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Informatica has published the findings from the second annual IDC Global Survey of the Office of the Chief Data Officer (CDO) examining the CDO’s challenges, priorities and key performance indicators while stewarding enterprises to success in a digital-first world.
“The changemakers of tomorrow in digital transformation will have to be move beyond just data integration to data intelligence but 37% of data leaders are spending most of their time grappling with data complexity as opposed to driving true transformation with data,” said Jitesh Ghai, Chief Product Officer, Informatica. “This year’s annual CDO study reveals that data fragmentation will be the biggest barrier facing data leaders next year and the key characteristics of those leading data-led transformations and achieving business value versus those that are still struggling to make sense of all of their data.”
Three key themes from the 2021 Global CDO study include:
Data Fragmentation and Complexity Distract from Innovation
Enterprise infrastructure will be cloud-first and multi-hybrid for many years, with systems spread across on-premise and multi-cloud environments. The findings showed that:
•Nearly 80 percent of organizations surveyed store more than half of their data in hybrid and multi-cloud infrastructures.
•79 percent of organizations are using more than 100 data sources, with 30 percent using more than 1000 sources.
•37% of data leaders are barely keeping the lights on when it comes to data management as opposed to driving strategy or innovation with data
But it is this fragmentation, with data spread across multiple sources and many clouds that is making it much more difficult to discover, manage and derive intelligence from their data. Highlighting the chasm in delivering business value between data leaders and laggards, the study found that enterprises with a high level of data maturity generate 250% more business value than those only beginning their data-led transformations, where most of the time in data management is spent keeping the lights on.
Operationalizing AI to Automate Data Management is Critical to Success
Only AI can deliver the speed and scalability demanded by modern enterprises and the study found that data mature organizations were 3X times better at operationalizing AI to automate data management activities than their less mature peers.
•Innovation with data starts with enabling access, yet only 31 percent of organizations provide AI-powered self-service access to all the data needed by different teams
•Organizations in APAC lead the way with 37 percent automating data management across the business.
•North American organizations follow closely behind with 33 percent and EMEA businesses slower to adopt automation with only 25 per cent deploying it in all areas of the organization.
Data leaders were also seen to be leveraging AI driven insights and process optimization to improve efficiency as well as the availability and use of data to users within the business.
Cloud-centric models and integrated data management approaches
The study highlighted how critical data management is to digital transformation, noting that organizations with strong data leadership are three times more likely to be well underway with digital transformation. With cloud central to that, migrating to the cloud was a primary objective for 34 percent of respondents.
•Over the past year most organizations increased the data functionality hosted in the cloud by 10-20 percent. However, a quarter of EMEA and North America enterprises saw a 30 percent uptick.
•This momentum is set to continue with 30 percent of organizations noting that migrating data management functions to the cloud is a priority.
•75 percent of organizations do not yet have a complete architecture in place to manage an end-to-end set of data activities including integration, access, governance and protection.
The way organizations resolve fragmentation and complexity issues separates leaders from laggards, with leaders adopting an integrated approach to data management with standardization and automation as core facets.