.