Editorial
Established 2006, the Chief Data Officer and Information Quality (CDOIQ) Symposium has become a haven for data leaders, providing a platform for emerging chief data officers (CDOs), especially now in the age of generative AI. With an early foundation rooted in data quality, the event emphasizes the importance of precise and reliable data management, drawing inspiration from Honeywell and other total quality initiatives.
The symposium’s early core premise was that data is much like manufactured products, requiring rigorous standards and processes to ensure its quality. To be clear, the event’s leaders believe data is manufactured and not created out of the corporate ether. Part of the symposium’s charter now includes offering a CDO Certification Program to help CDOs mature in their roles by equipping them with the skills and knowledge to succeed in their data agendas.
Stewart Bond, Research VP at IDC, 'Enabling GenAI Data Governance'
In Stewart Bond’s presentation, he discussed how ChatGPT has increased IT investment despite economic uncertainty. He emphasized the tangible risks, noting that 10% of organizations are advancing effectively, with another 10% experiencing disruptions. The main hindrance his data shows is AI readiness — data maturity and the skills for advancing maturity.
IDC identified 52 use cases across financial services, government, health care, manufacturing and retail. In this process, he highlighted CarMax’s use case for improving customer experience (CX) through enhanced product data access. He said there is a 72% success rate for proof of concepts (POCs). Yet, 17% of POCs proceed to production.
Bond believes data issues are likely central to this issue, looking at the entire value chain from data injection to decision making. Governance requires organizations to identify and control data, ensuring cleanliness and availability. He said 29% of companies feel their data is AI ready, underscoring the need for corporate-wide data policies and practices. Successful AI implementation involves experimenting with clear use cases and striving for optimal data quality and utilization.
Tom Davenport, Professor and Author, 'Evolving Role of Data and Technology Leaders'
Tom Davenport addressed the evolving roles of data and technology leaders, emphasizing the proliferation of C-suite titles and the complexities they introduce. He noted that the timing and emergence of various tech-related C-suite roles, saying the number of roles has led to confusion. Despite 81% of respondents claiming to have some understanding of these roles, 87% admit to being somewhat confused about who to approach for specific issues, with 30% sharing they are clueless.
Another critical issue involves the ownership over GenAI initiatives. Davenport’s research shows a tie for ownership between CIOs and CDOs. Davenport stressed, however, that collaboration is essential to successful implementation. He highlighted the need for strategic planning and deployment planning. Another significant issue is the desire for C-suite tech leaders to report directly to the CEO — which often doesn’t occur in reality. Davenport suggested this misalignment calls for a unified tech leader who reports to the CEO. The goal being to streamline leadership and ensure a cohesive strategy and implementation at the executive level.
Ashish Verma, CDAO at Deloitte, 'CDAO or the Future: Navigating AI'
Ashish Verma stressed that CDOs need to embed data into the corporate DNA and modernize the data architecture. This process involves establishing a compelling vision, measuring value, ensuring accountability, building talent and fostering a data product mindset. To leverage GenAI momentum, managing risks and effective communication are crucial.
Verma underscored here the need for ethical and trustworthy AI, supported by the right data and policies. Deloitte utilizes five types internally: public domain, external licensed, internal data, synthetic data and client data, each with specific rules. A policy engine manages these data sets and explains models, tying everything to a transformational agenda. Successful CDAOs, Verma said, have driven transformation, gained board approval and led significant changes — skills commonly found in Silicon Valley tech leaders.
Tom Redman, The Data Doc, 'All Roads Lead Through Data Quality'
Tom Redman likened poor data quality to dragging a wet bag of cement into your house — it is heavy, cumbersome and unwieldy. Redman asserted that the current state of data quality is dire, with only 3% of data sets meeting basic standards and just 16% of leaders trusting their company’s data. This poses a significant challenge for CDOs, who often misdiagnose the issue as a technical problem rather than a management one.
Redman emphasized that improving data quality requires connecting data creators with data consumers and fostering a culture of accountability and awareness. Everyone in the organization, he said, creates and uses data, and by this process, everyone impacts coworkers down the line. To address this problem, Redman advocated for the creation of internal data ambassadors from every function: individuals who are courageous, open-minded and understand the broader implications of data quality.
The process should begin with a baseline assessment, identifying errors and pinpointing where business processes fail. Raising awareness, training staff and determining the necessary level of data quality are crucial steps. Only after these foundational steps should organizations consider data quality solutions. Otherwise, the initiatives are likely to fail. Redman pointed out that 90% of data problems stem from human error, which can be resolved by cultivating a strong data culture and effective change management. He concluded that AI should be used to address the right problems, with longevity in mind, and technical issues should be tackled only after resolving people-related challenges.
Panel: 'Data Readiness for GenAI'
This session featured former CDOs now working as consultants, including Tony Cyriac (formerly CDO at Charles Schwab, now at Accenture), Mark Ramsey, Saurabh Gupta and Derek Strauss. The discussion highlighted how the CDAO role was traditionally focused on structured data. Without question, this changed with the advent of GenAI.
Despite this evolution, data quality remains a significant challenge, affecting the accuracy and reliability of GenAI copilot applications. The inconsistency in input data poses problems, creating pressure on CDOs to deliver quality outcomes. Historically, CDOs have been considered secondary within organizations, but GenAI has increased top-down pressure to demonstrate their value.
Davenport felt the concept of data products and the importance of reporting to top executives are potential solutions. For instance, JPMorgan Chase decided to have the CDAO report directly to CEO Jamie Dimon, who emphasized the role's significance in the corporate annual report. This reflects the growing recognition of data's strategic importance. Preparing data for GenAI is crucial, and it can involve cultural shifts within organizations. A key challenge involves educating people on interpreting data outputs from large language models (LLMs), which requires comprehensive training on the capabilities and limitations of these technologies.
