Six Key Steps to Ensure Data Quality for Artificial Intelligence

As a growing number of companies are looking to build out and leverage artificial intelligence solutions across their organization, they’re often delayed due to poor data quality that exist across their business operations. This quality deficiency prevents them from proceeding with their intended AI rollout. Once AI is fully implemented, it can improve data quality throughout a company.

Being faced with data quality issues forces a company to shift priorities and resources from implementing AI to fixing these quality shortcomings before they can proceed. This means extensive time delays, allocation of resources, and a slow draining of the AI budget.

The magnitude of this problem is multiplied by the number of data sources a company possesses, and even more so when you consider the ever-growing volume of new data pouring in. By some measures, the amount of new data available to a company doubles every two years. Having an abundance of data is a strategic advantage and should be looked upon in this manner, and a data infrastructure should be in place to support all of it. One sure way to take advantage of all this data is with the application of AI.

To help address this challenge of having data quality in place that is suitable for AI, organizations should look to appropriately design or redesign its underlying data architecture. Following are a few key steps that can be taken.

Consolidating and integrating all corporate data into a centralized data hub provides a single platform for all data. This helps ensure one version of the truth and a consistent data home for all users throughout an organization regardless of department.

Having connectivity and a data exchange enables the retrieval of data in its raw form before cleansing. This allows for single connectivity to all data sources now and into the future.

By using a modern data warehouse (MDW), users can modify and enrich their data so that data issues can be resolved once.