Want to Become a Digital Leader? Automate Data Governance.

This article was written by Informatica and originally appeared on the Informatica Blog here:


As a data management professional helping evangelize the need for a modern data foundation with intelligent data governance, not a day goes by without me hearing from a customer or analyst about the importance of effective data management for driving digital transformation. This point was driven home powerfully in the recently published IDC Global Survey of Chief Data Officers (CDOs) sponsored by Informatica .

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More Data Sources Means More Data Governance Is Needed

Stewart Bond, IDC Research Director who led the study, succinctly summarizes this in the following quote: “Data is the lifeblood of digital transformation and how well you manage it impacts your business success in a digital-first world.”

However, the survey highlights growing data fragmentation and complexity as pervasive problems for organizations, diverting attention of data leadership away from innovation and increasing risk.

For instance:

  • Two-thirds of surveyed organizations regularly use multiple clouds (see Figure 1).
  • A staggering 79% of them use over 100 data sources, a number that keeps growing by the day (see Figure 2).
Figure 1. Two-thirds of surveyed organizations regularly use multiple cloudsFigure 1. Two-thirds of surveyed organizations regularly use multiple clouds
Figure 2. 79% of surveyed organizations use over 100 data sourcesFigure 2. 79% of surveyed organizations use over 100 data sources


The survey findings underline the need for a focus on effective data management with data governance to address these challenges and enable data-driven business value.

But, to me, what makes this survey really valuable are the insights it provides on where the opportunities are:

  • What separates the leaders from the laggards?
  • What do data leaders need to focus on to drive more business value from data and become true digital leaders?

The survey indicates that organizations with the highest level of data management maturity are three times more likely to be leaders in digital transformation and generate 250% more business value from data.

How to Address Data Management Challenges with Intelligent Data Governance

Effectively addressing data management challenges starts with contextual understanding of your data and the data intelligence you need to enable timely and responsible use of data for data-driven decision-making. Data governance and privacy programs play a critical role in helping drive discovery, definition and understanding of business-critical data. They also provide guardrails for appropriate use of data in compliance with internal policies and external regulations.

It’s no surprise that the survey finds that organizations across the data maturity spectrum view data governance and privacy as a priority to help ensure the right data is being used by the right person for the right reasons. Yet, only 36% of organizations have standardized their data governance and privacy function (see Figure 3). Clearly, this points to an untapped opportunity for organizations that can be addressed with the right level of focus and investment in data governance and privacy.

Figure 3. Only 36% of surveyed organizations have standardized their data governance and privacy functionFigure 3. Only 36% of surveyed organizations have standardized their data governance and privacy function


Data Governance Insights: 3 Key Findings from IDC CDO Global Survey

Now, let’s take a deeper look at additional survey findings tied to data governance and understanding of data, and what it implies for areas of opportunity to focus on:

  • Automated data discovery: Data discovery is often the foundational first step for data governance programs and enabling data intelligence. A scalable, AI-powered data catalog can be an invaluable tool for organization-wide data discovery and begins to address data fragmentation and complexity challenges. However, the survey found data cataloging and metadata management are among the least important priorities for reactive organizations.
  • Data quality for trusted business decisions: Optimized data organizations are more likely to incorporate data into decision-making and understand how improving data quality improves the quality of analytics and AI for trusted business decisions. Optimized data organizations are two times more likely to make data quality a top objective. AI-powered automation, such as creating data quality rules from natural language business definitions and automatically applying these rules to all relevant datasets, is essential to address the need for data quality at scale across the entire data estate.
  • Data democratization and sharing: Everyone now understands the value of trusted data for informed business decisions. However, to turn this into reality at scale, organizations need to find a way for data users of all skill levels, including all line-of-business users, to easily find, understand, access, trust and share the data they need for driving business decisions. It should be as simple as “shopping for data” using a self-service data marketplace. Yet, only 31% of organizations provide AI-powered self-service access to all the data needed by different teams. At the same time, it’s no surprise that 75% more of optimized data organizations, who are at the top of the maturity scale, enable business self-service access to data compared to reactive organizations, who are at the bottom of the maturity scale.

AI-Powered Automation Is the Only Way to Scale Data Management and Governance

Investing in data cataloging, metadata management, data quality and self-service data access as part of a cohesive approach to data governance and intelligence can pay rich dividends for organizations. However, given the scale of fragmentation and complexity in the modern data landscape, the survey highlights that AI-powered automation is the only viable approach to tackling this challenge across the business’s entire data estate.

There is still a lot of untapped potential in the automation of key data management functions. For instance, only 37% of organizations have automated most of their data quality and enrichment processes, and only 38% of organizations have done the same for data cataloging. On the other hand, optimized data organizations are five times more likely to have operationalized the use of AI to automate data management activities. All these findings underline the huge potential gains to be made through AI-powered automation of critical data management functions.