Panel: 'Using AI to Transform the IRS'
This panel included several members: Melanie Krause, COO of the IRS; Reza Rashidi, director of data management, IRS; and Maya Bretzius, strategic advisor, IRS. Honestly, it was amazing to have the COO of the IRS attend the event. The IRS is responsible for 98% of the government’s funding. Yet, it has seen a 22% decrease in funding, turning it into a political football. The funding cut has resulted in lower audit rates and deteriorated customer experience. In response, the IRS is leveraging machine learning (ML), AI and generative AI to transform its data capabilities and operations.
To create low-risk innovation, the IRS has established a project team, a PMO, a governance function and a governance board to oversee and manage data risks associated with AI models. The organization’s goal has been to enhance the entire life cycle of customer interactions, including addressing issues with tax returns without human intervention. A comprehensive impact assessment and data rights evaluation are underway to ensure safe and ethical use of data. The IRS is pursuing transformative use cases for AI, particularly with unstructured data. These will improve taxpayer interfaces, ensuring compliance, fraud detection and auditing, with generative AI playing a significant role.
The IRS aims for AI to augment its operations, providing recommendations and nudging taxpayers towards compliance while reducing the need for audits. A key initiative, Taxpayer 360, is designed to integrate historic data and provide a comprehensive view to improve customer experience. More on this at CMSWire soon. Ultimately, AI is seen as a transformative tool by the IRS to preemptively address issues, enhance accuracy and streamline interactions with taxpayers.
Tom Godden, AWS Enterprise Strategist, 'How to Go From Idea to Value With GenAI'
Tom Godden emphasized the need for CIOs and CDOs to remove the constraints that limit their organization’s imagination. He argued smart organizations aim to be AI-based, starting by determining what is possible. Interestingly, he shared in passing that the compute cost of model fine-tuning is the second-largest GenAI expense.
Godden demonstrated some practical GenAI applications for marketing and the factory floor. He stressed the importance of mastering data collection, cleaning, annotation, versioning and lineage. This includes treating data as a product. Godden also said GenAI should be seen as a tool for optimizing and automating work, not replacing people. Smart organizations use it to simplify processes, prepare employees for new roles and establish AI academies for upskilling. They will also recognize there are challenges, like data controls, context, security and compliance, to create responsible AI products. Finally, they do not push risk levels too high.
Panel: 'CDO Organization: Centralization Versus Decentralization'
Adam Lundberg, CDO at Total Wine and More, is spearheading the shift from a decentralized to a centralized data management approach. His strategy recognizes that the analytics team possesses specialized resources not found elsewhere in the business. A key priority with the strategy is to address data ownership issues, with the belief that a central team is best suited to tackle the challenges effectively.
Nghi Ho, with Gilead Sciences, is in the process of creating a comprehensive data and analytics strategy. This involves assessing the data capabilities of the current team and then crafting a suitable strategy. Ho advocated for a domain/business unit strategy that enables business units to self-serve, while explicitly excluding data governance from this model.
Interestingly, it was suggested that strategy here largely depends on a firm’s operating model as was suggested by the authors of the book “Enterprise Architecture as Strategy.”
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Panel: 'CDO Certification'
The CDO Certificate’s first cohort emphasized the importance of CDO certification. Dean Pickett with the State of Ohio and Ashish Bajpai with John Deere shared their experiences. They underscored the importance of strategy and change management, noting the value of early business involvement in strategic initiatives. An example for John Deere is "See and Spray," which was reviewed as part of the “Fusion Strategy.”
Today's CDOs, much like CIOs, need to be outcome-focused while bringing fresh perspectives to their jobs. The certification process helps CDOs better align their strategies with desired outcomes, ensuring they contribute effectively to their organizations. The focus on outcomes is crucial in navigating the evolving data management landscape. Anita Hyde with Cognizant added that CDOs are trailblazers in the modern business environment. The challenge, she claimed, lies in executing their responsibilities effectively in the present, ensuring they not only keep up with, but also drive innovation and transformation for their organizations. This certification is critical to equipping CDOs with the knowledge and skills to succeed in these demanding roles.
Maceij Szpakowski, Co-Founder of Prophecy, 'Unlocking Data for AI'
Maceij Szpakowski emphasized the importance of data quality in AI, quoting Tom Redman: "If your data is bad, your machine learning is useless." He highlighted that GenAI can exacerbate problems by making erroneous conclusions faster when based on poor data. Bad data, according to Szpakowski, is akin to manure — it needs proper handling to become useful.
GenAI marks, he said, a fundamental change in computing serving as a transformative user interface. However, like traditional machine learning, its efficacy hinges on data quality and maturity. Szpakowski cited the Texas Rangers as an example of how leveraging data, akin to the "Moneyball" approach in baseball, can transform business models. For organizations to harness the full potential of GenAI, it is imperative to move data to the cloud for comprehensive analysis.
Furthermore, Szpakowski stressed the need for transparent data pipelines and the creation of visual and transparent data aggregates. This transparency and visibility are essential for effectively sharing and utilizing data across organizations, ensuring that AI applications are built on solid, reliable data foundations.
Parting Words
CDOIQ has become a key program for new chief data officers over the years. As Ashish Verma with Deloitte emphasized, embedding AI into corporate DNA is crucial. However, organizations must also address GenAI risks through robust data governance, corporate-wide policies and comprehensive training. Additionally, fostering a culture of data accountability and appointing a unified tech leader are essential to navigate the growing complexity of C-suite roles.
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