Avoiding being a ‘trophy’ data scientist

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Recently I’ve been speaking to a number of data scientists about the challenges of adding value to companies. This isn’t an argument that data science doesn’t have positive ROI, but that there needs to be an understanding of the ‘team sport’ and organisational maturity to take advantage of these skills.

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Trophies are nice to look at but they don’t drive business decisions.

The biggest anti-pattern I’ve experienced personally as an individual contributor has been a lack of ‘leadership’ for data science. I’ve seen organisations without the budgetary support, the right champions or clear alignment of data science with their organisational goals. These are some of the anti-patterns I’ve seen, it’s non-exhaustive so I provide it.

The follow is an opinionated list of some of the anti-patterns.

  • I’ve written before about data strategy. I still think this is one of the things that’s most lacking in organisations. I think a welcome distinction is that data collection which needs to happen before data analysis, and that this needs to happen in accordance with the strategy of the company.

Solution: Organisations should map their data science projects to the key business concerns of the organisation. This will help shape how resources are allocated.

  • There needs to be an understanding of what kind of leadership you need for a data science team. This needs to be someone with hands-on experience of doing data science. This is not someone familiar with ‘analytics’ or ‘reporting systems’ and ‘delivery’. It is someone familiar with things like ‘probabilistic programming’, ‘neural networks’ and ‘A/B tests’. So don’t put an ‘analytics leader’ in charge of a team of data scientists.

Solution: Executives – feel free to reach out to me to discuss data strategy, I’ll gladly point you in the right direction.

  • You need Business intelligence not data science – there’s nothing wrong with reporting, or building analytics systems, but it’s not data science. Be honest about what your organisation needs.

Solution: Ask clarifying questions when interviewing about why the organisation needs data science versus other things.

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AI in the Enterprise (the problem)

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I was recently chatting to a friend who works as a Data Science consultant in the London Area – and a topic dear to my heart came up. How to successfully do ‘AI’ (or Data Science) in the enterprise. Now I work for an Enterprise SaaS company in the recruitment space, so I’ve got a certain amount of professional interest in doing this successfully.

My aim in this essay is to outline what the problem is, and provide some solutions.

Firstly it’s worth reflecting on the changes we’ve seen in Consumer apps – Spotify, Google, Amazon, etc – all of these apps have personalised experiences which are enhanced by machine learning techniques depending on the labelled data that consumers provide.

I’ll quote what Daniel Tuckelang (formerly of Linkedin) said about the challenges of doing this in the enterprise.

First, most enterprise data still lives in silos, whereas the intelligence comes from joining across data sets. Second, the enterprise suffers from weak signals — there’s little in the way of the labels or behavioral data that consumer application developers take for granted. Third, there’s an incentive problem: everyone promotes data reuse and knowledge sharing, but most organizations don’t reward it

I’ve personally seen this when working with enterprises, and being a consultant. The data is often very noisy, and while there are techniques to overcome that such as ‘distant supervision‘ it does make things harder than say building Ad-Tech models in the consumer space or customer churn models. Where the problem is more explicitly solvable by supervised techniques.

In my experience and the experience of others. Enterprises are much more likely to try buy in off-the-shelf solutions, but (to be sweepingly general) they still don’t have the expertise to understand/validate/train the models.There are often individuals in small teams here & there who’ve self-taught or done some formal education, but they’re not supported. (My friend Martin Goodson highlights this here)  There needs to be a cultural shift. At a startup you might have a CTO who’s willing to trust a bunch of relatively young data science chaps to try figure out an ML-based solution that does something useful for the company without breaking anything. And it’s also worth highlighting that there’s a difference in risk aversion between enterprises (with established practices  etc) and the more exploratory or R and D mindset of a startup.

The somewhat more experienced of us these days tend to have a reasonable idea of what can be done, what’s feasible, and furthermore how to convince the CEO that it’s doing something useful for his valuation.

Startups are far more willing to give things a go, there’s an existential threat. And not to forget that often Venture Capitalists and the assorted machinery expect Artificial Intelligence, and this is encouraged.

Increasingly I speculate that established companies now outsource their R and D to startups, hence the recent acquisitions like the one by GE Digital.

So I see roughly speaking two solutions to this problem. Two ways to de-risk data science projects in the enterprise.

1) Build it as an internal consultancy with two goals: identifying problems which can be solved with data solutions, and exposing other departments to new cultural thinking & approaches. I know of one large retailer who implemented this by doing 13 week agile projects, they’d do a consultation, then choose one team to build a solution for.

2) Start putting staff through training schemes similar to what is offered by General Assembly (there are others), but do it whole teams at a time, the culture of code review and programmatic analysis has to come back and be implemented at work. Similarly, give the team managers additional training in agile project management etc.

The first can have varied success – you need the right problems, and the right internal customers – and the second I’ve never seen implemented.

I’d love to hear some of the solutions you have seen. I’d be glad to chat about this.

Acknowledgements: I’d like to thank the following people for their conversations: John Sandall, Martin Goodson, Eddie Bell, Ian Ozsvald, Mick Delaney and I’m sorry about anyone else I’ve forgotten.