MyDataWork is taking aim at a less visible problem in enterprise analytics: organizations often have plenty of data tools, but lack a unified understanding of the work those tools produce. The company has now made MyDataWork for Teams available through AWS Marketplace, giving AWS customers a procurement path for a shared workspace designed to catalog analytical assets, connect them to business use cases and lineage, and prepare that context for AI-driven workflows.

For enterprise data and analytics teams, the challenge increasingly extends beyond finding data. They also need to understand the dashboards, spreadsheets, SQL queries, notebooks, models, workflows and reports built around that data—and how those assets connect to business decisions.

That is the problem MyDataWork is attempting to address with its "work-context layer." The company announced that MyDataWork for Teams is now available in AWS Marketplace, allowing organizations already purchasing through Amazon Web Services to acquire the team edition through an existing cloud procurement channel.

MyDataWork is not positioned as another business intelligence platform, data warehouse, ETL system or analytics engine. Instead, it sits above those systems and creates a metadata-oriented view of the analytical work surrounding them.

In practical terms, the platform catalogs assets and associates them with owners, stakeholders, use cases, lineage, dependencies, business value, risk and AI-readiness signals. The company says it works with metadata and work context rather than reading business data values, file contents or query results.

That distinction is central to the product's positioning. Rather than replacing tools such as Excel, Power BI, Python, Snowflake or dbt, MyDataWork is designed to provide a management and context layer across a potentially fragmented analytics estate.

For enterprise marketing organizations, the same concept could apply to a broader MarTech stack. Marketing teams increasingly depend on dashboards, customer data pipelines, campaign models, spreadsheets, attribution reports and automated workflows that can span dozens of platforms. Knowing that an asset exists is different from knowing who depends on it, what business decision it supports and what could break if it is changed.

MyDataWork's approach attempts to make that context explicit.

The timing is particularly relevant as companies move from AI experimentation toward more structured deployment. Gartner reported in February 2025 that 63% of surveyed organizations either lacked or were unsure whether they had the right data-management practices for AI. Gartner also predicted that through 2026, organizations would abandon 60% of AI projects that lack AI-ready data.

The problem is not simply whether an enterprise possesses enough data. AI systems also require reliable context around the information, processes and assets they are expected to use.

McKinsey's 2025 State of AI research points to a similar scaling problem. Nearly two-thirds of respondents said their organizations had not yet begun scaling AI across the enterprise, while 62% said they were at least experimenting with AI agents.

That creates an opening for products that help organizations understand existing analytical infrastructure before layering AI agents or automation on top of it.

MyDataWork's AI-readiness features are therefore less about generating content or replacing analysts and more about making organizational context accessible to approved AI systems. The AWS Marketplace listing describes read-only, metadata-only access for approved agents, while also offering tools for assessing analytical estates and identifying potential agentic use cases.

For enterprise buyers, procurement may be as important as the technology itself. AWS Marketplace gives companies already operating on AWS a familiar purchasing route and allows the MyDataWork team edition to be billed through the customer's AWS account. The current listing specifies an annual license for up to 20 users, a catalog capacity of 25,000 assets, 9,000 AI credits per month and a 14-day free trial.

Larger deployments can be handled through private offers.

The competitive landscape, however, is crowded. Salesforce, Microsoft, Google and Adobe all provide increasingly sophisticated combinations of data management, analytics, AI and workflow capabilities. Cloud data platforms and modern data catalogs also address portions of the governance and lineage problem.

MyDataWork's differentiation is narrower: it is attempting to organize the work product around analytics rather than become the underlying analytics infrastructure itself.

That could appeal to organizations with heterogeneous technology estates. A company does not necessarily need to standardize every BI, data engineering or productivity tool before creating a common inventory of the work those systems generate.

The limitation is equally clear. A metadata layer cannot replace the underlying governance, data quality, security and lineage capabilities required by large enterprises. Its value depends on how accurately the analytical estate is cataloged and how consistently teams maintain the surrounding business context.

For marketing and analytics leaders, that makes MyDataWork an interesting category to watch. As AI moves deeper into enterprise workflows, the next competitive battleground may not be another dashboard or generative AI assistant. It may be the contextual infrastructure that tells those systems what the organization is already doing, why it matters and what should be changed.

Market Landscape

Enterprise analytics has historically been organized around the systems that store, transform and visualize data. The rise of AI agents is shifting attention toward the context surrounding those systems.

Platforms from Microsoft, Google, Amazon, Salesforce and Adobe increasingly connect data, analytics, automation and AI. At the same time, data catalogs, governance platforms and observability tools compete to make enterprise data estates more understandable.

MyDataWork occupies a narrower position between these categories. Its focus is the analytical work itself: the assets teams create, the business use cases they support, the dependencies connecting them and the organizational knowledge that can otherwise remain trapped with individual employees.

That positioning could become more relevant as enterprises attempt to scale AI. McKinsey found that 88% of surveyed organizations reported using AI in at least one business function in 2025, but only 7% said AI had been fully scaled across their organizations.

The gap suggests that AI adoption is increasingly an infrastructure and workflow problem, not simply a model-selection problem.

Strategic Outlook

MyDataWork's AWS Marketplace launch does not fundamentally change the analytics market, but it gives the company a clearer route into organizations where cloud procurement and centralized data governance already influence purchasing decisions.

The larger opportunity is contextual infrastructure. If AI agents are eventually expected to navigate enterprise analytics environments, they will need more than access to raw data. They will need to understand ownership, dependencies, business purpose, risk and the relationships between analytical assets.

That makes the work-context layer a potentially useful complement to existing data catalogs, BI platforms and AI infrastructure—particularly for organizations operating across multiple vendors.

Top Insights

• MyDataWork for Teams adds a metadata-focused context layer for analytical assets, helping data leaders understand ownership, lineage, value and AI readiness.
• AWS Marketplace availability simplifies procurement for AWS customers, potentially reducing friction for teams moving from individual experimentation toward shared analytics governance.
• The platform deliberately avoids business data values and file contents, positioning metadata-only context as a governance-friendly foundation for enterprise AI agents.
• Gartner's AI-ready data research highlights a broader enterprise problem: organizations may be adopting AI faster than they are preparing the underlying data-management environment.
• As AI agents expand, tools that map analytical work, dependencies and business purpose could become increasingly important to marketing and analytics operations.

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