{"id":2110,"date":"2019-12-03T02:22:17","date_gmt":"2019-12-03T02:22:17","guid":{"rendered":"http:\/\/kusuaks7\/?p=1715"},"modified":"2024-02-14T16:28:51","modified_gmt":"2024-02-14T16:28:51","slug":"how-to-run-effective-data-science-poc-in-7-steps","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/how-to-run-effective-data-science-poc-in-7-steps\/","title":{"rendered":"How to Run an Effective Data Science POC in 7 Steps"},"content":{"rendered":"\t\t
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Introduction<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tData Science projects are complex and always inherit the risk of not being feasible. As opposed to large-scale IT projects though, there are ways to quickly act on ideas in a sandbox environment. This allows for a fail-fast-approach and enables companies to sustainably allocate their resources towards those projects, which will reliably create value \u2014 may it be through the optimization of processes, enabling new services or increasing customer loyalty.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA proof of concept (POC)\u00a0is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. POCs should prove the larger value of a system, ensuring it\u2019s aligned with forwarding the company\u2019s longer-term strategic objectives.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhat does running a POC mean in practice specifically for data science? When it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that a system will provide widespread value to the company: that it\u2019s capable of bringing a data-driven perspective to a range of the business\u2019s strategic objectives.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFrom my internship and consultant experience along with my own research, the number of POCs executed by companies keeps increasing in the race to implement data science for competitive advantage. While the industry might indicate a very high \u201csuccess\u201d rate, the number of POCs that have successfully translated into production is not that obvious. I recently read\u00a0a very detailed white paper from Dataiku<\/a>, which lays out the 7 essential elements to keep the project on track for an efficient, effective, and most of all successful POC. I want to share these elements in this post as a way to raise more awareness about this issue for new data scientists entering the field.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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1 \u2014 Concrete Use Case<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe first, and possibly most important, step to running a successful POC is choosing a use case. Without this, a POC simply can\u2019t exist. To hone in on a use case for the POC, you start with a list of critical business issues from which to choose, possibly soliciting feedback and ideas from teams across the company for a variety of use cases. Then you can look at them and determine:\n