In recent years, aspirations for achieving digital transformation objectives have inspired the emergence of new senior management roles such as the Chief Data Officer (CDO), the Chief Analytics Officer (CAO), and the Chief Information Security Officer (CISO).  These roles indicate a general recognition of organizational value of data as a (conceptual) asset. They also reflect the division of digital transformation into virtual operational silos: Data management versus analytics versus security, privacy, etc.

Compartmentalizing these different facets of information management and governance perhaps does a disservice to the organization’s objectives for digital transformation. This is especially relevant since there’s a senior officer role that has been around for decades that should be at the center of any organizational initiative for digital transformation: the Chief Information Officer (CIO). The scope of the CIO needs to be in synch with the growing and evolving role of the CDO. Along those lines, it should not be reduced to that of tactical tasks, like reactive data management. The CIO and CDO should work as a team, particularly as data is now understood to be a strategic way to grow the business.

With the growing demand for data accessibility and advanced analytics, it may be time for the CIO to concentrate on information management methodologies that can shape successful digital transformation initiatives. Following are four suggestions for how to do it.

Realign the organization’s perspective on corporate information architecture

There are three key components involved in realigning perspectives:

  • A holistic revision of the collective end-to-end information pipelines, including data source assessment, data ingestion, stream processing, data persistence, integration, preparation, organization, accessibility, production and delivery.
  • Data democratization, which includes identifying and eliminating information bottlenecks, simplifying self-service, and empowering analysts and data scientists with broad capabilities for advanced analytics.
  • Instituting auditable data protection. This focuses on having conceptual categories of information assets according to a well-defined ontology for data sensitivity – and corresponding practices to prevent unauthorized or unethical information use.

Engage business consumers to fully understand and internalize expectations for information access and use

Many data management teams have limited visibility into optimal methods for assessing data user requirements. In some cases, the data analyst presents the user with a menu of data elements and asks the user to choose which ones they want in their reports. At the same time, agile development methodologies often push data requirements off to later sprints in the development sequence, further complicating proper requirements analysis. The CIO can positively influence this process by dovetailing information requirements into the agile methodology. Develop engagement models for soliciting information requirements in relation to what each role does, and institute intuitive processes to identify the underlying information requirements.

Integrate a “DataOps” approach to data pipeline management

Within modern hybrid environments, data integration and interoperability will be significant challenges. A key to addressing this challenge is adopting a concept called “DataOps” for facilitating end-to-end management of ingestion, integration and utilization of data from various sources to targets. Because organizations incorporate traditional (i.e., structured) data and an increasing variety of other types of information – and have to support different use cases – data integration can no longer be limited to a sequence of coordinated batch tasks of data extraction, staging, transformation and loading. Instead, organizations should introduce methods and tools to develop, manage and orchestrate data pipelines so they can develop and deploy analytics on a continuous basis.

Modernize the information management infrastructure

Cloud computing is a disruptive force. It not only promises scalable platforms at significantly reduced costs; it challenges information leaders to find ways to modernize existing business processes that will take full advantage of emerging and continually developing technology platforms and services. While you might be tempted to migrate your current on-premises applications to the cloud, take the opportunity to assess the scope of the extended information enterprise and develop a technology modernization vision that leverages a multi-cloud hybrid environment. Not only will this help streamline application modernization and migration, it will inspire ingenuity and innovation in developing net new applications built for the hybrid enterprise.

Setting the stage for the future

Reflecting on these concepts, you may see that they set the stage for sophisticated digital transformation initiatives – integrated machine learning analytics, robotic process automation and information monetization. In my follow-up post, we’ll explore the relationship between the CIO’s role and the data/information/insight transformations that will fuel the future of the innovative enterprise.

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About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at

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