Focusing priorities in data and analytics – recommendations from the Gartner Hype Cycle

Chief Data Officers (CDOs) and Chief Data & Analytics Officers (CDAOs) must therefore focus their investment and efforts in the right places to deliver both immediate and longer-term business benefits. 

However, with a growing range of technologies and solutions now available in the market, and ever-more complex IT environments, making the right choices can be difficult. Gartner’s Hype Cycles are designed to help with decision-making, providing an overview of technologies and their state of maturity, guiding prioritization and ensuring investment value.

Gartner has just released four Hype Cycles covering data management, data & analytics, smart cities, and banking. Huwise is listed as a sample vendor in all four, including in the Data Marketplaces and Exchanges (DMEs) category of the different Hype Cycles. This blog looks at the common themes across the four reports, as well as Gartner’s recent Market Overview for Data Marketplace Solutions, and outlines where CDAOs should invest their time and budgets.

Accelerating AI adoption is disrupting both business and wider society. Data management is no exception. Traditional practices and toolchains are being transformed, requiring new ways of working that incorporate AI into data teams. 

Rather than data simply being reliable and high-quality for human users, it must now be AI-ready. That means incorporating factors such as context and semantics to provide AI agents and models with the right inputs to create successful outputs.

Bringing AI and data together is critical in order to:

• Share data with AI (especially agents) so that it delivers value
• Automate data management through AI and make it easier for humans to discover and consume trusted data

These twin priorities are central to AI and data management success, but change can be difficult in a complex, fast-moving, business and technology landscape.

The Gartner Hype Cycle represents the different lifecycle stages a technology goes through from its initial development to its commercial availability and adoption, as well as its eventual decline and obsolescence. It maps human expectations against time and market maturity, and is split into five phases:

1. Technology Trigger. The very early stage of a technology, where there is more hype and prototypes rather than functional products. 
2. Peak of Inflated Expectations. Growing coverage of the technology and its potential applications, leading to increased enthusiasm and often unrealistic expectations of what it will do.
3. Trough of Disillusionment. Reality sets in, with hype replaced by a clearer understanding of the technology’s limitations, reducing enthusiasm and questioning its viability.
4. Slope of Enlightenment. More realistic expectations emerge about the technology’s capabilities, limitations and potential use cases, leading to more effective implementation in different scenarios
5. Plateau of Productivity. The technology matures and is adopted widely across the market. It delivers higher productivity and ROI due to it being used more effectively.  

Gartner has recently published: 

• Hype Cycle for Data Management, 2026
• Hype Cycle for Smart City Technologies and Solutions, 2026
• Hype Cycle for Data, Analytics, and AI Leaders and Programs, 2026
• Hype Cycle for Data and Analytics in Banking, 2026

It has also released its Market Overview for Data Marketplace Solutions, detailing the state of this sector it advances rapidly, with a particular focus on underpinning autonomous, agentic AI with reliable, consumable and contextualized data. 

While the Hype Cycles cover a range of sectors and use cases, each shares common themes that provide the foundation for success. To help CDOs and CDAOs, our Hype Cycle analysis has selected five key topics that span all reports and are applicable across all industries.

1 Agentic AI and AI-ready data

AI agents promise to transform operations, both across the organization and within data and IT. For example, data agents can monitor and act within the data stack, adapting and evolving pipeline activities, from quality management to coding. Within smart cities, agentic AI is able to make context-aware decisions and act with minimal human intervention in areas such as mobility, energy, public safety and public services. 

Agentic AI relies on AI-ready data, meaning CDAOs have to ensure that their data is reliable and able to be used within specific AI use cases. Many enterprises are learning from earlier AI-ready data pilot projects, and now looking to operationalize how they ensure that their data is AI-ready so that they can make it available at scale. For example, in banking, Gartner recommends that AI-ready data programs should be funded as enterprise capabilities that improve data reuse, explainability and build relevance and context.

