Predicting the future of data in 2030 – the Data Voices Manifesto
What will the world of data look like in 2030? What will the role of data leaders be and how can AI usage and data consumption be successfully scaled? Read the first Data Voices Manifesto, created by data leaders from across the globe, to find out more.
Accelerating the adoption of AI and widening data consumption are strategic imperatives for all organizations. What will it take to move from strategy to successful implementation? Where are the biggest barriers – and what are the greatest opportunities for success?
To understand what the world of data and AI will look like in 2030, we turned to the Data Voices, a global community of data & Al leaders committed to making data more accessible, responsible, and impactful.
Together with Huwise, they have created the Data Voices Manifesto, six predictions for the future of data and AI in 2030, covering the key objectives driving the world of data, and how they can be successfully realized.
This blog outlines these strategic priorities at a top level, providing insight and best practice advice for all data and AI leaders as they look to turn data into value.
Understanding the future of data - challenges and opportunities
The Data Voices 2026 Manifesto is based on a mixed-method research approach carried out with data & AI leaders from large international organizations. Respondents shared their views on six key predictions regarding the future of data and AI within their organizations by 2030. For each prediction, they assessed the expected benefits, anticipated challenges, priority actions, and their level of confidence in achieving these predictions. This was supplemented by qualitative research involving in-depth interviews conducted with selected Data Voices members, providing additional nuance, practical illustrations, and deeper insights.
The six key predictions are:
- Prediction #1: “Data leaders will be recognized as experts and strategists creating value and building trust in AI”
- Prediction #2: “We will enable every employee to effortlessly leverage data to inform their decision-making and meet all their business needs”
- Prediction #3: “We will turn every piece of data into a product accessible to every employee”
- Prediction #4: “We will entrust as much of data engineering as possible to AI”
- Prediction #5: “We will make data quality excellent as standard, and a driver for AI”
- Prediction #6: “We will adopt agile and responsible data governance that becomes a competitive advantage”
Prediction #1: “Data leaders will be recognized as experts and strategists creating value and building trust in AI”
To deliver on this prediction, the Data Voices community believes that the CDO must evolve into a strategic leader within the organization, while reinforcing their role as a trusted expert in the age of AI.
A data leader as a business strategist and driver of value
Respondents are focused on working in partnership with the business, shown by the fact that 81% of them are prioritizing aligning each data and AI initiative with strategic business objectives.
However, the move to become more strategic faces significant organizational challenges. This starts at the top – nearly three-quarters (73%) of CDOs identify a lack of data literacy amongst boards as their biggest significant obstacle. This means CDOs are seen as technical, rather than strategic, partners to the organization, holding back progress.
Changing this narrative requires collaboration and education. 62% of respondents highlighted the creation of cross-functional data/business teams (62%) as central to creating business credibility, while 54% also emphasized the importance of CDOs being present in relevant senior management discussions.
"The data leader is no longer simply a technical expert who has been promoted. They are the backbone of a multidimensional transformation."
Prediction #2: “We will enable every employee to effortlessly leverage data to inform their decision-making and meet all their business needs”
Cultural change is at the heart of increasing data consumption and the Data Voices believe that successfully building a data culture requires strong executive sponsorship and large-scale employee training.
Data democratization to drive business benefits
Becoming a truly data-driven organization requires overcoming persistent challenges, and
in some cases, internal resistance. While boards and executive teams talk a lot about AI, and indirectly about data and its value, many leaders still remain unconvinced of their importance. Demonstrating this, 58% of respondents say they get limited support from their senior management when it comes to pushing the benefits of scaling and standardizing data culture across different departments.
Faced with the challenges of industrializing data usage, Data Voices are looking to move beyond traditional, technical approaches. 65% of respondents believe organizations should implement an e-commerce-style data marketplace in order to simplify and streamline access to data products across the enterprise. This needs to be backed up by continuous training
programs (also highlighted by 65% of respondents), raising awareness around proper data understanding and usage (58%), and appointing data champions within each department (58%).
"While senior managers consistently state that data is strategic, the reality is that they do not dedicate more time to it than they did five years ago. At best, culture is forced upon people through regulatory requirements; at worst, it becomes temporary and trend-driven through the hype surrounding AI. But AI can only deliver on its promises if it is built upon a sustainable data culture."
Prediction #3: “We will turn every piece of data into a product accessible to every employee”
The Data Voices believe that making data accessible to everyone requires a fundamental shift in how it is shared. They advocate breaking down silos, creating data products and making them available through data marketplaces to deliver accessibility and trust.
Embedding a product mindset
When it comes to data products, the Data Voices community sees challenges as being less about technology, and more about organization and culture. One of the strongest barriers concerns the lack of clear human ownership across the data product value chain (69%). A data product without a clearly identified owner is not truly a product—it is simply a dataset made available to others. As a direct consequence of these challenges, business teams still struggle to adopt a true product mindset (54%).
The Data Voices recommend adopting an action plan that combines governance, industrialization, and accountability. These three essential pillars for moving from concept to operational reality deliver clear, adaptable governance, usage at scale via data marketplaces, and automation to increase impact.
"At Coface, we make a clear distinction: a dedicated entity is responsible for external data monetization (buyer scores, country ratings, APIs), while my role within the Data Office is to industrialize internal data in order to break down silos."
Prediction #4: “We will entrust as much of data engineering as possible to AI”
Whether to scale improvements in data quality or free up the time of technical experts to innovate, applying AI to data engineering provides multiple major advantages. However, automation must remain closely supervised to ensure control.
