Data scientists spend a majority of their time completing automated tasks: cleaning, transforming, and locating their data. With machine learning proving that it has the credentials to take on these responsibilities, where should they really be redirecting their focus?
At our last meetup, Daniel Gray, Senior Director of Corporate Sales Engineering at AtScale, talked all things BI, but more importantly shared his thoughts on loose definitions. What are the risks and just how far can they set your organization back? Gray believes that if your business intelligence team and your machine learning team are working on the same projects with different definitions, you won’t get the results that you want as “the chances that everyone throughout the entire organization defines the metrics exactly the same way is unlikely to happen.”
So, where is the middle ground? The solution lies within the semantic layer.
Gray explains that with a semantic layer, “you want to build your dimensions and hierarchies one single time, and inherit those in all of your different BI tools.” As this “will allow you to have governance and you won’t have mismatched data at the end of your cycles.” By exposing the semantic layer, you are empowering your organization to create an abstraction between your data warehouse lakes and the people that are consuming that information, enabling your team to maximize results.
Using these tips, how are you going to elevate your organization?