After my last blog on data monetization, I got several queries from customers, partners and colleagues. One common questions was, if we initiate building a data lake, how can we make sure the data lake is ready for advance data monetization use cases? I write from first-hand experience with my customers at different stages of their journey on building data lake. First and foremost, the journey starts from proper governance strategies which make the data lake a "trusted data lake." Next, moving from "trusted data lake" to data monetization involves a well-defined machine learning model and data science capabilities incorporated in data lake.

With the rapid evolution of technology, new tools for creativity and development are constantly emerging. Musicians today are beginning to use machine learning, where computers "learn" over time by being fed large amounts of data, to create music in new and innovative ways. The computers process this data and identify patterns, allowing them to act on future data. After identifying these patterns, computers can classify new information, make predictions, or even generate novel, creative content. In the world of music, the possible applications of this technology are endless.

After my last blog on data monetization, I got several queries from customers, partners and colleagues. One common questions was, if we initiate building a data lake, how can we make sure the data lake is ready for advance data monetization use cases? I write from first-hand experience with my customers at different stages of their journey on building data lake. First and foremost, the journey starts from proper governance strategies which make the data lake a "trusted data lake." Next, moving from "trusted data lake" to data monetization involves a well-defined machine learning model and data science capabilities incorporated in data lake.