Top ten priorities for data, analytics, & AI executives

Build and modernize technology architecture and infrastructure to enable data, analytics, and AI capabilities.

Effective data, analytics, and AI capabilities rely on modern technology architecture and infrastructure.

Conduct a comprehensive evaluation of existing technology and data infrastructure to inform your modernization plan. Your plan should also highlight the value of data as part of the core capabilities and infrastructure investments and its relationship with desired outcomes.

By developing a roadmap that outlines key initiatives and milestones, you can clearly demonstrate how early gains can be realized.

Establish a compelling vision, communicate an effective data strategy, and foster relationships with stakeholders.

Now is the time for CDAOs to take the lead in developing a clear vision for an effective data strategy for the organization.

To ensure success, you should engage, collaborate, and influence a diverse network of stakeholders and decision-makers impacted by these strategic initiatives.

A clear and compelling plan will help foster relationships, build trust, and cultivate a data-centric culture throughout the organization.

Articulate and measure the value of data, analytics, and AI investments.

CDAOs should focus on initiatives that yield measurable benefits and clearly demonstrate ROI, articulating and quantifying the value created for the business.

Consider developing a standard data value index to drive crucial connectivity to business value of data which can help ensure ongoing support and investment. Aligning your role and responsibilities with the larger value realization equation can help instill a sense of security and confidence in strategic initiatives.

Drive shared accountability by establishing and implementing a modern, flexible data governance and operating model.

Now a shared asset, data is driving informed decision-making across the organization. CDAOs should lead an advanced data governance and operating model approach for managing these data assets to ensure quality, integrity, and security.

By implementing a flexible operating model adaptive to evolving data requirements, technological advancements, and business needs, you can foster a culture of enterprise-wide accountability and accountability for this shared asset.

Attract, develop, and retain talent with data, analytics, and AI skills to build an AI-ready workforce.

To address the pressing need for an AI-ready workforce, CDAOs should align the technical capabilities of data and AI specialists with the organization’s strategic objectives.

As CDAO, you have an opportunity to build AI fluency organization-wide—not just within specialized teams—and integrate a balance of technical expertise and business acumen as part of your team mandate. This approach can enable the creation of actionable insights that drive business value.

Champion the development of traditional data scientists and statisticians to include a broader capabilities that help position them as advisors to the business.

Foster a data product mindset and insights-driven culture through data democratization.

CDAOs should stimulate greater interest, foster buy-in, and facilitate a culture shift by incorporating a “Data Product” mindset supported by a robust data governance framework, modern data architecture ecosystem, and other initiatives tied to the top CDAO priorities.

To foster a data-driven culture, consider driving initiatives that promote data literacy and AI proficiency through training programs, workshops, and seminars that encourage learning, experimentation, and innovation.

Leverage technology investments to capitalize on the Gen AI momentum.

As organizations shift from discussion to action on Generative AI, CDAOs should start to reframe Gen AI as a subset of broader data, analytics, and AI initiatives and capabilities, such as data governance and cloud modernization.

Craft a compelling value narrative that aligns technologies to strategic business objectives to maximize impact and demonstrate value.

Manage and mitigate data privacy, security, and compliance risks and align with evolving regulatory changes.

In today's data driven landscape, CDAOs should ensure that their data and AI practices comply with legal and regulatory standards. Implement protocols that ensure data protection measures are embedded throughout the organization’s processes.

This proactive approach helps mitigate risks and fortifies your organization's reputation by upholding high standards of data privacy and security, aligning with evolving regulatory requirements.

Evaluate and create accountable ecosystem partnerships to augment in-house capabilities.

To bolster in-house capabilities and meet future goals, CDAOs should thoroughly assess their current team's skills and technological infrastructure. Evaluate potential partners or vendors, prioritizing those who can significantly enhance analytics and AI capabilities.

By creating a dynamic, adaptable data ecosystem that strategically combines in-house and outsourced expertise, you can accelerate innovation and maintain a competitive edge in a rapidly evolving market.

Demonstrate internal and external commitments to ethical data, sustainability, and strategic trust.

Organizations are increasingly expected to uphold ethical standards in their data practices. Articulate clear expectations and demonstrate a strong commitment to ethical data practices to build foundational trust and protect the organization’s reputation.

A culture of data-driven intelligence relies on trust and is supported by robust governance frameworks to ensure sustainable business practices.