The goal of my "Demystifying" blog series is to provide a simple, informative explanation of common Data & AI themes.  I hope you find the format easy to follow, the information easy to consume, and the links to additional resources valuable.

Data Fabric Definition (n):

1. 1.  A data architecture with an integrated set of technologies and services designed to democratize data access across the enterprise at scale.
   2.  A data architecture that facilitates the end-to-end integration of various data pipelines and cloud environments using intelligent and automated systems.
   3.  An architectural approach with the goal of ensuring that quality data is accessible by the right people at the right time.

Challenges a Data Fabric addresses:

·       Within every organization data is:

• • Growing:  As data continues to grow at an exponential rate, many companies struggle to derive value from it and make data-driven decisions.
  • Everywhere:  Data silos are a significant barrier in any organization, locking away valuable insights from data consumers across the enterprise.
  • Unknown:  The lack of rich metadata impedes the organization from identifying, cataloging, and classifying business critical and sensitive data.
  • Inaccessible or unavailable:  Information that is not easily obtainable or difficult to access prevents the organization from unlocking valuable insights.

Benefits of a Data Fabric:

• • Intelligent integration: Unify data across various data types and endpoints and help to eliminate silos across data systems, centralize data governance practices, and improve overall data quality.  There’s no need to move or duplicate data when you can access enterprise data where it lives both on premises and in cloud.
  • Democratization of data: Data fabric architectures facilitate self-service applications, broadening the access of data beyond technical resources, to data engineers, developers, and data analytics teams across the organization.  Democratization enables data consumers to quickly find, prepare and use governed data, reducing time-to-market insight.
  • Better data protection: Data fabric architectures allow technical and security teams to implement data masking and encryption around sensitive and proprietary data, mitigating risks around data sharing and system breaches. 

IBM has divided the data fabric practice into six distinct capabilities. 

1. 1. Data governance:  Enable data discovery, data trust, data protection, and data consumption using a well-described catalog of assets.
   2. Data integration:  Streamline access to decentralized data and enable automated integration, ETL, and data orchestration.
   3. Master data management: Connect and match associated records to create accurate entities across multi-domain sources and determine relationships between the data records.
   4. Data lineage:  Enable data users to observe and trace different touch points along the data journey, allowing organizations to validate for accuracy and consistency and gain context about historical processes and even trace errors back to the root cause.
   5. Data observability:  Enable automated monitoring, triage alerting, tracking, comparisons, root cause analysis, logging, data lineage, and SLA tracking, all of which help practitioners understand end-to-end data quality—including data reliability.
   6. Data Products:  Access data products from across clouds, platforms, tools, and catalogs via an online shopping experience.

For more details about Data Fabric and IBM Data Fabric solutions visit this article on IBM.COM