Looking Glass 2026
In 2026 and beyond simply having a data platform will not position an enterprise to compete and grow — especially if that platform is a centralized lake that cannot adapt to AI-era demands. As pressure mounts to develop AI-enabled capabilities and products, organizations are learning that the data foundations they built over the past decade are no longer enough. To rebuild the core for the AI era, data ecosystems need to evolve into product-centric, federated environments that can supply trustworthy, real-time data to both humans and intelligent agents.
The goal is no longer a single platform to rule them all, but a dynamic, composable ecosystem ready for composable capabilities that turn modernized data, processes and logic into modular building blocks that teams and agents can reuse, combine and evolve as needs change. This ecosystem becomes an enterprise’s enabling layer for agentic systems, grounding them in high-quality data, governed access patterns and clear lineage.
A future-ready data platform encompasses an operating model as well as a technology stack, empowering business domains to create, govern and consume data products on a self-serve basis. Essential emerging features include data product discovery and onboarding, SLAs and quality metrics, lineage-aware ingestion and the golden paths that streamline product development. These foundations position the enterprise to rewire for agents, allowing AI systems to operate safely and at scale.
In this model, data becomes a network of domain-owned products, backed by strong governance and made ready at the edge through real-time processing. Teams can experiment and release AI-powered innovations quickly and securely. Rather than starting from scratch, developers draw on feature and model stores to support training and inference. Evals verify performance and reliability. Safe data access patterns ensure agents can act responsibly across the ecosystem.
A healthy data ecosystem unlocks AI for everything from modernizing the tech estate to reimagining processes and products for agentic workflows. With this network underpinning AI strategy, enterprises gain the speed, insight and productivity that define competitive advantage in the agentic era.
Key trends
- Data mesh 2.0: From principles to proven playbooks. Organizations formalize blueprints for domain onboarding, data product MLOps and federated governance. Expect standardized templates, data product lifecycle metrics and cost/showback as part of rebuilding core data capabilities.
- The composable data product platform. Platform teams assemble interchangeable capabilities (catalog, lineage, quality, transformations, streaming, privacy tooling) behind a coherent developer UX. The emphasis is on golden paths to reduce cognitive load, preparing the environment for agent-ready workflows.
- Federated computational governance. Policies (access, PII handling, retention, purpose limitation) are encoded and enforced automatically across pipelines, storage and consumption layers. Governance moves from committees to real-time controls and auditability.
- Synthetic, privacy-preserving and edge data. Synthetic data augments scarce or sensitive datasets; federated learning and edge processing reduce data movement and strengthen compliance. Platforms standardize the evaluation of utility vs. privacy risk.
- AI-ready data value chain. Data platforms integrate feature stores, model registries, eval suites and policy layers. Production AI becomes a first-class consumer of data products, strengthening feedback loops and rewiring for agentic decision making.
- Lakehouse and streamhouse interoperability. Teams converge on formats and table standards enabling ACID, time travel and streaming in the same substrate. Operational analytics and real-time ML become simpler and cheaper to operate.
- Autonomous data platforms. Platforms evolve toward self-optimizing systems that dynamically allocate storage, compute and governance controls based on workload behavior. These platforms learn from telemetry and can reconfigure themselves automatically, reducing maintenance and operational complexity.
- Data mesh and agentic AI fusion. Agentic AI systems begin acting as autonomous data stewards — monitoring lineage, evaluating data product quality and suggesting schema or governance improvements. This horizon marks a convergence of AI operations (AIOps) and data governance, leading toward self-healing data ecosystems.
- Neural knowledge graphs and semantic infrastructure. Emerging architectures use graph-based embeddings and vector databases to unify structured, semi-structured and unstructured data. These serve as foundations for context-aware, reasoning-capable AI and intelligent retrieval systems across enterprises.
