Building a vision for your data team should revolve around the business strategy — what the organization as a whole is trying to achieve. Data teams are a means to an end, and their job is to help the organization achieve that vision. Business executives often lack the skills needed to build data products themselves.
Data scientists, for instance, tend to be strong at creating algorithms and crunching numbers but are less experienced with product design. Designers have experience working in service-oriented companies, but not necessarily analytical ones.
Managing Successful Relationships
According to Tom, his approach to managing successful relationships is that of collaboration — where he makes it a point to listen and understand rather than just speak. Tom does not come from an insurance and investments background, he comes from a data and analytics background, so coming into Aviva he was aware that he has a lot of insights to offer but he also kept his mind open to the perspectives of different people, the people he would be collaborating with.
“Get a name”, Tom says regarding building meaningful relationships and as simple as that phrase may be — it is one that has very profound implications. He says that you have to know who you are working with and be proactive in seeking that person out. If you are going to form meaningful relationships with people it is important that you do not see them as COGS in a machine, but as human beings first and foremost.
In other words, your goal when it comes to managing successful relationships is to turn it into a human endeavor, your success will depend on human interaction. It is important to remember that people want to be inspired, and they do not inherently show up to work with the intention of doing a poor job; it is quite the opposite. People want to be impressive and show up to do the best possible work that they can offer, so they need to be inspired to provide it.
Tom learned these lessons partly during his time in Asia, working and building a team in countries like China and South Korea. He noticed that even though there are different hierarchies and different procedures, the inherent need for humanity resounds.
The biggest takeaway here is that companies should not underestimate the power of relationships, a culture of collaboration needs to be inspired at all times.
Attracting the Right Talent
With regards to attracting the right talent, Tom makes it very clear that you need to know your strengths and use them to your advantage. These advantages do not always have to be monetary and compensative, but things that are far more intrinsic to the company’s mission.
An example of these advantages that recruiters and decision makers often neglect is the type of problems that the company is trying to solve, and the type of improvements that they are looking to make in other people’s lives. Talent in the data-space tends to be very goal-focused and they get very excited about solving certain types of problems, this is something that you need to communicate to them to get them to want to work for you.
Of course, it could be other factors as well, such as the location of your company. In Tom’s case, Aviva is located in Oxford Square — which is a very vibrant area in London that a lot of workers want to be situated in.
Lastly, no matter how good somebody looks in their CV, it all boils down to whether or not that person is a good cultural fit. If it is not a good cultural fit, that person will not enjoy being part of the team nor will the team enjoy working with that person. It is important to acquire a more 3-dimensional and holistic view of the talent that you are recruiting, understand who they are beyond just their skills and education.
The Best Structure for Successful Business Teams
Determining the best structure for success business teams depends on what that team is trying to achieve. It also depends on the scale the company as a whole has achieved. If you are starting out small, align the data teams into particular business units — a business unit that is well funded and can make the investments that you need and to make sure the team is close to the pulse of the business.
On the other hand, when you have achieved a decent level of growth, you can centralize some of those capabilities, in what Tom calls a center of expertise, wherein these capabilities are shared across multiple business units; data science products are created and used throughout multiple units.
Lastly, determining your ratios depends on the type of problem you are working on. For some businesses, you are looking at a 1:1 ratio between data scientists and data engineers — this could easily be 10:1 for some companies depending on their focus.