2 Data products and data contracts 

Organizations have growing volumes of data, but often it is difficult for non-experts or AI agents to understand and benefit from it. For example, just 30% of banking executives surveyed by Gartner mentioned that their data is AI-ready.

Data products, high-value, ready to consume and continually improved data assets, are designed to overcome this challenge. They make information easily accessible to human, and now AI, users. Data contracts are integral to data products, governing their usage and providing guarantees and certification. This builds trust and drives confidence and consumption. 

Data products have progressed rapidly through Hype Cycles, for example moving from just past the peak to the Trough of Disillusionment in the latest Data Management report. This is due to organizations struggling with operationalizing data in a reusable, consumption-ready format. Gartner expects data products to mature rapidly, with an estimated two to five years to reach the Plateau of Productivity. It lists Huwise as a sample vendor for data products in its Banking Hype Cycle.

3 Data Marketplaces and Exchanges and Urban Data Exchanges 

Sharing reliable data across the organization and wider ecosystem is vital to deliver value. Data Marketplaces and Exchanges (DMEs) and their smart city equivalent, Urban Data Exchanges (UDEs) enable this sharing, providing a centralized, accessible, self-service source of validated information. By combining an e-commerce style experience and AI features they enable anyone to discover, access and consume data, without requiring technical skills or support. DMEs are essential to the sharing of data products, providing a marketplace where they can be found by both humans and AI agents. Huwise is listed as a sample vendor in the DME/UDE category in all four Hype Cycles.

The Gartner Hype Cycle represents the different lifecycle stages a technology goes through from its initial development to its commercial availability and adoption, as well as its eventual decline and obsolescence. It maps human expectations against time and market maturity, and is split into five phases:

1. Technology Trigger. The very early stage of a technology, where there is more hype and prototypes rather than functional products. 
2. Peak of Inflated Expectations. Growing coverage of the technology and its potential applications, leading to increased enthusiasm and often unrealistic expectations of what it will do.
3. Trough of Disillusionment. Reality sets in, with hype replaced by a clearer understanding of the technology’s limitations, reducing enthusiasm and questioning its viability.
4. Slope of Enlightenment. More realistic expectations emerge about the technology’s capabilities, limitations and potential use cases, leading to more effective implementation in different scenarios
5. Plateau of Productivity. The technology matures and is adopted widely across the market. It delivers higher productivity and ROI due to it being used more effectively.  

Gartner has recently published: 

• Hype Cycle for Data Management, 2026
• Hype Cycle for Smart City Technologies and Solutions, 2026
• Hype Cycle for Data, Analytics, and AI Leaders and Programs, 2026
• Hype Cycle for Data and Analytics in Banking, 2026

It has also released its Market Overview for Data Marketplace Solutions, detailing the state of this sector it advances rapidly, with a particular focus on underpinning autonomous, agentic AI with reliable, consumable and contextualized data. 

While the Hype Cycles cover a range of sectors and use cases, each shares common themes that provide the foundation for success. To help CDOs and CDAOs, our Hype Cycle analysis has selected five key topics that span all reports and are applicable across all industries.

1 Agentic AI and AI-ready data

AI agents promise to transform operations, both across the organization and within data and IT. For example, data agents can monitor and act within the data stack, adapting and evolving pipeline activities, from quality management to coding. Within smart cities, agentic AI is able to make context-aware decisions and act with minimal human intervention in areas such as mobility, energy, public safety and public services. 

Agentic AI relies on AI-ready data, meaning CDAOs have to ensure that their data is reliable and able to be used within specific AI use cases. Many enterprises are learning from earlier AI-ready data pilot projects, and now looking to operationalize how they ensure that their data is AI-ready so that they can make it available at scale. For example, in banking, Gartner recommends that AI-ready data programs should be funded as enterprise capabilities that improve data reuse, explainability and build relevance and context.

2 Data products and data contracts 

Organizations have growing volumes of data, but often it is difficult for non-experts or AI agents to understand and benefit from it. For example, just 30% of banking executives surveyed by Gartner mentioned that their data is AI-ready.

