Building Project Teams with Data Science Radar: A Managers Guide

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Project managers guide to playing to a team’s capability with Data Science Radar

Data science consulting is a rapidly evolving beast that is often difficult to tame. The many moving parts require a broad set of capabilities that, in many cases, simply don’t exist in any single person. Engaging our customers in what is a relatively new discipline, and one which is rife with exaggeration, misconceptions and misaligned expectation requires a team effort. To be successful, finding the right fit of team is a critical component of working productively at both technical and strategic level. For us, Data Science Radar (DSR) enables an efficient and effective route to building project teams aligned to both the culture and needs of any client. And it can do the same for you.

Building the winning formula for your team

Beyond the shape and relative strengths that one can evidence by looking at the output of DSR, I’d suggest it’s the inputs that are most important. The language of advanced analytics can be ambiguous at best, so having a tool that makes clear how analytics can be categorised around central themes provides a foundation that has brought our organisation together.

Mango isn’t unique in the way it might conduct its’ business, but I’ve yet to see another consultancy that has a common language set that enables everyone – practitioners, managers, leaders and support staff – to understand relative skills and capabilities in such a diverse and complex environment.

Why is this important?

Knowing the Radar’s of your team gives a greater understanding of the skills, capabilities of the whole team or common language to manage resourcing. My belief and observations are that having a common language enables our whole organisation to understand and talk about what we do. This brings us together, helps us build a commonality of purpose, and means that what we present to the outside world is more powerful. Additionally, the route to our messaging is more efficient and on point. Having an organisation that can sing from the same hymn sheet (my Welsh roots coming through here!!) sounds like an obvious requirement to building a data-led culture.

Strategically and culturally DSR has provided a platform for us to get stronger. But its more than a strategic platform. Operationally, DSR is used everyday to identify who in our team may have the best technical skills to deliver projects and bring value to our clients (Fig 1). Not just individually, but as a project team. Getting the right mix of skills, all executing aligned to respective strengths, means we can be confident to deliver on time and to the exacting standards we set ourselves. Being the right cultural and strategic fit only takes us so far – backing this up with the right technical mix ensures success across the full project lifecycle.

Develop and retain key skills

As a growing company in a fast-moving industry its critical to continually attract, develop and retain key skills and capabilities. Many of our staff have a strong desire to continually grow themselves and are keen to engage in professional development wherever opportunity presents. In some cases, this happens as a natural consequence of engaging in a wide variety of project work. But in others, it happens due to development planning aligned with our career framework. Before DSR, this meant looking at generic descriptions of technical capabilities – eg using, teaching, mentoring, coaching in R – which whilst valuable didn’t offer a level of specificity many of our team were after. Post DSR, we’re able to explicitly align development needs with DSR traits and allow our team to grow capabilities in the areas that are of most interest to them. We can also guide and shape this outcome based on business need, meaning we have a significantly more engaged workforce with a set of skills that the business needs. It’s a win-win for everyone, including our clients.

Software as a service

Data Science Radar is a piece of software, powering analytic capability. It isn’t a panacea that provides users with a utopian state of analytic health, nor is it the silver bullet that ensures everyone in your organisation understands the language of analytics. But it can be the differentiator that helps secure competitive advantage.

Gartner indicates that 80% of analytic projects fail. Reasons vary from lack of executive buy-in, misaligned expectations, disconnect between technical teams and users, and ill-defined business problems. DSR doesn’t eradicate these problems, but it does provide a platform through which many of these issues can be surfaced, discussed, understood and acted upon. Once better understood, it can then be used to ensure the right people are tasked with the right actions to execute effectively. DSR removes square pegs commonly put in round holes. It can bring an organisation together; help empower growth and provide clarity in terms of existing capabilities. Once you know these things, building project teams for the future becomes a natural next step, and keeps you always looking at where the gaps are, and how to close them.

We never built DSR to close strategic or cultural gaps, but that’s what it has done. And its enabled a more empowered and more engaged technical workforce too.

Pete Scott is Client Services Director here at Mango. For more infomation about a demo of Data Science Radar please click here.