Systems | Development | Analytics | API | Testing

Enterprises are investing in data mesh initiatives to accelerate how decisions are made and to create novel experiences based on machine learning models. Similarly, enterprises are investing in API platform initiatives to productize business domains (or bounded contexts in domain-driven design parlance) as self-service digital assets that accelerate innovation and improve business agility. Both initiatives are typically run as separate work streams.

Data has become the lifeblood of modern businesses, and organizations are constantly looking for ways to extract more value from it. While there isn’t a one-size-fits-all solution for data management, organizations tend to take some common approaches. Two popular approaches to managing data are Data Mesh and Data Lake. Data meshes and data lakes have recently become popular strategies for groups that want to avoid silos so they can make data-driven decisions.

In today’s data-driven world, businesses must deal with complex challenges related to managing, integrating, and properly using massive amounts of data housed in multiple locations. Organizations that unlock the right data architectural approach empower themselves with much better decision-making and strategic insights. Two popular approaches — data mesh and data fabric — have surfaced as prominent and innovative solutions for handling data at scale.

This blog looks at some of the risks associated with data mesh and why organizations need to look at more than just the concepts of distributed data management to ensure successful data mesh. Companies need to evaluate the needs for managing their data products, data governance, the use of data platforms, and how business domains will be managed across the data ecosystem.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

Scania is at the forefront of a more autonomous, connected, electric future for the transportation industry. Find out why its Head of Data and Information Management uses data mesh—and Snowflake—to make it a reality. Scania is a global truck, bus, and industrial engine manufacturer and offers an extensive range of related services so its customers can focus on their core business.