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- The A-B-Cs of creating your Data Science team
- How Data Science fits into an Org
- Recruiting and Hiring the Right Data Science Team
By VICTOR ANJOS
In this series we will cover:
– Definitions of data science
– The purpose of data science
– How data science teams should integrate into the organisation
– Recruiting for data science
– Team roles
A definition
It seems trite to say that data science’s applications are broad, but they are. And data science teams come in different forms, within different organisational structures and under different names.
There’s a pretty good Venn diagram developed by Drew Conway (below) which gets to the heart of the ambiguous phrase ‘data science’. Where hacking skills, maths and statistics knowledge, and substantive expertise overlap, this is data science.
In Conway’s words, “The difficulty in defining these skills is that the split between substance and methodology is ambiguous, and as such it is unclear how to distinguish among hackers, statisticians, subject matter experts, their overlaps and where data science fits.”
I recommend heading over to Conway’s article to read more of his thoughts. But the basic takeaway for a layman like me is – there’s a hell of a lot to learn and many different skillsets that can be brought to bear on data.
”The difficulty in defining these skills is that the split between substance and methodology is ambiguous, and as such it is unclear how to distinguish among hackers, statisticians, subject matter experts, their overlaps and where data science fits”
Whilst data science has many grey edges, it’s probably worth including some fairly dry definitions of two common teams – ‘Big data analytics’ teams and ‘data product’ teams. The former looks for predictive patterns in data without necessarily having a preconceived notion of what they are looking for, and the latter works to implement automated systems that are data-driven.
Data Products
Ben Chamberlain, senior data scientist at ASOS, describes a data product as “an automated system that generates derived information about our customers such as predicting their lifetime value. This information is then used to automatically take actions like sending marketing messages or it gets sent to another team who use it for insight.”
If you don’t have any statistical knowledge and you fancy a challenge, you can read one of Chamberlain’s papers about this very ASOS CLV data product.
Big Data Analytics
IBM gives us a serviceable definition of big data analytics: “..a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency.
“..it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media – much of it generated in real time and in a very large scale.”
Data Science is all about tackling a real problem
As I read in a Harvard Business Review article, economist and Harvard professor Theodore Levitt once said that “People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.”
The same applies to data science – the business needs to see a solution. It’s another obvious thing to say, but I’m writing it because new(ish) and complicated disciplines such as cognitive computing can temporarily blind marketers to the fact that normal rules of business apply – what is the problem that needs solving? What data can be brought to bear, and how can the data be used to create most value?
This is something summed up very nicely with another trusty Venn diagram on a Juice Analytics article. (The intersection of the three circles is where successful data products live.)
“The first step is to really, really, really clearly define what problem you’re trying to solve… only then consider whether or not an analytics team or whatever is the right approach.
Parry Malm, co-founder of Phrasee (email marketing language generation software), takes a pragmatic tone and warns about employing a data science team before you know exactly what you want to achieve.
“The first step,” he says, “is to really, really, really clearly define what problem you’re trying to solve… only then consider whether or not an analytics team or whatever is the right approach. What you DON’T want to do is to hire 10 ‘data scientists’ or something, and then have a huge working capital hit for an undefined outcome, when the money could potentially be spent better somewhere else.”
Check in soon for our next installment in this series as we move beyond definitions and start to examine the interplay between data science teams and the wider organzation.
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