Much has been written about the power of our massive data collections to enable the 360 view of our customers, our business, our employees, and our processes. When our numerous disparate heterogeneous data collections are aggregated and joined in our data lake or data cloud or data fabric or wherever, with appropriate data tagging, data discovery and data integration tools in place, then we can reach for that ideal: the 360 view of our domain! But is the "360 view" really the right goal? It is definitely a good target and we should incentivize productive work toward that ambition, but should we go all the way to achieving that full 360 view in all projects, at all times? Most of us have probably learned by now the truth in the statement "the perfect is the enemy of good enough."

In November, Tejasvi Addagada from Dattamza, published an article espousing strong Data Governance when undertaking AI related projects, so we thought it'd be helpful to co-publish a series of blogs relating this and similar topics. With significant emphasis on Machine Learning these days, we thought it would be valuable if we shared some data quality struggles that our clients face during Machine Learning efforts. Over the next five blogs we'll address challenges we see in the industry and most importantly we'll provide data quality related solutions. The first Data Quality challenge is most often the acquisition of right data for ML Enterprise Use cases. As any data scientist will tell you, developing the model is less complex than understanding and approaching the problem/use-case the right way.

There has been a lot of hype around machine learning lately. Over the past decades, we've heard about various concepts around machine intelligence that in most cases didn't get anywhere. But more and more frequently, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and there is a simple reason for that: according to Moore's law, the performance of microprocessors has increased since 1980 be a factor of more than 16 million! A program that ran on a 1980 computer for more than half a year today delivers its results in one second! That is why I think Machine Learning will be the story for 2017.

Artificial intelligence and thinking computers are a prominent plot device for Hollywood. Movies such as 2001: A Space Odyssey, Alien, and Terminator come to mind, as well as current day examples such as I, Robot, Ex Machina, and Transcendence. These pop culture examples underscore the fears and challenges we face as we attempt to program natural behavior and interact with intelligent technology. I think about this whenever I read the growing list of examples of how bots are becoming more pervasive, especially after seeing an article that proclaimed "the bots are taking over." In reality, bots do not carry such hyperbolic doomsday results.