Chief data and analytics officers (CDAOs) are poised to be of increasing strategic importance to their organizations, but many are struggling to make headway, according to data presented last week by Gartner at the Gartner Data & Analytics Summit 2023.\n\nFewer than half (44%) of data and analytics leaders say their teams are effective in providing value to their organization. That\u2019s from a survey of 566 data and analytics leaders globally that Gartner conducted online from September to November 2022.\n\n\u201cIt was kind of an eye-opener that one-third of them felt they were not as effective as they could be,\u201d says Donna Medeiros, senior director analyst at Gartner. \u201cThere\u2019s so much going on, so many things they are compelled to do versus what they really want to do, know they need to do, know they need to prioritize. They\u2019re spending a lot of time on things like data quality, data management, things that might be tactical, helping with operational aspects of IT. But that\u2019s not helping move the value of the organization as a business forward.\u201d\n\nThe responsibilities of data and analytics leaders are many and varied: Sixty percent of respondents cited defining and implementing data and analytics strategy; 59% said oversight of data and analytics strategy was in their portfolio of responsibilities; 55% pointed to data and analytics governance; and 54% cited managing data-driven culture change.\n\nOrganizations are still investing in data and analytics functions. Respondents to the survey reported their organizations are increasing investment in data management (65%), data governance (63%), and advanced analytics (60%). The mean reported budget among respondents was $5.41 million, and 44% said their data and analytics teams increased in size over the past year.\n\nKey obstacles to data success\n\nDespite that increased investment, CDAOs say lack of resources and funding are among their top impediments to delivering results, with 13% citing it as their top obstacle and 29% listing resource constraints among their top three hurdles.\n\nThe top impediment? Skills and staff shortages. One in six (17%) survey respondents said talent was their biggest issue, while 39% listed it among their top three. And the tight talent pool isn\u2019t helping, Medeiros says. \u201cCDAOs must have a talent strategy that doesn\u2019t count on hiring data and analytics talent ready-made.\u201d\n\nTo counter this, CDAOs need to build a robust talent management strategy that includes education, training, and coaching for data-driven culture and data literacy, Medeiros says. That strategy must apply not only to the core data and analytics team but also the broader business and technology communities in the organization.\n\nOther obstacles to data and analytics success, according to Gartner, include:\n\n\u201cTheir life is very complex,\u201d Medeiros says of the current state of the CDAO role. \u201cThey have lots of areas of primary responsibility \u2014 implementing data and analytics strategy, oversight of data and analytics initiatives, creating and implementing information systems and data management \u2014 and the people side \u2014 workforce development, upskilling, making the organization data-driven, artificial intelligence, and centers of excellence. They\u2019ve got a lot of complexity and a lot of people they\u2019re answering to.\u201d\n\nThis lack of funding for data initiatives echoes the findings of Foundry\/CIO.com\u2019s 2022 Data & Analytics Study, which also found other digital transformation initiatives taking priority and lack of executive advocacy for data initiatives as other key roadblocks to data-driven success.\n\nWhat it takes to lead data strategy\n\nStrategic missteps in realizing data goals may signal an organizational issue at the C-level, with company leaders recognizing the importance of data and analytics but falling short on making the strategic changes and investments necessary for success. According to a 2022 study from Alation and Wakefield Research, 71% of data leaders said they were \u201cless than very confident\u201d that their company\u2019s leadership sees a link between investing in data and analytics and staying ahead of the competition.\n\nEven in the case where an organization taps a designated IT leader to helm data strategy, whether in a chief data officer or chief analytics officer role, the complexity of the role and how it interfaces with other business leaders needs to be addressed for success.\n\nMedeiros likens the CDAO role to a combination of three personas: an orchestra conductor, a composer, and a performer. The conductor looks across the organization and conducts how data and analytics is done, both across business lines with the help of domain experts, as well as in a centralized function. The composer creates and sells the storyline of the value of data and analytics. And sometimes, data leaders must be performers: helping to implement data management, data quality, data trust, spending time on data governance, compliance, and risk.\n\n\u201cThese three personas require juggling soft, people skills and technical savvy,\u201d Medeiros says, adding that \u201cthe CDAO serves multiple stakeholders across the organization and cannot operate in isolation. They need to align with organizational strategic priorities, collaborate and sell the overall vision and strategy for data and analytics, and get buy-in for their initiatives.\u201d\n\nThe most successful data leaders, according to Gartner\u2019s survey, outperformed their peers by projecting an executive presence while also building an agile and strategic data and analytics function capable of shaping data-driven business performance and operational excellence, Medeiros says. Gartner asked respondents to rate themselves across 17 executive leadership traits. There was a strong correlation between those leaders who said they were effective or very effective across those traits and those who reported high organizational and team performance. For example, 43% of top-performing data and analytics leaders said they were effective in committing time to their own professional development, versus only 19% of low performers.\n\nProminence matters\n\nHow CDAOs are positioned in the organization also impacts data and analytics success. According to Foundry\u2019s 2023 State of the CIO survey, 53% of chief data officers and 45% of chief analytics officers report to the CIO, while just 35% and 38% report to the CEO, respectively. Moreover, only 37% of CDOs and 25% of CAOs report having budgets separate from IT overall.\n\nMedeiros concedes that CDAOs who report to the CIO and sit within the IT function can still be effective, but, in general, the higher CDAOs sit in the org chart, the better, she says, as this gives them more visibility and better leverage to work on organizational goals.\n\n\u201cIt depends on their roles, responsibilities, and how much time they\u2019re allotted for what we call business enablement \u2014 not just enterprise IT but actually helping the organization do what matters,\u201d Medeiros says. \u201cIt can be things like cost efficiency, but it\u2019s also new products and services that data and analytics supports and can call out.\u201d\n\nIndeed, Rita Sallam, distinguished VP analyst at Gartner, says that by 2026 more than a quarter of Fortune 500 CDAOs will have become responsible for at least one data- and analytics-based product that becomes a top earner for their company.\n\nTo get there, though, Medeiros says CDAOs must prioritize strategy over tactics. While tactical elements such as data quality and data security are important, improving effectiveness relies on aligning the data and analytics function with organizational strategic priorities and selling the data and analytics vision to key influencers like the CEO, CIO, and CFO.\n\n\u201cMost CDAOs are delivering on immediate-term business goals, but for around half of CDAOs surveyed, delivery against goals for future-term growth and sustainability is lagging,\u201d Medeiros says.\n\nShe notes that the most successful data leaders are focusing on improving decision-making capabilities, monetization of data products, and cost optimization, as well as improving data literacy and fostering a data-driven culture.