Data products, high-value, ready to consume and continually improved data assets, are designed to overcome this challenge. They make information easily accessible to human, and now AI, users. Data contracts are integral to data products, governing their usage and providing guarantees and certification. This builds trust and drives confidence and consumption. 

Data products have progressed rapidly through Hype Cycles, for example moving from just past the peak to the Trough of Disillusionment in the latest Data Management report. This is due to organizations struggling with operationalizing data in a reusable, consumption-ready format. Gartner expects data products to mature rapidly, with an estimated two to five years to reach the Plateau of Productivity. It lists Huwise as a sample vendor for data products in its Banking Hype Cycle.

3 Data Marketplaces and Exchanges and Urban Data Exchanges 

Sharing reliable data across the organization and wider ecosystem is vital to deliver value. Data Marketplaces and Exchanges (DMEs) and their smart city equivalent, Urban Data Exchanges (UDEs) enable this sharing, providing a centralized, accessible, self-service source of validated information. By combining an e-commerce style experience and AI features they enable anyone to discover, access and consume data, without requiring technical skills or support. DMEs are essential to the sharing of data products, providing a marketplace where they can be found by both humans and AI agents. Huwise is listed as a sample vendor in the DME/UDE category in all four Hype Cycles.

Not only do data marketplaces turn data into value, they also increase efficiency by saving time for both data teams and end users. Data consumers don’t need to rely on data teams to answer their queries or spend time searching for or validating data, boosting productivity for all. Third-party data subscriptions can be added to the marketplace, reducing duplication and maximizing their usage.

4 Data and AI sovereignty (including sovereign cloud)

Digital sovereignty is no longer just about where data is stored or whether an organization uses a sovereign cloud. Instead, it has shifted to a fundamentally new paradigm: technological dependence as a business risk. This is accelerating as AI moves deeper into business operations, with control and decision-making shifting from the company itself to AI models, and the vendors that produce and train them. 

Essentially, relying on a small number of dominant vendors for core operations leaves organizations exposed. This is driving a rise in the importance of data and AI sovereignty to CDOs and CDAOs, which is also underpinned the need to protect data confidentiality and meet compliance requirements, particularly in sectors such as banking. Ensuring the right technology and management framework is in place is vital to maximize data value and minimize risk.   

5 The need for increased data sharing

Across all the Hype Cycles, there is a common focus on the business and organizational benefits of increasing data sharing with all employees and stakeholders. Data marketplaces and exchanges, and data products, are part of this, but need to be supported by cultural change and an understanding that data is an asset, rather than simply an output to manage.

Increasing data literacy amongst the wider company is an essential first step, encouraging everyone to access and use data within their daily working lives. Data storytelling that humanizes information and puts it in context is part of this, while AI-driven analytics automates complex analysis to democratize decision-making. 

Ensuring data is reliable and understandable also opens up new opportunities, with data monetization creating fresh revenue streams, while digital twins enable more detailed monitoring and predictive analytics around how cities or manufacturing supply chains operate.

While different sectors share common themes around the importance of increasing data consumption, specific Hype Cycle drivers vary:

Banking

Banks are entering a new phase of data and analytics modernization, shifting from fragmented initiatives toward governed, reusable and context‑rich data foundations capable of supporting generative AI and regulated analytics workflows. Adoption is being shaped by four pressures: rising AI demand for high‑quality, well‑described data; heightened regulatory and geopolitical requirements for sovereignty and privacy; a move toward reusable data products and semantic consistency; and business expectations for measurable outcomes in credit, fraud, customer experience, revenue and efficiency. To meet these pressures, banks must sequence investments that strengthen architecture, governance and observability before scaling advanced analytics.

Smart Cities

Smart cities in 2026 are reaching a pivotal stage where advanced data analytics and AI form the backbone of intelligent urban ecosystems. Cities are shifting toward seamless, digitally orchestrated citizen experiences powered by agentic AI and superapps, while operations centers increasingly rely on digital twins and machine learning to optimize planning, predict costs and improve service performance under tightening budgets. As these technologies mature beyond early hype, governance, transparency and digital sovereignty have become essential to maintaining citizen trust, ensuring regulatory compliance, enabling data sharing, and supporting responsible AI adoption.