Signals of this shift include
- A surging appetite for AI development and deployment. Whether from boards, investors, competitors or their employees, , organizations are facing unprecedented demands to integrate AI into workflows, and to develop and launch new AI-powered solutions. Investments are rising accordingly, yet data quality, governance and deciphering the complexity of agentic systems remain formidable challenges. This highlights the need for platforms that embed these capabilities in data and development lifecycles.
- The diffusion of centralized data platforms. The limitations of current-generation platforms based on data lakes are becoming more apparent, as they struggle to ingest and harmonize growing amounts of customer data from different sources, or to serve data to users at the speed required for innovation and experimentation. This is driving the evolution of centralized architectures into self-service platforms that empower individual business domains. The shift is not only technical but also organizational; research shows data and data teams being distributed throughout the organization and aligning more closely with different business functions.
- The maturity of data mesh adoption, with more enterprises moving from pilots to scaled implementations anchored in domain ownership and data products. The global market for data mesh — an architecture and operating model that decentralizes data resources and accelerates the development of data products — is forecast to nearly quadruple to over $4 billion by 2033 as more enterprises adopt AI-driven data management and real-time analytics.
- Data governance becoming ‘productized’ as it rises in importance. Adoption of data governance programs and policies has surged as awareness of the essential role data quality plays in AI initiatives grows and the high cost of governance failures becomes more apparent. Platforms are changing to incorporate features like policy-as-code, federated computational governance and privacy-by-design patterns as standard.
- The rise of edge and real-time data processing in platforms. The proliferation of connected devices and the replacement of traditional batch processing with data streaming means data can be processed faster and ever closer to the source. This has the potential to minimize latency and streamline the processing of workloads, with promising implications for autonomous systems and functions like predictive maintenance.
- Guardrails emerging around the data stack. With many leaders lacking confidence in the protection of AI data in their organizations, and a general redoubling of efforts to manage AI risks, more enterprises are looking to integrate evals, guardrails and observability for data and models in data platforms. Such features are also becoming standard issue in the platform solutions being rolled out commercially for capabilities like agentic AI.
Trends to watch
Adopt
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Unified governance addressing both traditional data management and AI-specific requirements.
Analyze
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AI assistance across the entire software development lifecycle: requirements, design, coding, testing and deployment.
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Using ML/AI to identify threats, detect anomalies and automate security operations.
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An ongoing assessment and prioritization of vulnerabilities based on real exploitability and business impact.
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Treating datasets as products with defined SLAs, documentation and consumer focus.
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Connected data flow from design through production and service for traceability.
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AI models fine-tuned for specific industries or functions, delivering higher accuracy than general-purpose models.
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Programmable Linux kernel instrumentation for deep observability without code changes.
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Integration patterns using events for loose coupling between enterprise applications.
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Cloud solutions tailored for specific industries with pre-built compliance, workflows and integrations.
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Low-power wide-area networks and satellite for remote IoT device connectivity.
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AI systems processing multiple data types (text, images, audio and video) simultaneously for richer understanding.
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Technologies enabling data use while protecting privacy: differential privacy, homomorphic encryption, etc.
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An abstraction layer that consistently defines business metrics. Particularly critical for AI accuracy per Gartner.
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Platforms analyzing development data to measure productivity, identify bottlenecks and optimize processes.
Anticipate
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Autonomous AI systems performing data analysis, generating insights and executing data tasks.
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Using AI to predict infrastructure needs and optimize resource allocation.
Adopt
Analyze
Anticipate
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AI systems capable of independently conducting scientific research, generating hypotheses, designing experiments and making Nobel-worthy discoveries.
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Engineering biological systems to produce materials, chemicals and medicines at industrial scale, potentially replacing 60% of physical production by 2040.
The opportunities
By getting ahead of the curve on this lens, organizations can:
Accelerate time to insight
By smoothing data ingestion, making high-quality data more accessible and facilitating analysis, a modern data platform can significantly increase the speed at which the enterprise is able to identify and act on emerging trends that present opportunities — or risks — for the business. Some of the most prominent examples of AI powered platforms acting as an accelerant are in drug discovery, where the time required to identify promising compounds is being slashed from months to just minutes.
