Data science is rapidly becoming a primary driver of competitive advantage for organizations. As a result, enterprises are hiring more data scientists and building more models in a quest to find business value in their data. And increasingly, they are recognizing that the greatest value comes from not merely using their data diagnostically, but in using it predictively to prescribe the next best action.
According to Domino, however, it is not enough to merely do data science. Organizations need to do data science right or risk unintended consequences that may put them in peril. The problem, as the company explains it, is that too many organizations attempt to approach data science the way they might any other technology project. But data science is not deterministic; it is experimental. And, in order for organizations to get data science right while simultaneously managing their growing data science investment, they need to change the way they approach it.
The company’s solution is a data science lifecycle management platform that helps organizations manage the process without disrupting the experimental nature that is critical to its success. Their platform begins at the ideation stage and progresses through research and experimentation, risk validation and finally through deployment where it helps control and monitor the model in production.
Using this lifecycle approach, the company believes that it can help foster the collaboration amongst data scientists and between data scientists and the rest of the organization that is required to scale enterprise efforts and create business advantage from them. More importantly, however, it believes that their approach helps organizations accomplish this without disrupting the creative process that is central to data science.
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