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RESEARCH & RESOURCES

Executive Summary | Modernizing the Organization to Support Data and Analytics

Executive summary for the TDWI Best Practices Report: Modernizing the Organization to Support Data and Analytics

As organizations strive to compete in a dynamic environment, they are trying to modernize their data and analytics environment to help. This modernization includes implementing new technologies such as scalable cloud platforms and unified approaches. It includes utilizing more advanced analytics such as geospatial analytics and machine learning. It also includes new paradigms such as the data fabric and the data mesh. Moreover, as part of this, modernization may include new organizational constructs such as the data office and new teams such as DataOps, MLOps, and data literacy enablement teams.

This TDWI Best Practices Report provides best practices research about how successful companies are organizing to execute to win with analytics. It focuses on topics including leadership structures, organizational structures, new roles, and new paradigms such as the data mesh. It also examines new technologies and their impact on organizations. Some considerations highlighted in this report include:

Leadership models. Leadership models for modern data and analytics continue to evolve. One model that is gaining traction is the chief data officer (or chief analytics officer or chief data and analytics officer), whose role it is to provide business value to the organization. In the survey for this report, the position was in the early mainstream stage of adoption, although the CDO wasn’t always part of the C-suite. CIOs, CTOs, and others still lead many data and analytics efforts. The results of this study, however, indicate that organizations where the CDO is part of the C-suite are more likely to measure the value from their data and analytics efforts.

Organizational models. This Best Practices Report also examines how businesses are organized to extract the greatest benefit from data and analytics. This includes centralized, hub-and-spoke, and decentralized organizational models. The results of the study indicate that no one model is best for modern analytics—at least not now. The right model for a particular organization will depend on their specific circumstances. The report also explores new paradigms such as the data mesh, which is not yet widely adopted, although many CDOs support the data mesh principles.

New roles. Organizations are already implementing new roles to help them move ahead on their data and analytics journey. These include data engineers, MLOps engineers, data literacy enablement team members, modern data analysts, and even data product managers. These roles might also have their own models. For instance, some DataOps teams are organized as pods. These roles are often part of a data office or a center of excellence. Adoption is typically based on where the organization is on its analytics journey, although organizations should be planning for these roles as part of their modernization strategy because these roles are important.

Enabling technologies. Vendors are offering a range of options to enable modern data and analytics, including cloud platforms and data fabrics, to help establish a unified and trusted data foundation, intuitive GUIs, and augmented and automation features to help make analysts more productive. They are also offering tools to help to deploy and manage advanced analytics in production, and tools such as data catalogs to help different personas understand and utilize data for analytics.

Although it is still early for many new technologies, roles, offices, and other organizational constructs, TDWI research suggests that they can provide a top-line impact to those companies that use them in modernizing their data and analytics environment.

Alation, Carto, Dataiku, Denodo, Qlik, SAP, and Snowflake sponsored the research and writing of this report.

About the Author

Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community, having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also co-author of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine-learning, AI, cognitive computing and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her by email (fhalper@tdwi.org), on Twitter (twitter.com/fhalper), and on LinkedIn (linkedin.com/in/fbhalper).


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