If AI is the engine, data is the fuel. Artificial intelligence (AI) and predictive analytics are powering faster, better decisions in industries as diverse as vaccine development and viticulture. 

While its made great advancements, if the AI model is trained on subpar data, it will end up being more cumbersome than helpful - potentially causing a slew of issues for businesses, individuals and society. In this whitepaper, you'll learn common data mistakes companies make when adopting AI and present seven steps to get data right. 

By Sharath Tadepalli, Director, Data Science and Analytics, HGS

Key Takeaways:

  • Understand 7 key steps to get maximum business value from AI and analytics
  • Realize common data mistakes that lead to erroneous predictions

  • Uncover real-world examples of data driving  valuable outcomes