Improving collaboration between data analysts and decision-makers

The analytics landscape is constantly changing, both technically and organisationally. Technology is progressing quickly, and businesses are trying to adapt to the resulting opportunities and challenges. They have to harness the potential within their data, and this means not only reviewing their technology platforms and tools, but also evaluating how they are set up skills-wise, with both their technical and non-technical personnel.

In order for an organisation to realise the full potential of analytics, there are three core elements that need to be aligned:

The most important of these is the people.

Why? Because without the right people, an organisation cannot properly leverage its data or its technology.

 

An analogy for technology, data and people working together

Think of it like a road trip. To get moving in the first place, you need a vehicle (technology), fuel (data) and a driver (people).

Without a driver, you can’t go anywhere. With a driver, you can get to your destination even if your only choice is a clapped-out Austin Allegro and 12 gallons of chip fat.

In terms of people, there are two sides of the coin:

  1. The analysts, whose job it is to harness the technology and data
  2. The stakeholders, who request the data and who make the decisions in the business

For analytics to have any significant impact on an organisation’s decision-making, these two groups must work collaboratively and communicate effectively.

In each project, the analyst needs to fully understand the business challenge or the issue that the stakeholder is hoping to address. But on top of that, the analyst needs to be able to relay their outputs and conclusions effectively, so that the stakeholder can appreciate the value of the information and how to utilise it in decision-making.

So, what are the skills that both of these groups need in order to work together harmoniously and productively?

 

Skills that data analysts need in the modern workplace

Technical skills have always been demanded of analysts, but businesses are now starting to recognise the need for complementary soft skills to go along with technical ability. In short, the modern analyst needs to offer more than just figures and graphs – they need to genuinely influence decision-making within their organisation.

An analyst who cannot communicate effectively will struggle to “sell their wares” to the wider business, and therefore their influence on commercial decisions will always be limited. A new emerging role – analytics translator – recognises the need for more effective two-way communication between analysts and their stakeholders, or, to look at it another way, between the technical and commercial spheres. And this collaboration is vital for delivering data-driven decisions.

The best analysts work consultatively rather than subserviently, and are able to put into perspective not only their work but also the commercial/organisational environment they are attempting to influence. They translate the commercial situation, develop their own briefs and then engage effectively – in a non-technical manner – when delivering the results of their work back to the wider organisation.

Without these soft skills present in the analyst, much of the onus rests with the decision-makers to effectively communicate their analytical requirements. And here’s the problem with that: often, without the depth of knowledge of the data and the analytical techniques applied, the decision-maker’s request can be badly articulated or is not within the realms of what is possible within the required timescales. If that is the case, the result is often that both parties are misunderstood, and the relationship breaks down in the longer term as the analyst fails to build trust and credibility with the stakeholder through an inability to get the work ‘right’ first time.

So, businesses need to recognise the necessity for this translation and then make a conscious decision on whether they a) employ analysts who possess this complementary skillset or b) create a role that’s dedicated to bridging the gap between the analytics community and the wider organisation.

 

How stakeholders need to adapt their approaches

In the workshops I’ve been running for analysts for the last 17 years, many have voiced their frustrations about their relationships with stakeholders (or lack thereof).

Here are some of the most common:

All of these issues are symptomatic of an uncollaborative relationship, but there are measures that both parties can take to improve this. If an analyst is working passively, they’re probably not giving the stakeholder the perception that they particularly want to engage – and much of what we talk about in our workshops is how the analyst can improve this for themselves by working more proactively.

However, there is also much the stakeholder can do to ensure that they get the best out of the analytics team. Some stakeholders are very prescriptive about what they require from analysts, which can result in brief lists of stakeholder ‘wants’, which the analyst has to work from. Frustratingly for the analyst, this doesn’t give the necessary context that’s needed to work flexibly and deliver a result that hits the nail on the head, first time round. If, on the other hand, the stakeholder makes an effort to communicate their ultimate needs (i.e. express the symptoms and impacts rather than make a diagnosis and prescribe a solution), the analyst can utilise their knowledge of what’s under their control (data and tools) to deliver the optimum solution.

Overarching both of these groups, though, is the culture of the organisation they are working in. With any new strategic initiative to become more centric towards data or analytics, it is so important that senior management recognises the need to win the hearts and minds of everyone in the organisation – including the analytics team. ‘Data democratisation’ is becoming a commonly used phrase, and it’s all about putting the power of data and analytics in the hands of the decision-makers.

For this to be effective, the organisation needs to have a greater awareness of data and analytics, and this needs to come down from the top – it’s no good to spend a fortune on software tools and infrastructure without the education to go along with it. That’s a bit like having a brand-new, fully-fuelled Aston Martin on your driveway but nobody around who has a clue how to drive it.