Freeing up time while building trust
Given its advantages, Chief Data & AI Officers are naturally exploring AI’s potential within their own activities, including data engineering. The Data Voices see two primary benefits. First, the ability to massively improve data quality, notably through automatic anomaly detection,
outlier identification, and intelligent completion of missing data (77%). Secondly, AI helps drive innovation (73%), enabling experts to spend less time on repetitive engineering tasks and more time on high-value activities.
However, AI automation also brings potential issues. 65% of Data Voices highlight barriers linked to trusting a system capable of making autonomous optimization decisions, as well as the difficulty of measuring the ROI of AI-driven data engineering. This means that before automating, organizations must first prepare teams, strengthen trust, and establish robust governance.
"Without the ability to adjust, contextualize, and reinject business expertise, AI remains superficial. Sustainable performance depends on human-machine collaboration, never on technology in isolation."
Prediction #5: “We will make data quality excellent as standard, and a driver for AI”
AI reliability depends above all on data quality and maturity. Data must be AI-ready, going beyond the raw data to include context, semantics, and native interoperability with generative AI and autonomous agents.
Driving more reliable, higher-performing AI
According to 85% of respondents, the biggest strategic data quality challenge is in improving AI reliability and reducing bias and hallucinations through contextualized and certified data. Without high-quality, AI-ready data, it will not deliver benefits.
However, the Data Voices believe the right foundations are not yet in place. Two-thirds (65%) believe that data lacks semantic layers and contextual information, while 58% are concerned about insufficient data quality at a more basic level.
Based on this, the top priority for the Data Voices is to add contextual metadata to every data asset (69%), while 58% stress the importance of solving data quality issues before developing AI models or use cases. Clearly, making data quality the standard of excellence by 2030 requires a shift in mindset: moving from corrective quality to built-in quality, embedded from the design phase of every data asset.
"It is common to see two indicators diverge depending on whether you look at a point-in-time view or historical data. For end users, the verdict is immediate: the report is considered incorrect. The BI tool gets blamed even though the real issue lies deeper, in the source data and the lack of shared documentation.
Prediction #6: “We will adopt agile and responsible data governance that becomes a competitive advantage”
Traditionally data governance was seen as a restrictive, compliance-driven activity. Rather than acting as a constraint, the Data Voices believe it must evolve into a framework that protects and amplifies the impact of data and generates greater value.
From compliance to value creation
Data leaders increasingly see governance as a driver for greater data usage. 73% highlight the ability to precisely track the usage and impact of every data asset as its foundational benefit, while 65% value its importance in guaranteeing real-time data quality and security.
Transforming governance first requires a transformation in the way data is perceived and used. Cultural resistance to shifting from centralized control to distributed autonomy is identified as the main obstacle to achieving this vision (65%). Asking organizations to distribute authority, such as through adopting a data mesh approach (54%) often requires a major organizational mindset shift.
Transforming data governance into a competitive advantage means moving away from governance as a burden toward governance as a living system that supports, secures, and enhances every use of data.
"If the term “governance” still generates resistance, it is because it remains associated with a bureaucratic mindset that discourages operational teams. Instead, focusing on Business Data Ownership changes the perspective by putting responsibility back where value is actually created."
Preparing for 2030 - a manifesto for change
“We must invest in this cultural change effort now, because the digital divide will not only be about access to tools, but about the ability to understand and control them. Ultimately, future performance will come from a unique coupling of AI, internal data, and human excellence. This trio will form our true competitive differentiator.”
When Data Voices rank the six predictions in order of importance, a clear consensus emerges: the transformation of data by 2030 will be driven primarily by employee data literacy, data quality, and leadership.
Creating and spreading a data-driven culture across all employees is identified as the number one priority. This is not just another initiative: it enables all the others.
The second priority is AI-ready data, to deliver on the promised benefits of LLMs and agents. Without quality, there is no trustworthy AI.
The third priority focuses on the evolution of the CDO role. Once cultural change has begun and data quality is established, the next step is to demonstrate leadership at the highest level. Without a strategic data leader acting as a CEO partner and business value driver, data initiatives cannot fully scale.
Agile and ethical-by-design governance ranks fourth, reflecting that governance does not precede transformation, it accompanies it. The adoption of the data product model comes in fifth place, demonstrating the need to build strong foundations before moving forward in this area. Finally, AI-driven automation of data engineering ranks last, highlighting that delegating data engineering entirely to AI remains the most difficult prediction to deliver, as it depends on all the others being successfully achieved.
The Data Voices manifesto outlines a clear two-step approach. First, build the essential foundations: scaling human data literacy, ensuring data reliability and strengthening leadership. Only then can organizations deploy more ambitious transformations: distributed governance, self-service data products, and AI-driven automation.
FAQ
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Data Voices is a global community of data & Al leaders committed to making data more accessible, responsible, and impactful. They are united by a key belief that data only creates value when it is understood, used, and turned into better decisions. Curated by Huwise, Data Voices provides an ecosystem of more than 100 influential figures who are involved on an ongoing basis in designing, governing, sharing, and leveraging data within organizations to improve the day-to-day experience of all employees.
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A data product is a high-value, packaged data asset that provides users with everything they need to carry out a specific task. It is governed by a data contract, has an identified owner, and is continually improved based on user feedback.
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A data marketplace is a centralized data platform that makes all relevant data available through an intuitive, self-service experience that is based on e-commerce marketplace principles. This makes the discovery, access, and consumption of data products simple and seamless, without requiring training or technical skills. Capabilities such as AI-powered search and comprehensive metadata connect users easily with relevant data. Clear descriptions of data products and other data assets, including details of their owners, build trust and confidence, while security and governance is enforced through granular access controls.
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About the author
Lauréline Saux is passionate about the democratization of data and its impact on society. Through the content she writes, she analyzes the trends and challenges that impact the world of data.
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