Intro from the book
Data is the gold of the twenty-first century. Humanity went through the digital revolution in the 20th century, which allowed us to generate and utilize huge volumes of data from different business areas. Thus we’ve learned how to gather different types of data, such as sales, production, supply chain, etc. And now we have an opportunity to understand this data with Artificial Intelligence and make automatic data-driven decisions with immediate learning of a continuously changing environment. In this context arise a lot of opportunities to be more and more efficient and generate new streams.
For the first time in our history, we’re experiencing two simultaneous transitions, one being in the data, and the second transition towards accelerated computing. The first transition is occurring within data, pushed by development of AI. As enterprises grow, the volume of data they’re working with does the same. This continuous, never-ending growth can be tamed and turned into a tool with AI thanks to its ability to process, analyze, and learn from massive amounts of data. For it to happen the data itself needs to be in highest quality and backed by accelerated computing.
For AI to deliver its full potential, it requires a deep integration with the underlying data capabilities. This coexistence ensures that the AI system has access to the necessary data to learn, adapt, and make informed decisions. Without this intertwined relationship between data landscape and AI - all initiatives in the modern landscape are bound to fail.
This is not possible without a proper Data Architecture Platform with full integrity between Data, AI and Transactional Architecture. Enterprise Data is a very complex topic which has a very unique and difficult landscape for each enterprise. It’s hundreds and thousands of data sources with very complex and unique data structures. That makes the data transformation path a huge challenge for enterprises.
As of now there are a lot of architecture paradigms like Data Mesh, Lakehouse, Data Domain Architecture that promote specific approaches of data democratization within the transformation path. But such paradigms are not some specific guide or reference architecture that can solve such complex topics. It could only provide just some set of principles, but not an applicable solution/plan for the whole data & AI transformation path. Quite a large list of tools and technologies and vendors on Market results into Technical gap, locked into on some architecture level, but not actual possibility to enable comprehensive Data & AI Architecture view consistent with operation architecture and AI enablement. All of these points bring the next and next level of complexity that basically can result not in Data/AI Mesh but into Data/AI Mess for Enterprise.
That is why there is a big demand across enterprises and any company requires a Data & AI Transformation path. Having clear architecture concepts mapped to the exact processes and technological possibilities will significantly simplify the Data Transformation execution plan for the Enterprises. This book connects all dots of all steps and provides a full comprehensive picture of the Data & AI Transformation path in what it should result from a People, Process and Technology perspective. It shows principal new possibilities for enterprises that they can take from Data Assets. As of now it’s not even estimated and not reflected in the Balance sheet, but we don’t know yet the borders & bottom of Data & AI possibilities.
This book is for CEO, CIO, Chief Data&AI Officers, Data Managements teams, Data & AI Engineers, Business process owners, Data Owners, Stewards, Data&AI Engineers, all teams and everyone who is involved in DATA & AI transformation and in creation or consuming new types of Enterprise Products - Data Products. It provides full alignment for all stakeholders level across Enterprise.
The book shows how to build data transformation in Technical Architecture, Processes, Roles and Engagement frameworks, what are common mistakes and weaknesses and how to mitigate them. That will clearly state the definition of Data Product as a fundamental unit in the data ecosystem. The book will show Logical Architectures of Distributed Data Platforms and AI Platforms, its integrity with Operational Transactional Architecture to ensure Enterprise Architecture Consistency.
Our Message
In the modern landscape, any company that wants to succeed needs to be backed by quality data. Whether you want to get an insight into your operations or you want to kick off your AI initiative, none of this will be possible without a strong data platform. Your data platform is the foundation for every initiative in 2026, but building it is not as easy as it may seem. It requires a clear strategy and a deep understanding of how information flows through an organization.
Throughout our years of experience, we saw how a lot of initiatives to build a Data Mesh ended up leading to a Data Mess instead. This transition from a promising architecture to a disorganized tangle of sources is more common than most people realize. Usually there’s a multitude of factors playing into this initiative coming up short. Sometimes it is an incorrect approach from the beginning where the architecture does not match the company culture. Other times it is the lack of unity in vision across different departments or just simply not knowing the best practices for decentralized governance.
When these initiatives fail, they create technical debt and frustration that can stall a company for years. We have spent a long time working in the trenches and cruising through these exact issues with our clients. This hands-on experience allowed us to identify the patterns that lead to success and the traps that lead to failure. We decided to take all of those lessons and package them into our first official book, which is titled Data Mesh vs Data Mess.
Within this book, we cover the correct approach to implement a data platform for any organization. We focus on practical steps that work within any cloud environment, whether you are using AWS, Azure, or Google Cloud. We dive into how to maintain ownership, how to ensure data quality, and how to scale your infrastructure without losing control of the big picture. Our goal is to make sure your foundation is rock solid so that your AI and analytics goals can actually become a reality.
