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Savvy CDAOs will jump at the chance to use AI to deliver business value but must hone strategy and governance, as well., whereby I agree to provide Gartner with my personal information, and understand that information will be transferred outside of mainland China and processed by Gartner group companies and other legitimate processing parties and to be contacted by Gartner group companies via internet, mobile/telephone and email, for the purposes of sales, marketing and research.
The rapid advancements in generative AI are reshaping strategic agendas and impacting the responsibilities of D&A leaders and their functions in diverse ways. Download this research to learn how D&A leaders are shaping GenAI initiatives and the key challenges they face. Plus:The D&A leader’s success with AI hinges on delivering business value by integrating AI into the D&A strategy and prioritizing AI-ready data governance.AI initiatives are proliferating, often within a single organization in an uncoordinated and tactical manner. This is especially true of GenAI adoption. When one part of the organization is unaware of what another part is doing with AI, it can unwittingly create business risk and potentially destroy strategic business value. To prevent that, organizations need an The point here is not to shut down proliferating AI initiatives but create a context for orchestrating them. Given that the chief data and analytics officer is already responsible for many enablers of AI — including the data analytics and AI foundation, data governance and trust, data risk management, D&A ethics, analytical biases, data transparency, and parts of business-change-management through data and AI literacy — it follows that theEffectively framed, a data-driven AI strategy can inspire transformative thinking, orchestrate business outcomes, nurture informed decision making and define success. Yet too often, attempts to integrate AI into D&A strategies focus on high-level projects and projected budgets, while failing to identify how these AI initiatives will deliver value and by what metrics. Avoid this trap with a more holistic view that connects AI adoption to business goals and ultimately business outcomes, as follows: Align and map AI initiatives to the strategic business priorities of the organization; include metrics and KPIs for determining impact. Engage enterprise stakeholders by speaking in the language of finance and business rather than the language of AI and D&A. Understand the value levers and pain points of the organization and articulate how AI and D&A are delivering tangible value, linked directly to the business strategy. The sudden surge in interest around GenAI requires a specific strategic focus. Since this form of AI has widespread consumer application with a low barrier to adoption, teams can easily use it without organizational knowledge or oversight. Even in the case of official GenAI initiatives, CDAOs are rarely the sole owners. Since you are responsible for AI capabilities such as data science and the underlying data needed, however, you can take a strategic role of providing perspective on wheretoday and where it cannot. Plus, you can provide leadership with critical guidance on what is needed for success in terms of data and trust, risk management and governance. That enables the organization to focus on GenAI use cases that enable business value, and avoid getting distracted by hype and unreasonable expectations. To enable that perspective, provide additional details on GenAI initiatives separate from traditional AI that include:Identifying and prioritizing high-value GenAI use cases based on competitive impact, business value, urgency, cost and risk Building proof of value and cost, then delivering via a portfolio of GenAI investments, measuring their impact, learning and correcting Tracking emerging trends in GenAI to take advantage of the evolving value/cost advances as the market maturesis motivating ambitious CxOs to credibly argue that they should own AI in the organization. The CIO, CTO, chief digital officer, head of innovation, head of AI can all lay claim. Yet like AI itself, if explored in a reactive way, all this jockeying for ownership may be a distraction that slows the organization’s progress toward delivering value. CDAOs need to be in the mix and have a voice in the AI conversation. You may not want to or need to own AI, but you need to be part of the organization’s AI leadership coalition. Your participation is essential, given that you are already responsible for many of the key enablers of AI — including the fundamental need for AI-ready data, data governance, and data and analytics skills and upskilling. You can also be a stabilizing force, if you show AI leadership by emphasizing discipline and practices that you are in a unique position to spearhead. To keep the D&A organization central to the organization’s AI ambitions, CDAOs should take action in two key areas that differentiate you and your AI leadership:Seventy-four percent of CDAOs report that executive leadership has confidence in their D&A function, yet only 49% have established business-outcome-driven metrics that allow stakeholders to track D&A value. CDAOs may have been given a short-term honeymoon period, but that is over now. Without the ability to clearly tie D&A initiatives to value creation — including AI initiatives — CDAOs risk having their function dismantled and assimilated into the IT department, or into a data-heavy function.for D&A success, and most organizations have successfully matured governance in recent years. For example, 82% of Gartner D&A survey respondents say they can identify the data assets needed for new D&A projects, and 80% commonly share a data asset across more than one use case. Gaps remain, however, as it relates to value-oriented KPIs for D&A governance, which only 46% of respondents have. D&A capabilities and delivery models must evolve to support business innovation and AI.D&A programs with highly mature D&A governance are most likely to have adopted data-driven innovations. This runs counter to the common perception among business stakeholders that governance disciplines can hinder innovation. On the contrary, a lack of governance prevents organizations from realizing value from their AI initiatives. Gartner predicts that by 2027, 60% of organizations will fail to realize the expected value of their AI use cases due to incohesive ethical governance frameworks. One clear gap is in establishing value-oriented KPIs for D&A governance policies, practices and procedures, which only 46% of organizations have. AI-ready data governance requirements are also different from traditionalTo make data AI-ready, AI and D&A teams will need to be able to quickly identify data that is fit for use through three actions:Ensure the data meets the requirements across the life cycle of the use case, from designing and training to operating an AI model.Define the ongoing data governance requirements for the AI use case using parameters like data stewardship, data and AI standards and regulations, AI ethics requirements, and controlled inference and derivation.While D&A leaders strive to ensure its data governance enables AI-readiness, they must also respond to teams building AI that is data-centric — meaning, AI initiatives that prioritize engineering data as a path to building better AI systems rather than prioritizing refining and fine-tuning the algorithms or enhancing the code in AI models. For example, by 2025, synthetic data and transfer learning will reduce the volume of real data needed for AI by more than 50%.Data preparation mostly focuses around exploratory data analysis , cleansing and transformation to prepare high-quality structured datasets for feature extraction and engineering. Features add nuance or meaning to datasets for improving model performance and accuracy.These critical, time-consuming and resource-intensive tasks involve adding metadata to unstructured data to identify features for AI development.Already common in computer modeling and simulations, synthetic data has emerged as an important resource for AI development. It’s projected to overshadow real data in the future due to its ability to retain the statistical and behavioral aspects of real datasets while optimizing scarce data, mitigating bias or preserving data privacy.Augment internal data with domain-specific data from external data sources. Data enrichment tools can gather third-party data from the internet and organize, clean and aggregate the data from disparate sources.AI in data analytics offers numerous benefits, including improved accuracy, predictive capabilities and enhanced decision making. By investing in quality data, choosing the right tools, developing talent and addressing challenges, businesses can leverage AI to gain a competitive edge.
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