The key to turning a data analyst into an analytics translator
The role of ‘analytics translator’ is still a fairly new concept, and it’s been a hot topic for a while now – at least in our world.
If you’ve not encountered the term yet, an analytics translator is, well, just that: someone who translates analysis within their organisation. But how do you know if your business needs one? And more importantly, how can you upskill one or more of your data analysts into this new role?
Why has the analytics translator role emerged?
McKinsey wrote about the role in February 2018, and the discussion has continued ever since, with many companies now actively recruiting for such positions.
Most businesses have no trouble recruiting technically gifted analysts who can identify patterns and anomalies within data.
The problem is that the resulting reports and recommendations can often be undervalued or misunderstood by decision-makers.
It’s not necessarily the case that either party is to blame here. It might simply be down to innocent miscommunication, lack of business context for the analyst to work from, poor data visualisation in the final report – or something else. Analysts and decision-makers being on separate wavelengths is a longstanding issue. Whatever the cause, there’s still the fundamental problem of the analysis not being used properly.
And this is precisely why the analytics translator has entered the picture:
To bridge those gaps between analytics and the wider business, so that the data really does make a difference once it’s turned into insight.
Does your organisation need an analytics translator?
If any of that sounded familiar, an analytics translator might be exactly what you need. A disconnect between insights and decision-making will lead to unwise decisions being made, whether that’s occasionally or commonly.
So let’s take a closer look at what you need from an analytics translator.
What are the essential skills and traits for the role?
- Sector expertise
- Company expertise
- Strong technical knowledge (but not practical expertise in programming and modelling)
- Communication, negotiation and persuasion
- People management and delegation
- Data presentation and visualisation
An analytics translator doesn’t necessarily have to come from an analytical background, but it can work well if they do, especially in terms of understanding how analysts work, the obstacles they face and the way they’re often viewed by other departments across the business.
Of course, most analysts do need to upskill in certain areas before they can become effective analytics translators – specifically building those softer skills, which tend not to come naturally.
Upskilling an analyst to become an analytics translator
Looking at your existing analysts, can you see any potential analytics translators among them? If so, it’s time to get working on their professional development to prepare them for the step up.
One way to start nurturing a data analyst for the transition into analytics translation is to identify exactly what sort of analyst they are.
Predominantly left-brained, or more balanced?
Most analysts tend to be inclined towards the left side of the brain: logical and analytical by nature. To become an effective analytics translator, they’ll need to work on bringing out their creativity and intuition – the right side of the brain – because that’s how they’ll be able to see things from stakeholders’ and decision-makers’ perspectives.
You will find, though, that some analysts are already fairly balanced between left and right. If that is the case, you’ll need to identify the specific lacking skills and focus on developing those.
Where are the skills gaps?
Like all kinds of professionals, very few data analysts are ‘perfect’. Identifying the analyst’s skills gaps and highlighting it to them is therefore an essential step in the progression path towards the analytics translator role. You should ask yourself the following questions when evaluating their skill-set:
- Do they communicate effectively with non-analytical colleagues and other departments?
- Are they able to negotiate with stakeholders when tasks or deadlines are unrealistic?
- Have they mastered the art of storytelling in their reports? Is data visualised as well as it could be? Is commentary compelling? Are conclusions and recommendations clear?
- Do they understand their stakeholders’ motivations?
- Can they adjust their approach depending on the stakeholder’s personality?
- Have they shown leadership capability at any point so far? Can you envisage them managing other analysts and delegating work?
You can use the answers to these questions when you put together a development plan for the analyst.
Our guide, “How to train and retain your data analysts”, covers this in more detail – as well as wider advice on how to make analytics work better for your business.
Download the guide to find out how you can get all of your analysts working more like analytics translators – you might decide that this setup would work better for your organisation than having one single analytics translator. Or you might not. Ultimately, you need to decide what will work best for your business.