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Simplify, unify and connect all your data

Chapter 03
5 min read

Most organizations are dealing with an overwhelming volume of data from disparate sources. It’s often siloed and sprawled out across multiple clouds, data stores, locations and vendors — making data access time-consuming and complicated for data scientists, business analysts and other stakeholders.

One-third of organizations cite data complexity and silos as the top barrier to AI adoption.1
Graphic representing data points as cubes and spheres falling into open box
Solving how to simplify data access in a complex, dispersed environment is fundamental to building a strong foundation for AI.

For data to deliver high-value business insights, it must be accessible and contextualized by any user or application.

The challenges of data consolidation
Many organizations depend on outdated data architectures that attempt to consolidate disparate, siloed data into data warehouses or data lakes. Consolidation approaches that use extract, transform and load (ETL) procedures to copy data into a single data store are time-consuming and costly, adding complexity to the data landscape. And most organizations still end up with multiple repositories. As a result, data scientists struggle with lengthy data preparation cycles and difficulty organizing data to achieve a single view.

Unifying the data landscape for easier access and universal queries
A data fabric is the architectural answer to these challenges, helping businesses unify and simplify their information architecture for AI and empower their users with faster access to the business data they need.

A data fabric uses data virtualization to access disparate sources across an environment, so you no longer need to move data or create duplicate sets. Data virtualization provides a single, seamless view without copying data. As a result, a data fabric enables self-service access, so users can simply query the data where it resides. This capability gives data scientists and other data citizens faster access to information — no matter what platform or geography it’s located in. Self-service access also means users can start querying the data immediately, without waiting on a data engineer to find and prepare it first.

By 2023, organizations using data fabrics to dynamically connect, optimize and automate data management processes will reduce time to integrated data delivery by 30%.2

Another key element of a data fabric is the ability to perform universal queries with a query semantic layer that essentially abstracts or translates queries into a universal language. No matter what query engines your organization uses, a data fabric’s semantic layer makes it possible to query distributed data much faster than a standard data warehouse and with a higher degree of relevancy.

Simplifying, unifying and connecting data across complex, dispersed environments is critical to building a foundation for successful, timely AI initiatives in your business.

What is the main benefit of using data virtualization and a data fabric to help connect data silos?
Choose your option
Hexagon, triangle and circles connected to each other
Data virtualization allows direct access to disparate data sources without data movement; helping to reduce storage and replication costs.

1 Global AI Adoption Index 2021 (PDF, 5.9 MB), IBM and Morning Consult, 2021.
2 Magic Quadrant for Data Integration Tools, Gartner, 9 March 2021.