Is poor data quality letting your AI down?

The most successful businesses in the future will be those that optimize their AI investment. As companies start their journey to AI readiness, they must develop robust data management strategies to handle increased data volume and complexity, and ensure trusted data is available for business use. Poor quality data is a burden for users trying to build reliable models to extrapolate insights for revenue-generating activities and better business outcomes.

It’s not unusual for business users to prioritize access to the data they need over its quality or usability. The simple truth is if an organization has bad quality data and uses it to feed AI tools, it will inevitably deliver poor quality and untrustworthy results.

Why data quality matters

Data quality is critical because it acts as the bridge between technical and business teams, enabling effective collaboration and maximizing the value derived from data. Depending on the data source and governance requirements, this presents a time-consuming challenge to data scientists who can spend up to 80 percent of their time just cleaning the data before they can even begin to work with it.

Amalgamating data sources is one huge task. The work of combining and transforming multiple data sets, such as raw data from regular business operations, legacy data in a variety of formats, or new data sets acquired following an acquisition or merger, should not be underestimated.

This is important work for business development purposes. Data is critical to better target marketing and sales, direct product innovation and market expansion, improve customer service, and even…