Reduce regulatory and reputational risks
Low levels of consumer trust and growing regulatory scrutiny mean enterprises using AI in the development process or developing AI products need to act carefully. A platform that ensures data made available to development teams is carefully governed by default, and that activity takes place within defined guardrails, allows the enterprise to innovate in a safe and sustainable way.
Map out future scenarios to make better decisions
AI-ready platforms provide a basis for predictive modeling that can help the enterprise see through likely outcomes before deciding on the best course of action. It’s now possible to credibly simulate user behavior and responses to products prior to launch, highlighting areas for improvement or when a product may need to be reimagined altogether. These anticipatory abilities are driving tangible improvements in decision-making, with positive implications for product quality and cost structures.
Enhance resilience
Clear data lineage, consistent data quality signals and the automation of governance reduce the enterprise’s exposure to the challenges that accompany wider adoption of AI, from third-party risks to vulnerabilities in AI-generated code.
Shift from a project to a product operating model
The support provided by modern data platforms for data-driven decision making, the rapid testing and rollout of data products and the measurement of outcomes speeds the transition from a project-centric mindset, where products are developed to meet pre-defined requirements, to a product mindset, in which data products are oriented around end-user value. This paves the way for differentiation through customer-centricity and elevated customer experience.
What we’ve done
A recent engagement with a global medical device maker shows what a modern data platform makes possible in practice. Facing a legacy system that was slow to scale and costly to maintain, the company rebuilt its data foundations around a cloud-first, event-driven architecture. This shift replaced more than 500,000 lines of brittle legacy code with a streamlined, serverless platform capable of handling clinical workloads at volume.
The impact was immediate. Clinical reports that once took hours now run in under ten minutes, even at peak demand. Operational overhead dropped, data quality and consistency improved, and teams gained real-time visibility into device performance and patient outcomes.
These improvements did more than stabilize the platform. They created a data environment that can support more automated decisioning and workflow coordination over time. With data that is timely, trusted and accessible by design, the organization now has the technical footing to explore more adaptive, agent-supported capabilities when they choose to.
Actionable advice
Things to do (Adopt)
- When aiming to build a modern data platform, start with clearly defined domains and use cases, gradually extending these to encompass the organization. Avoid platform-first, ‘big-bang’ variety builds, which often struggle to scale or deliver value.
- Invest in developer experience for data teams, including robust ‘golden path’ templates, CI/CD, testing and observability. This will ease the path to adoption and position teams to make the most of the platform’s capabilities.
- Treat governance as code and product; measure it to ensure it is embedded in the platform and the data products being released.
- Align platform metrics to measurable indicators of business value such as lead time to new data product, user adoption, reliability SLOs or cost per insight to measure platform performance and make improvements where necessary.
Things to consider (Analyze)
- Introducing edge processing to potentially speed up time to insight, minimize the movement of data and strengthen compliance.
- Experimenting with synthetic data, which has the potential to enhance functions like market research by bridging the gaps inherent in limited or sensitive data sets.
- Exploring the fusion of data mesh and agentic AI systems. As agents are integrated into more enterprise workflows the opportunity will emerge to have them act as autonomous stewards of data, responsible for processes such as tracking data lineage, evaluating data product quality and flagging areas for governance improvement.
Things to watch for (Anticipate)
- Data platforms operating as self-regulating ecosystems, governed by policy-aware agents that negotiate access, generate synthetic datasets on demand and spin up ephemeral processing enclaves at the edge.
- More platforms orienting on the North Star of continuous value delivery: generating measurable business outcomes driven by safe, observable data products and powering adaptive agentic AI.
- Neural knowledge graphs emerging as a means to enhance flexible governance of modern data platforms, due to their capacity to effectively represent structured data and capture complex data relationships.