Dream team: the 4 people you need on your digital analytics team

If you’re looking to enhance your in-house analytics capabilities, you’re going to need skills. A certain set of skills. Scarce skills. Premium-priced skills. Silky soft skills, as well as the hard ones.

With quality candidates thin on the ground, you may be forced to compromise, or tempted to take a gamble on attitude over aptitude. You may be inclined to reshape the role around a particular person.

There’s a lot of potential for overlap and ambiguity when it comes to analytics roles and responsibilities, especially when making your first hires. So, where do you start? What skills are essential and which are nice-to-have?

Much will depend on your company’s size and structure, your budget and resources, analytics maturity and objectives. However there’s one thing we wouldn’t recommend you try…

Desperately seeking unicorns

A lot of organisations attempt a shortcut, hunting for an analytics unicorn – that mythical being possessing endless expertise: implementation, collection, integration, preparation, exploration, visualisation, activation… not to mention an alicorn (Google it) and a swishy tail.

The quest will be futile. Even if you do find someone with such a broad skill set can you capture and keep hold of them? Training them will be a full-time job. Offering a career path will be a challenge. Replacing them a proper nightmare. Before you know it, you’ll be back at square one.

We’ve found the real magic happens when smart people with different skills, mindsets and experience come together and collaborate on data and analytics. Far better to find and combine specialists – even if that means working with external consultants while you build the business case for hiring more people internally.

To get you started, here are four types of people you should look for. Together, they’ll provide a solid foundation on which to build and advance your analytics capabilities.

Olivia – the orchestrator

Olivia is a pragmatic visionary. She sees the big picture as well as the individual puzzle pieces needed to make up the whole. Olivia likes to make things happen. She’s resourceful – always finding a way around obstacles to keep projects on track.

With sufficient technical know-how to know what she doesn’t know, she acts as the bridge between the analytics team and the rest of the business, helping to turn sometimes fuzzy marketing and product goals into specific analytics initiatives. Olivia isn’t one to put words in your mouth, but can very effectively translate what you’re trying to say.

Background: Either leading teams of analysts (client or agency side) or an experienced data-led, strategic marketer.

Soft skills: Communication, stakeholder management, presentation skills.

Tools and tech: strong background with the GMP, some GCP, and Project Management tools.

Killer question: What’s the biggest improvement you’ve driven through an analytics initiative?

Practical test: Take some sample marketing data, analyse it and present back your interpretation and recommendations.

Ellis – the Engineer

Ellis is possibly the most organised and logic-driven person you’ve ever met. Ellis likes everything to be in order (a bit of a neat freak – check out that desk!). Systematic, methodical, patient and persistent, and totally focused on the mechanics of how things work, Ellis constantly strives to find a better, more efficient, way of doing things.

Ellis played a lot with lego as a child. An ingenious ‘do-it-yourself’ architect and builder who takes great satisfaction in seeing a vision realised. You’ll often find Ellis developing data infrastructure and pipelines, and building systems from the ground up. No problem is too complex if you give Ellis enough time to seek out a solution.

Background: Engineers often come from a development background, others are former analysts who got frustrated with the quality of data they were analysing and decided to fix the problem themselves.

Soft skills: The ability to collaborate is critical, whether that’s with a developer tasked with implementing a data layer, negotiating with IT for access to data or figuring out what tables an analyst needs.

Tools and tech: The list is almost endless. Tag management solutions like GTM and Tealium, JavaScript, Python, experience working with APIs and pipelining tools like Funnel, Fivetran and Skyvia, data transformation tools like dbt or Dataform, BigQuery and other GCP engineering tools.

Killer question: There maybe more than one way to approach a problem in the world of engineering, ask for an alternate method to the one they already laid out.

Practical test: Engineering is broad. Present a candidate with an existing solution and ask them what steps they’d take to improve it, both in efficacy and cost.

Sam – the Scientist

Sam lives and breathes data, and whether it’s mathematics, statistics, programming, machine learning, or data mining, modelling, or visualisation, when Sam develops an interest in something, they go deep – very very deep.

A critical thinker, Sam questions everything, systematically analysing a problem or idea from multiple perspectives to understand it as thoroughly as possible. Sam sees things that aren’t immediately obvious to others, whether it’s patterns and meanings in the data, a new way of looking at something, or a solution uniquely suited to the problem at hand.

Background: Either academic research (in a STEM subject) or direct experience on the job.

Soft skills: Communication is key as it’s hard to explain (and thus justify) the value that data science can bring.

Tools and tech: We could list a bunch of tools like Jupyter Notebooks, Python, R, etc. (we try to use the GCP stack wherever possible), but any data scientist will have their preferences.

Killer question: What data science solution that you built had the biggest impact?

Practical test: Tricky. The work of a scientist might take days or weeks to produce. Share some code, methods, or systems with them and see if they can spot any errors and suggest improvements.

Alex – the Analyst

Alex is an inquisitive, critical thinker who loves to dig into the data to find the story, extracting patterns, trends, or correlations that others might have missed. Alex is a pro at bringing the story to life, visualising and translating the data to guide others in their decision-making.

Curious by nature, Alex is constantly questioning, exploring new ideas and falling down data rabbit holes to seek out answers. Alex has a knack for problem-solving – they’re perceptive, observant, insightful, and always mindful of the big picture. Loves an Escape Room team challenge.

Background: Transferable skills are key here so the Analyst may come from a variety of backgrounds. The common thread will be tenacious problem solving using data.

Soft skills: Working self-sufficiently, breaking down problems, thinking analytically and critically, good communication skills including presenting complex explanations in simple terms.

Tools and tech: SQL, Sheets/Excel, GA, and some Python and/or R.

Killer question: Which problem are you most proud of solving?

Practical test: Build some SQL queries, perform a comparison of two data sets in Google Sheets/Excel, analyse some sample data and make some recommendations for improvement/further investigation.

Winning the world cup of analytics

As you look to scale your analytics maturity and build an in-house data analytics team, remember it’s a team sport. And a team can’t function effectively with too few players – or if everyone wants to play in the same position. Be more Gareth. As a manager, it’s your responsibility to adjust the formation to suit your organisation’s analytics needs (which may change over time). And if you don’t have the resources to fill all these roles on day one, you can always draft in a ringer or two from the Measurelab all-star squad.