Data & Analytics

CDAOs in 2026 are contending with an environment where AI innovations dominate the Hype Cycle, often diverting attention and investment away from core data and analytics (D&A) capabilities just as organizations accelerate GenAI adoption. Leaders must demonstrate the complementary nature of D&A and AI, showing how robust data management, governance and proprietary datasets amplify AI value, reduce failure and enable monetization, while addressing widening literacy gaps, governance misalignment, and cultural resistance. Success depends on reframing data management as essential to scaling AI, educating business leaders using recent AI successes and failures, monitoring vendors realistically, and investing in literacy programs that build a culture of responsible, insight‑driven innovation.

Data Management

In 2026 D&A leaders must navigate the fast‑moving opportunities and challenges created by generative AI, AI agents and emerging agentic capabilities, all of which demand stronger context layers and semantic understanding, while geopolitical instability heightens the need for data sovereignty, distributed transaction databases and resilient data foundations. With cloud deployments and AI‑infused capabilities becoming standard, D&A leaders face a strategic inflection point where they must evaluate new technologies on the Hype Cycle to determine when to adopt promising but immature innovations and when to scale those reaching mainstream maturity, ensuring their organizations balance experimentation with disciplined, value‑driven progression.

Huwise is listed as a sample vendor in all four Hype Cycles. Its data product marketplace technology enables users (human and AI) to easily discover, access, and consume high-quality data products and assets through self-service. Thanks to its intuitive e-commerce-style experience, users quickly connect with the right data for their needs, building trust and delivering ROI. 

The Huwise solution meets the business and technical requirements detailed in Gartner’s

Hype Cycles in five key ways:

Providing contextualized, reliable data for agentic AI

Built-in analytics and lineage capabilities within the Huwise solution mean data teams can understand which data is used, by whom, in which context. They can see where it has come from and any transformations or changes made during its journey. This usage intelligence signposts which data has value to the business for specific use cases, highlighting which data products that AI agents should trust and consume. 

Enabling organizations to easily create, share, maintain and collaborate around data products

The Huwise data marketplace provides a single, trusted and centralized source of data products for the business. Users can seamlessly discover and consume data products, providing feedback to data product owners that can be used to improve them. The data marketplace acts as a collaboration space that brings together employees across departments, data teams and product owners to work together and innovate through data.

Underpins data marketplaces and exchanges

Organizations increasingly recognize the need for a centralized source of data that can be shared across the business. The Huwise data marketplace provides rich functionality for DMEs, including an e-commerce-style experience, AI-powered search, the ability to personalize the user interface, and role-based security to ensure robust data governance.  

Supports data, AI, and digital sovereignty

From the outset, Huwise’s architecture has been designed to be cloud-provider-agnostic, vendor-agnostic and to provide AI model choice. It delivers flexibility and control over where data is stored and the downstream models or solutions that consume it, mitigating risk. Organizations can store data in line with business and regulatory requirements, underpinning data sovereignty and independence. 

Drives greater data sharing and data literacy

Building an effective data culture requires employees to be confident when consuming data, and to trust that they are accessing the right information for their needs. Through its e-commerce experience and features such as feedback, recommendations and AI discovery, the Huwise data marketplace creates this confidence. Data can also be easily exported to familiar tools, building literacy and ensuring data is an integral part of every employee’s working life. 

The data management function is undergoing unprecedented transformation, driven by the needs and opportunities that AI brings. Finite budgets and resources mean it is vital to invest in the right areas, both to meet immediate needs and for future success. Gartner’s Hype Cycles provide a clear guide to which technologies to focus on, highlighting how trends are changing over time. As outlined in the latest set of reports, data marketplaces provide the foundation to bring together data management and AI, accelerate agentic AI, and scale data consumption, delivering business benefits both now and in the future.