I recently read this article title Data as a Product vs. Data as a Service; it discusses the idea of data teams within organisations, their true purpose to the organisation and the different skill-sets needed depending on the Data as a Product (DaaP) or Data as a Service (DaaS) model being run. I was struck by how applicable this is to any ‘data team’ or ‘data consultancy’ within any organisation.
As I read these two different models I could picture teams I’ve worked in or organisations I see today and understand a little more why things did/are breaking down.
What are the models?
What is DaaP – the job of the data team is to provide the data that the coaches needs Teams operating in a DAAP model tend to provide access to reports/data/video that the managers coaches use themselves to answer their questions. The flow is unidirectional, from data team to the coaching staff and therefore their is limited domain knowledge within the data team. This data team acts more like engineers. I see it as the teams that put’s a lot of the processes in place, the unseen work to many.
What is DaaS – the focus of the data team is answering questions as opposed to providing the tools to others to answer their own questions. As a concrete example this might look like a request to build a model that identifies left-backs that fit the club and coaching criteria. There is a much stronger relationship between the coaches and the data team. This is even where you see the specialization of roles within the data team. Loans analyst, opposition analyst or set-piece analyst. This allows the data team to have much higher levels of domain expertise and allows them provide insights as opposed to rows and columns. In essence the data team becomes more like strategic advisers.
On the face of it it sounds like everyone should operate the DaaS model – but the thing I see is a lot of teams skip past the DaaP stage. It’s very difficult to master DaaS if you don’t have the right foundations in place.
Teams need to be good at DaaP before they can be good at DaaS. I see so many clubs skip past the foundation stage of having good data/video processes in place. Common things I see; like all data being stored in a ‘master’ excel file for example. Little or no programming skills to automate the mundane everyday tasks. Copy pasting data over and over again. These are not wrong perse – but there is a much much better way.
These teams can be filled with great analysts who have a lot of domain expertise but have lacked the engineering/programming/technical skills to make DaaP a reality before you can push on.
You could go through a lot of the jobs advertised on this site and tick off jobs that fall in the pertain to want domain experts but the job descriptions are filled with very repetitive (engineering/programming) tasks.
If you hire strategic, functional data people to do Data-as-a-Product things like reporting and modeling, they will be unhappy, they will leave, and you will fail.
Conversely, if you hire engineering oriented individuals without functional experience, they won’t be very good at partnering with stakeholders on critical decisions.
This — and failing to communicate the shift to DaaS to senior level stakeholders and leadership — is what tends to doom strategic data science.
Interesting I do think we have a seen a change in this regard with the sports data companies too. For a long time they offered Data as a Product. They shipped rows and columns of data to clubs and analyst and it was very much up to you to make it what you could. That’s changed, these companies now hire a lot more data scientist who can offer a much more strategic, partnership driven approach to using their data.
Personally I’d much rather operate in a data team that has a strategic partnership with the coaches/management but I’m acutely aware of the need to have the right tools and processes in place before the real gains can be made.
My advice would be intentional about the purpose of the data team you are building or joining. Is their a road-map for developing/changing that purpose and who needs to be hired to get that done.
I recommend you check out the original article Data as a Product vs. Data as a Service – The difference between providing “data” and providing “insights” (actually though)