Let me clear the air; Mr. Magoo would not have made a very good data scientist. It wasn’t so much his stubborn nature; that might actually have been useful in a profession where selling insights can tax the will. His downfall would have been the blurry world he saw around him every day. He just never was able to pull a parking meter into enough focus to know it wasn’t someone unwilling to return his pleasantries. Clearly, Magoo would not be the ideal data scientist hire for any organization.
If for whatever reason your company did decide to hire Mr. Magoo as a data scientist, every data set would look the same to him. Similar to this:
- Like this picture, you would be forced to make assumptions as to what it is based off a blurry image. Can't figure out what it is? Find out here.
- The greatest value a data scientist can bring to an organization is the ability to tell a story through patterns in the data, and clear view of that data is often the difference between “The Odyssey” and “The Very Hungry Caterpillar”. When time is your enemy (it almost always is), a slow data warehouse can be detrimental to a data scientist if it can’t respond to the changes necessary to have a clear view of the data. So how can you give your organization 20/20 vision?
The answer lies in improved resolution through iterations. The number of iterations you can run before a decision-making deadline improves quality of the insights and often spells the difference between success and failure. In a recent poll of data scientists, nearly 100% felt that “rapid data integration” was critical to their ability to sharpen insights. But 85% of business and IT professionals say that business needs change too fast for IT to keep up. In other words, as the business finds trends in data that cause them to request additional sources to sharpen their insights, IT can’t deliver those fast enough. The decision makers’ collective vision is something less than 20/20.
- Magoo lacked the core qualities necessary to thrive as a data scientist, like an innate sense of curiosity, as influential data scientist Carla Gentry described to us during a recent webinar: Data Scientist: Your Must Have Business Investment NOW
The ability to rapidly integrate new data sources or update the data foundation supporting analytics is incredibly important because data scientists don’t really know where their analysis will lead until they start asking questions. They won’t know what questions follow the first until they see that first answer. Each time they introduce new data to answer a question it is an iteration. Every iteration brings their picture more into focus. A rapid data integration platform can result in 5-10X the number of iterations per decision cycle as compared to a traditional data warehouse. And that leads to sharper vision.
What is the moral of this story? There really isn’t one. Just a cold, hard fact. Traditional approaches to data integration and traditional data warehouses utilizing those timeworn methods will not deliver the 20/20 vision required to make confident decisions. They force business professionals, like Mr. Magoo, to step tentatively through a business world that won’t slow down enough to let them keep up.
Were you able to figure out what the blurry image above was? Chances are you wouldn’t be willing to bet your job on it. Why do the same with your data? (click here for a clear view of the image)
Oooh Magoo, you’ve done it again!
Did you find this story interesting? like or comment as 1 already did!