Data governance is the people, process, and technology required to create a consistent and proper handling of an organization’s data throughout the entire enterprise. It provides all data management practices within the necessary foundation, strategy, and structure needed to ensure the data is managed as an asset and transformed into meaningful information.
In yesteryear, it’s clear past data management techniques that could be instituted and maintained by few. As the modern enterprise grows and becomes increasingly data-centric, data is re-purposed many ways with Big Data and NoSQL, in various places and siloed BI data stores.
As a strategy, an operating philosophy must be used in governing the company’s data handling to increase confidence in decision making, improving security, accountability for information quality, optimizing effectiveness, and eliminating re-work.
“In order to be successful in data governance, you need to be successful at metadata management,” Amnon Drori, CEO of Octopai stated. “The overall philosophy of governance must have strong metadata management to better understand the context, journey, or data lineage of the information.” The context and subtleties are key in the information-driven enterprise. Without context, businesses have experienced the data leading to misleading indicators and ultimately, bad decisions. If a poor decision was made, either business or data breach, you need to go back and trace the data lineage to see how perhaps that decision was made from a configuration or application of that particular data at that specific time.
Metadata management can give you visibility as to where the data resides to illuminate the context, and later to re-purpose – to utilize effectively to further wring out the desired information for insights. This strategy needs buy in from both the technical and business constituents. It is suggested to lead with the business users to highlight the business lineage as well as specific technical lineage.
There are tools to make this easier, but the organization must adopt the governance and metadata philosophy as foundational to success. This will also help ensure regulatory compliance, and help re-purpose for value-add data science applications such as machine learning or data visualizations.
The organization must look at this as value in several key ways:
- Data Governance is a requirement through regulation (GDPR)
- Metadata Management is the underlying pillar to Data Governance
- Efficiency and Productivity are based on streaming, ever-growing data, and maximizing insights. Decision making is enabled through contextual data lineage and metadata management
The organizations to embrace and lead metadata management as critical to success will find themselves agile and having the ability to deal with regulatory issues, market shifts, and/or market opportunities. Legacy systems predate many in the modern enterprise, however, starting with small steps like a metadata management solution will create the necessary vocabulary and insight the company needs for calibrating to this new reality.
