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Companies are scrambling to build their artificial intelligence (AI) arsenal, machine learning (ML) capabilities are evolving, and organizations are employing several robust third-party plug-and-play AI solutions (such as Microsoft’s Azure Machine Learning, Amazon’s SageMaker, and Google’s TensorFlow).
However, companies seem to overlook a significant differentiator — „Good Data.“ Whenever I interact with an infant AI team, I see behind-the-scenes data-panic. Even though businesses have tons of data, the question to ask yourself is: Do you have good data? For implementing or adopting ML into your products, good or useful data is your significant differentiator. Many organizations do not realize it because good data is uncommon.
Traditionally, businesses collected data about „things and money.“ Today, it is different. Most valuable businesses now sell software and networks, not just physical goods and capital assets. Even manufacturing companies such as Whirlpool selling washing machines and microwaves offer apps and platforms that connect people.
