How do we deliver Data Science in the Enterprise


I’ve worked on Data Science projects and delivered Machine Learning models both in production code and more research type work at a few companies now. Some of these companies were around the Seed stage/ Series A stage and some are established companies listed on stock exchanges. The aim of this article is to simply share what I’ve learned — I don’t think I know everything. I think my audience consists of both managers and technical specialists who’ve just started working in the corporate world — perhaps after some years in Academia or in a Startup. My aim is to simply articulate some of the problems, and propose some solutions — and highlight the importance of culture in enabling data science.

I’ve been reflecting over the years as a practitioner why some of this ‘big data’ stuff is hard to do. I’ll present in this article a take that’s similar to some other commentary on the internet, so this won’t be unusual.

My views are inspired by in this article Matt says:

Big Data success is not about implementing one piece of technology (like Hadoop or anything else), but instead requires putting together an assembly line of technologies, people and processes. You need to capture data, store data, clean data, query data, analyse data, visualise data. Some of this will be done by products, and some of it will be done by humans. Everything needs to be integrated seamlessly. Ultimately, for all of this to work, the entire company, starting from senior management, needs to commit to building a data-driven culture, where Big Data is not “a” thing, but “the” thing.

Often while speaking about our nascent profession with friends working in other companies we speak about ‘change management’. Change is very hard — particularly for established and non-digital native companies, companies who don’t produce e-commerce websites, social networks or search engines. These companies often have legacy infrastructure and don’t necessarily have technical product managers nor technical cultures. Also for them traditional Business Intelligence systems work quite well — reporting is done correctly, and it’s hard to make a case for machine learning in risk-averse environments like that.

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