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Ensure data quality and governance for AI

Chapter 04
5 min read

Making sure data can be quickly and easily accessed by data scientists is crucial for successful AI projects, but equally important is ensuring that the data they’re working with is relevant and of high quality. Given the enormous volume, variety and velocity of data enterprises are dealing with, they need effective tools for cataloging, tagging and organizing data, and governing access levels.

Organizations report approximately 90% or more of their time is spent preparing data for advanced analytics, data science and data engineering.1

According to IDC research, 80% of worldwide data will be unstructured by 2025, and difficulty analyzing semi-structured and unstructured data is a growing challenge to achieving the analytics at scale required for AI and ML.2 In addition, business users in different roles — from HR to data scientists to lines of business — need access to different data. Governance policies are needed to safely determine appropriate data entitlements in accordance with data privacy and regulations.

Contextualizing data and the use of data
Governance, data quality and data cataloging capabilities are at the heart of data fabric’s value. A data fabric automatically profiles data so you understand it in its current form and then classifies it to be fit for purpose — making it easier for people with different roles and experience levels to put organizational data to use.

The cataloging function of a data fabric offers several beneficial capabilities:

Interactive graphic showing falling dominoes

Detecting sensitive data for internal and external regulatory purposes

Creating enforcement policies to protect data through masking, redaction and substitution of values

Profiling data to make its initial form comprehensible to data consumers

Recommending data based on a user’s history and search patterns

Discovering data by inventorying assets and applying governance rules

Assigning business terms from your business taxonomy to new assets

These cataloging features solve many of the historically manual and time-consuming aspects of data preparation and management. As a result, data consumers can more quickly derive value from relevant organizational data to make informed decisions while adhering to appropriate governance policies.

Discover how global banking and financial services institute ING uses a data fabric in a hybrid cloud environment to improve data access and governance.

Accelerate the journey to AI
To take full advantage of the promise of AI, organizations need well-organized, trusted data that’s business-ready for analytics and AI model building. A data fabric introduces automation technologies that help solve many of the challenges and inefficiencies of data management — from access to preparation to governance — getting you one step closer to putting AI to work in your business.

Businesses report spending approximately 90% or more of their time doing the following.
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Preparing and analyzing data for optimization and validation
Nearly 90% of an organization’s time is spent preparing data for advanced analytics, data science and data engineering.