Reflection on Previous Predictions: A Rearview Mirror for 2022 Data Stack Forecasts
In the ever-evolving world of data, 2022 saw significant strides in the adoption and implementation of the data mesh concept. This approach, centred around distributed ownership, domain-based design, data discoverability, and data product shipping standards, has been embraced by numerous companies.
The year witnessed a surge in the growth of data observability, although its future categorisation remains uncertain. Meanwhile, data-focused companies rebranded their services as the "ultimate data mesh platform", with Snowflake reporting record revenue and 67% year-over-year growth.
One of the key developments in the data mesh landscape was the emergence of two popular implementation theories: via team structures and via "data as a product". The focus on data quality, reliability, and discoverability, along with self-serve data platforms, empowered domain teams to handle data pipelines independently. However, maintaining robust standards and tooling was crucial to avoid fragmentation or chaos.
A hybrid architecture combining data mesh and data fabric also gained traction. This model, which leverages AI for optimising data flows between domains, has been successful in improving agility and reducing silos while maintaining centralised compliance and visibility.
However, the implementation of the data mesh remains vague and lacking in clarity and specificity. A strong data culture and governance were identified as critical success factors, with empowered data stewards, data contracts, and shared tooling essential to avoid inconsistencies, semantic misalignments, or governance gaps.
Integration with Master Data Management (MDM) and real-time processing was also crucial, particularly in sectors like healthcare. Effective data mesh deployments integrated MDM for consistent identity resolution and real-time processing tools to combat data latency and fragmentation, enhancing trust in decentralised data products.
Privacy, security, and regulatory frameworks posed challenges for decentralised data sharing. Many data mesh implementations under-implemented privacy-preserving computation and governance, highlighting new complexities requiring further tooling and policies.
Unexpected developments included the significant computational and operational complexity in real-time and multi-scale modeling scenarios. The necessity of tightly coupling data mesh with strong metadata management, AI-driven optimization, and automated governance to prevent decentralised chaos and ensure compliance was also highlighted.
Perhaps most surprisingly, organizations often merged data mesh with AI and machine learning for dynamic policy enforcement and intelligent monitoring, moving towards proactive data governance beyond traditional reactive compliance approaches.
Despite the progress, there hasn't been much progress on how data teams fundamentally operate - their culture, processes, etc. Zhamak Dehghani, the pioneer of the data mesh concept, published a book about the data mesh with O'Reilly. The 2023 Future of the Modern Data Stack Report is out, and the data mesh tooling stack is still premature.
Real user stories are growing on the Data Mesh Learning Community, providing valuable insights into the practical application of this concept. As we move forward, it's clear that successful data mesh adoption depends not only on decentralising data ownership but also on embedding strong governance, cultural change, integrated tooling (MDM, real-time processing), and hybrid architecture frameworks to manage complexity and compliance.
- The data mesh concept, with its focus on distributed ownership and data product shipping standards, has been adopted by various businesses, including those in the finance and wealth-management industry.
- In the realm of personal-finance and investing, data-driven companies are positioning themselves as the "ultimate data mesh platform," with Snowflake reporting record growth.
- The sports industry, too, benefits from the improved data mesh landscape, with a hybrid architecture combining data mesh and data fabric enhancing agility and reducing silos, thus improving team performance and decision-making.
- Apart from businesses, education-and-self-development platforms can leverage the data mesh concept to empower learners with self-serve data platforms and domain-based design for better understanding and exploration.
- However, the casino-and-gambling sector may face challenges in adopting the data mesh due to complexities in privacy, security, and regulatory frameworks for decentralised data sharing.
