Artificial intelligence (AI) has excited many a business leader. The idea of predictive analytics helping to drive their organization’s efficiency and future is alluring. There are certainly benefits of an end-to-end data engineering approach to achieving clean data, but there are also challenges. Information Age brought this interesting information to our attention in their article, “The hardest part of AI & analytics is not AI, it’s data management.”
Fraud detection, next-best-action, operational efficiency and forecast analysis are all among the many business challenges that AI and analytics can help solve. However, bad data is currently hindering AI since machine learning models are only as good as the data that is input.
In essence, the key is to enhance the quality of data at the front end. Organizations should build a catalog of assets to inform decisions. Then you can start to bring data together and make it searchable. AI is needed in both the analytics portion and the back end to be successful.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
