> For the complete documentation index, see [llms.txt](https://data-tiles.gitbook.io/latttice-how-to-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://data-tiles.gitbook.io/latttice-how-to-guide/introduction.md). # Introduction ## Enabling the Conversation We believe the data challenges of **complexity, time, and cost** are increasingly rendering businesses ineffective, preventing them from unlocking the full potential of their data. Modern organizations struggle with siloed data, lengthy engineering backlogs, and expensive infrastructures that fail to deliver value fast enough to meet business demands. To address these challenges, we’ve created **Latttice**, an agentic AI data mesh solution that redefines how teams access, share, and work with data. Latttice empowers users to create data products on their terms, bypassing traditional bottlenecks by enabling **self-serve data access** and fostering **collaboration** without dependency on centralized data engineering teams. It provides a unified platform for **discovering, creating, and collaborating on data products**, making it possible for anyone, regardless of technical expertise to unlock insights and drive innovation. With Latttice, organizations can democratize data, break down silos, and create a culture of shared responsibility for data, enabling people to work with anyone, anywhere, to achieve meaningful business outcomes faster and at lower cost. {% hint style="info" %} You can read more about [Latttice's Mission](https://data-tiles.com/about) on our dedicated website. {% endhint %} ## Treating Data as a Product Treating data as a product means shifting the mindset around data from being a byproduct of operations to a core asset that is intentionally designed, maintained, and consumed like any other product. It involves focusing on the **end-users of the data**, ensuring it is high-quality, well-documented, discoverable, and easy to access. Just like a product has a clear purpose and value for its users, data products are built with specific use cases and outcomes in mind, backed by strong ownership, governance, and iterative improvement. This approach not only improves the usability and trustworthiness of data but also fosters collaboration across teams, making data a reliable and reusable asset that drives business innovation and decision-making. ## Why is this important? Treating data as a product is important because it fundamentally transforms how organizations extract value from their data. In traditional approaches, data is often fragmented, poorly governed, and difficult to access, leading to inefficiencies, distrust, and missed opportunities. By treating data as a product, organizations create a structured, user-centric approach that ensures data is reliable, accessible, and actionable for the people who need it most. This shift enables better decision-making, reduces dependency on centralized data teams, and allows businesses to scale their data operations efficiently. Ultimately, it fosters a culture where data is seen as a strategic asset, driving innovation, competitive advantage, and long-term growth. ## So why Latttice? Achieving the principles of treating data as a product is notoriously difficult without a solution like **Latttice** due to the inherent challenges in modern data ecosystems. Traditional architectures often rely on centralized data teams and monolithic platforms, which lead to bottlenecks, slow delivery times, and limited scalability. Ensuring data quality, discoverability, and accessibility across distributed teams without creating silos or duplications is complex and resource-intensive. Additionally, the lack of standardized governance and self-serve infrastructure makes it challenging for non-technical users to collaborate effectively on data products. Latttice solves these challenges by enabling a decentralized yet unified data mesh framework, empowering teams to manage, share, and consume data products with built-in governance and automation, removing the need for manual, time-consuming processes. ## Annotations To help you get quickly up to speed with the terminology used in Latttice, we have provided explanatory notes on select words and phrases throughout the Guide. You will find these annotations marked with a dotted underline. ## Other ways to Learn The How to Guides are just one part of Latttice's learning system. We offer additional ways for you to learn about Latttice including our YouTube channel (@DataTiles), our demo sandbox environment and regular blog posts. You can also reach out to us via Slack or email at . Look for the header *Other ways to learn* in articles to find links to other relevant resources you may find helpful. --- # Agent Instructions This documentation is published with GitBook. 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