Chief Data O˜cers’ Perspectives on...Debra Slapak, Nicole Reineke, Hanna Yehuda Subject:...
1
Click here to learn more about our conversations. Visit delltechnologies.com/datamanagement for insights about how we can help you with your data. © 2020 Dell Inc. or its subsidiaries. All Rights Reserved. Dell Technologies is a trademark of Dell Inc. or its subsidiaries. Dell Technologies believes the information in this document is accurate as of its publication date. The information is subject to change without notice. 1 Based on interviews with nine CDOs across the indicated segments. . Our conversations with Chief Data Officers (CDOs) identified two emerging groupings with distinct priorities. Innovation- focused CDOs Priorities Revenue-driven Not highly regulated Informal processes CDOs from Marketing & Advertising Software Technology Regulation- focused CDOs Compliance-driven Highly regulated Formal review board CDOs from Finance Insurance Priorities 2 • Drive differentiation with data science and the underlying data. • Combine business and engineering savvy in cross-functional teams. • Help upskill team members with training and tooling. • Broaden employee access to data science through tooling. • Drive governance, security and risk avoidance with data management and data science. • Invest in formalized, institutional processes and review boards. • Utilize off-the-shelf algorithms for commodity activities and automated machine learning (autoML). • Centralize data science skills to leverage across the organization. Emerging Chief Data Officer segments 1 Regardless of their priorities and motivations, CDOs revealed four attributes for measuring and achieving maturity of their data management and data science practices. Maturity attributes 4 Attribute scorecard 5 of 9 CDOs we interviewed had mature adoption of tools to manage data. Organizational trust Data platform fit for purpose Prioritization and measurement Interpretation at scale How does your organization stack up on these four attributes? Diagram: How the CDOs rated their level of maturity against each attribute. Higher levels of maturity are shown farther from the center. Prioritization and measurement • High-value projects are prioritized across the organization. • Value measurements include revenue increase, business savings and risk mitigation. • Projects are re-used to increase return-on- investment. • To build trust, CDOs must work effectively across the organizations. • CDOs who possess IT skills and business acumen are more likely to succeed at this task. • When there is trust, the business invests in data- centric value creation. Organizational trust • Data is managed in a way that is meaningful for the use cases it must support. • A single source of truth is a tooling requirement. • Tools must support governance and vertical mandates for data management and access. Data platform fit for purpose • Team members are trained and empowered to interpret data. • Business understanding and engineering capabilities are built into teams. • The organization can use and deploy models at scale. Data interpretation at scale Chief Data Officers’ Perspectives on How to Achieve Data Management Maturity
Transcript of Chief Data O˜cers’ Perspectives on...Debra Slapak, Nicole Reineke, Hanna Yehuda Subject:...
-
Click here to learn more about our conversations.
Visit delltechnologies.com/datamanagement for insights about how we can help you with your data.
© 2020 Dell Inc. or its subsidiaries. All Rights Reserved. Dell Technologies is a trademark of Dell Inc. or its subsidiaries.
Dell Technologies believes the information in this document is accurate as of its publication date. The information is subject to change without notice.
1 Based on interviews with nine CDOs across the indicated segments.
.
Our conversations with Chief Data O�cers (CDOs) identi�ed two emerging groupings with distinct priorities.
Innovation-focused CDOs
Priorities
Revenue-drivenNot highly regulatedInformal processes
CDOs fromMarketing & Advertising
Software Technology
Regulation-focused CDOs
Compliance-drivenHighly regulated
Formal review board
CDOs fromFinance
Insurance
Priorities
2
• Drive di�erentiation with data science and the underlying data.
• Combine business and engineering savvy in cross-functional teams.
• Help upskill team members with training and tooling.
• Broaden employee access to data science through tooling.
• Drive governance, security and risk avoidance with data management and data science.
• Invest in formalized, institutional processes and review boards.
• Utilize o�-the-shelf algorithms for commodity activities and automated machine learning (autoML).
• Centralize data science skills to leverage across the organization.
Emerging Chief Data O�cer segments1
Regardless of their priorities and motivations, CDOs revealed four attributes for measuring and achieving maturity of their data management and data science practices.
Maturity attributes 4
Attribute scorecard
5 of 9 CDOs we interviewed had mature adoption of tools to manage data.
Organizational trust
Data platform �t for purpose
Prioritizationandmeasurement
Interpretationat scale
How does your organization stack up on these four attributes?
Diagram: How the CDOs rated their level of maturity against each attribute. Higher levels of maturity are shown farther from the center.
Prioritization and measurement
• High-value projects are prioritized across the organization.
• Value measurements include revenue increase, business savings and risk mitigation.
• Projects are re-used to increase return-on- investment.
• To build trust, CDOs must work e�ectively across the organizations.
• CDOs who possess IT skills and business acumen are more likely to succeed at this task.
• When there is trust, the business invests in data- centric value creation.
Organizational trust
• Data is managed in a way that is meaningful for the use cases it must support.
• A single source of truth is a tooling requirement.
• Tools must support governance and vertical mandates for data management and access.
Data platform �t for purpose
• Team members are trained and empowered to interpret data.
• Business understanding and engineering capabilities are built into teams.
• The organization can use and deploy models at scale.
Data interpretation at scale
Chief Data O�cers’ Perspectives on How to Achieve Data Management Maturity
https://www.delltechnologies.com/resources/en-us/asset/white-papers/solutions/cdo-perspectives-how-to-achieve-data-management-maturity.pdfhttps://www.delltechnologies.com/en-us/what-we-do/emerging-technology.htm#data-management
