Best Practices in Data Science
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Data science is fundamentally different from other analytics technologies because it is prescriptive: it uses enterprise data to tell business users what to do, not what they have already done. It optimises key business processes, helping companies reduce costs, grow revenues, and manage risk. Given these benefits, many executives are eager to invest in data science.
But many companies fail to achieve success with data science. Many don’t recognize the need to
manage the data science lifecycle that spans data preparation, model design, deployment, and model
management. In addition, some business people refuse to trust models, preferring their own experience over a statistical output. Others have lost confidence in analytical models that contain errors or don’t deliver much business value.
However, there is a way to unlock the power of data science.
This report presents best practices to overcome ten common data science challenges.
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Data science is fundamentally different from other analytics technologies because it is prescriptive: it uses enterprise data to tell business users what to do, not what they have already done. It optimises key business processes, helping companies reduce costs, grow revenues, and manage risk. Given these benefits, many executives are eager to invest in data science.
But many companies fail to achieve success with data science. Many don’t recognize the need to
manage the data science lifecycle that spans data preparation, model design, deployment, and model
management. In addition, some business people refuse to trust models, preferring their own experience over a statistical output. Others have lost confidence in analytical models that contain errors or don’t deliver much business value.
However, there is a way to unlock the power of data science.
This report presents best practices to overcome ten common data science challenges.
Brought to you by: