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By 2026, 66% of data used as candidate master data will be identified as data fabric or data mesh. In this blog post, we will explain these two approaches to data management – focusing specifically on data fabric strategy development, best practices, and challenges – and how to identify what is right for your firm. For more on data mesh, please see Part I of this blog series.
Definitions: Data Mesh & Data Fabric
Data Mesh: Data mesh is an architectural framework that involves people, process, and technology with the goal to create easy access to data.
Data Fabric: Data fabric is a data management design that aims to reduce the time to integrated data delivery though active metadata-assisted automation.
Data Fabric Overview
Goals:
- Centralized, technology-driven data ownership
- Eliminate Data silos and improve access
- Provide a wholistic view of the business
- Improve data consistency and quality management
- Enable data governance at scale
Considerations:
- How large is your enterprise data? Do you need to combat data gravity (difficulty of moving data as it grows in size)?
- What is the current architectural state, are you ready to support data fabric (which is more technologically driven)?
- What is the current state of your data governance program?
Source: What is Data Fabric? Uses, Definition & Trends | Gartner
Common Mistakes and Challenges with Data Fabric
- Complexity: Implementing appropriate processing can be challenging across multiple data domains. Business teams may have differences in expected data item definitions and update requirements. For example, a Trading department may need Security Weight with a beginning of day view, while the Performance department may need end of day Security Weight to communicate results.
- Transformations: unifying disparate sources into appropriate formatting and synchronizing databases can be challenging across varying outputs, symbologies and primary keys.
- Trust: Missing, irrelevant, and inaccurate data can frustrate business users and limit adoption and overall trust of the data.
- Cost: Licensing enterprise access for multiple data domains can be cost prohibitive for many firms.
- Security: centralized data implies centralized risk. Organizations need to ensure data is protected through encryption, data masking and data lineage.
Snowflake as a Key Tool for Data Fabric
- Centralized, Scalable Platform: Provides data lakes, data warehousing, and data sharing under a single, integrated platform that tracks the lifecycle of your data and stores it in the cloud
- Secure Real-Time Data: Supports multi-cloud architectures across accounts, regions, and clouds within the Snowflake ecosystem
- AI Capabilities: Leverage open-source LLMs for data analytics through Snowflake’s Cortex to add unique value for business domains
Ready to Modernize Your Data Architecture?
Partner with Continuus to identify whether data mesh or data fabric is the right approach for your organization, and redefine what’s possible for your business. Contact us today to explore how we can help you stay ahead in an ever-evolving data landscape.
