Steps to Ensure Data Quality for Artificial Intelligence
Do you know how data quality is maintained, how data is consolidated? The question here is how the quality is maintained, upon which standards it has to; This can be solved by ensuring some steps with the help of Artificial Intelligence. Quality deficiency prevents them from proceeding with their intended AI rollout.
In process of maintaining data quality issues, it forces a company to shift priorities and resources from implementing AI. In order to improve the data quality in the companies, in order to improve the data quality, AI is working on extensive time delays, allocation of resources, and a slow draining of the AI budget.
Some basic measures, the amount of new data available to a company doubles every two years, a data infrastructure should be in place to support all of it and having an abundance of data is a strategic advantage and should be looked upon in this manner. One sure way to take advantage of all this data is with the application of AI.
Firstly consolidate your data by integrating all corporate data into a centralized data hub provides a single platform for all data which helps ensure one version of the truth
Secondly having connected to your data with a data exchange enables the retrieval of data in its raw form before cleansing.
Thirdly using a modern data warehouse, users can modify and enrich their data so that data issues can be resolved once.
Fourthly consider a semantic layer where data can be governed and prepared for any visualization tool
Finally automat data management procedures with the help of time-consuming, hand-written code are eliminated in favor of automation.
