Building a data science pipeline: Benefits, cautions

A data science development pipeline is critical for digital business. But the sequence of the pipeline must be monitored closely to ensure the output reflects the business goal. Enterprises are adopting data science pipelines for artificial intelligence, machine learning and plain old statistics. A data science pipeline -- a sequence of actions for processing data -- will help companies be more competitive in a digital, fast-moving economy. Before CIOs take this approach, however, it's important to consider some of the key differences between data science development workflows and traditional application development workflows. Data science development pipelines used for building predictive and data science models are inherently experimental and don't always pan out in the same way as other software development processes, such as Agile and DevOps. Because data science models break and lose accuracy in different ways than traditional IT apps do, a data science pipeline needs to be scrutinized to assure the model reflects what the business is hoping to achieve.