Use Case #13: Quality Data

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Business AI applications require quality data. Without quality data, it will be garbage in garbage out, intelligent models can't help the actual business applications. Many enterprises face challenges in collecting and managing quality business data.

(1)
Many enterprise AI adoption programs list the availability of quality as one of the top challenges. The reasons are multifaceted, but a common theme leads to the so-called data silos, because departments do not share data due to integrity and interoperability issues. TSP is an excellent solution for data integrity between departments. It provides protection in critical questions like who owns data, auditing of data sharing, prevention of data leaks to unauthorized parties, accounting after data sharing.

TSP can help break down silos, enable accountability and scalable and adoptable.

(2)
Many businesses list lack of trust in data a major issue. If data is not pre-treated properly, businesses risk misinformation and privacy or right violations nightmares. TSP can help ensure trust and track proper management of required data management steps. Robust data governance requires a protocol like TSP.

For reference, see "Improving Data Quality in the Age of Generative AI", a white paper by Alteryx, Databricks, and CIO Dive.