Data Governance: From Risk Management to Business Value

Last Published: Dec 23, 2021 |
Jitesh Ghai
Jitesh Ghai

Chief Product Officer

data governance requires empowering people across the organization

You’ve heard the analogies: Data is the new oil, data is the new jet fuel, data is the new currency, etc. However, research by New Vantage Partners indicates less than one-third of organizations are data driven. To truly capitalize on the opportunity of being a data-driven business you must empower as many people as possible across the organization with data that is fit for their business use. This means data governance needs to shift from a risk management focus to a business value focus, and must scale to address more data, data types, and users, while simultaneously addressing more regulatory requirements.

So how do you make the shift to business value?





  • Data Strategy: You have a business strategy, but do you also have a data strategy that is mapped to your business strategy? A McKinsey study found only 30% of respondents had a data strategy in place. Data governance helps you understand how data is linked to business processes and outcomes, so you can build a data strategy and prioritize data management activities based on business goals such as increasing revenue, decreasing cost, and minimizing risk.

  • Data Literacy: It’s not enough to simply collect more data, people must understand what data is available, as well as its business meaning and context. In fact, According to Gartner[1] by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy. Data governance helps you with data discovery, and cataloging, as well as standardizing business terminology, and data policies. These capabilities are critical for creating a shared language about data, as the number of people, and diversity of skills, experience, and backgrounds grows.

  • Data Trust: With data being used by more people across the organization it’s critical to create trust that the data is fit for their business use. A Harvard Business Review article reported that only 3% of data meets basic quality standards. Data governance helps you show data lineage and its quality; and with it, you can crowd source relevance for specific use cases. Data governance also helps you understand how quality changes as data flows between systems and across processes, as well as embedding data validation checks directly into business process workflows to ensure quality at the point of entry.

  • Data Privacy: Data protection and responsible use of data are critical if organizations want to continue to have access to customer data. According to RSA Security research, nearly 70% of global consumers are prepared to boycott any company they believe doesn’t take data protection seriously. Data governance helps define and implement policies and controls that ensure appropriate collection, use, and lifecycle of personal data. It also helps you provide transparency to customers on questions like: What data is being collected? How is it being used? And who is it being shared with?

Data governance and automation

When data governance was just oriented around compliance, the scope of data and the governance requirements were controlled and prescriptive. This narrow focus made it possible to use manual processes for governance and stewardship activities. In the new world of business value-based data governance the sheer scale of data, and the collaboration required across all organizational functions makes automation critical to success.

We now have data lakes with petabytes of data, being updated in real time with streaming sensor data, social data, and mobile location data. There are tens of thousands of users accessing the data across finance, sales, marketing, service, procurement, research and development, manufacturing, logistics, and distribution. It’s at least a thousand-fold increase in scale and complexity. At this scale the only way you will keep up is with AI-powered automation.

Gartner predicts, through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated the addition of machine learning and automated service-level management. During Informatica World 2019 we showcased AI/ML innovations for data governance and privacy, including automated:

  • Domain discovery
  • Data classification
  • Mapping of business terms to technical metadata
  • Linking of data across structured and unstructured sources
  • Data quality rule generation and execution

You can get a deeper look at these innovations at the Informatica Summer Launch when you register for one of these virtual events:

The System Thinking approach

Governance and privacy represent the fifth pillar of a System Thinking approach to data management. You can read more about System Thinking in our blog series:


First Published: Jun 10, 2019