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Data governance is a general term that is often thrown around without the weight of a formal definition behind it. It’s a term that can apply very broadly to any rules, policies, or regulations put in place–by a company or government bodies–that determine how data is classified and tracked. Often, governance is a policing mechanism, but it has the potential to land with impact. Taking those verbose policies and turning them into actionable information for how data travels through a company yields better external data practices, and in turn, better data.
Banks and financial institutions face a range of compliance requirements from ePCI, CCPA, NYDFS Cyber Privacy, and GDPR to the precursor of them all, GLBA. There are of course many others around the world, but they all require an organization to fully understand where confidential data is, how it flows through various systems and processes, and how it is protected. For CFOs and financial pros to be able to adequately answer these challenges, data must be classified according to a standard classification scheme and must be tracked from origination from the moment it’s created or the moment it enters a financial organization.
Additionally, for relevant data classes such as personal identification information (PII), the flow of the data through internal systems must also be tracked. Establishing a comprehensive data governance plan is only the first step in successfully governing the data practices within an organization. Financial and bank CDOs, CSOs and analysts alike must establish the scope of data management by fully understanding where and how data flows and how it is used. CFOs should be cognizant of this scope from a cost perspective as well.
Remaining financially compliant is not the only goal; in today’s business climate, data and the insights it provides can be the difference between a bank that thrives and one that withers in the face of competitors that understand their customer’s data better and can apply the insights faster. In a recent survey of business leaders from retail banking and the financial services sector, Forrester found that only 20% of respondents felt confident that their organizations knew how to apply data-driven insights. Good data governance is an enabler for effectively analyzing the structure and lineage.
Regardless of regulatory requirements, industry, size, or location, an effective organization must govern the flow of data–both internal and external–within their business. A robust data governance protocol provides additional benefits to an organization, seamlessly integrating third-party data sources.
Data lineage is an important aspect of data governance. Being able to track the movement of a single data point from entry into the cloud through its transformation, schema changes, filtering, and sharing is satisfactory data lineage. This same concept also applies to how external data is sourced. Financial regulations require tracking data lineage once a datapoint enters an organization. But knowing its history before that point helps evaluate good data sources, and gives a bank the competitive advantage of having a 360-degree view of their customers. Applying and enforcing proper governance procedures to external data can improve performance, alleviate internal issues pertaining to data and help prevent data breaches. That’s a must in today’s market amid unrelenting competition, ever-escalating cyber threats, and increasing regulatory oversight.
If data lineage–as a piece of overall data governance–isn’t maintained when data is integrated, transformed and monitored, each time it changes it loses some of the value it can provide. Ask what metadata it comes with, what the current classification is, what the schema structure looks like, and the timeliness of the source. This information should be tracked as it’s consumed by an organization in various places to support the data management policy. The solutions external must support the overall governance strategy and be enforced based on who is doing what with the data in each application.
In today’s technology-fueled financial landscape, external data can fuel machine learning, algorithms, risk models, and insights into customer behavior–all of which have the power to take an organization to the next level if the data supply is properly sourced and maintained. Doing so means organizations must internally build or externally source solutions for data integration, transformation, and monitoring in a way that supports data governance to reap the benefits external data has to offer.
Will Freiberg is the CEO at Crux Data.