How are Softer Skills still relevant for Data Leaders? Part 6: Insight
For the next step in our series on the softer skills that data leaders need, let’s focus on Insight Generation. This builds on the advice previously shared on how data leaders can use softer skills to create the culture needed for Data & Analytics.
In this post we build on the foundations of effective data analysis to consider the need for insight generation and how to encourage it. On my training course for analysts we focus on the skills needed to generate insights, including using a cross-functional workshop. However, in this post, I will share common themes that I have heard from the data leaders whom I mentor.
What tips, what softer (or people) skills have helped them nurture an environment for effective customer insight generation? As with my previous posts in this series, I will share four themes that I have noticed when listening to practitioner data leaders. As in the previous two posts, these will cover both role modelling by the leader themselves and tools that help sustain the needed culture.
Data leaders stretch their teams to generate insights
If I had one thought that unifies the four themes I share in this post, it would be setting the bar higher. Leaders I have known who struggle to get insights from their team have often settled. Shallow analysis or results without sufficient exploration of causes or implications for the business are accepted. What is rewarded gets repeated. So, I would encourage all data leaders to set the bar high enough. Stop accepting simplistic presentations of “what the data shows“. Push your teams to better critical thinking. Require them to answer 2 questions every time: Why? So What?
Without further ado, here are the 4 themes I have heard on leading teams to generate insights…
1) Develop a focus on outcomes
Perhaps the most obvious difference I noticed in data leaders who have a thriving insight culture is their focus on outcomes. What I mean is that they consistently demonstrate that their interest is in driving meaningful improvements. Not the technical work per se. Rather what can be enabled by acting upon learning from data, analytics or models. In their interactions with both staff & other stakeholders they are clearly motivated by making a difference. They ask action-orientated questions that require a knowledge of the implications of analysis.
They are also self-aware of this behaviour. Such leaders can & do explain to their teams why they are asking about follow-up actions not just technical delivery. As I shared in an earlier post on culture, their use of language in meetings & required updates make a difference. Insight is needed by analysts because they know their leader will want to know the implications. Plus, their leaders obvious curiosity is going to be exploring evidence & motivations. So, generating insight is an obvious next step before analysis work is truly created.
2) Develop and use a Competency Framework
Some of the most sustained improvements that I have seen in data & analytics teams has its origins in a Competency Framework. I have shared previously how such a tool can help data leaders in many ways. One of those benefits is clarifying skills & knowledge that has been overlooked or poorly understood by analysts. A well designed Competency Framework identified a matrix of both the skills (technical, commercial & people/softer) and maturity levels in each required for every role. To supplement this, a bespoke description is produced to support diagnosis for each cell in that matrix. Describing behaviourally what will be observed if a person has & uses such skill/knowledge.
With regards to insight generation, such clarity is really helpful. At the least, it helps leaders identify that additional skill & knowledge is needed in this area (beyond the technical skills & domain knowledge to deliver robust analysis). A well worded evidence description manages to capture the mixture of curiosity, critical thinking, creativity, facilitation & communication skills needed to achieve this. Outputs & behaviours can be described such that analysts see what good looks like & where they need to develop.
3) Stop accepting “back room boffins” stereotypes
This tip is somewhat controversial, both amongst analysts & their leaders. But I have seen too many data & analytics roles limited in their impact because of assumptions that they don’t need to interact with the wider business. All too readily (in my view) technical roles are pigeon holed into being too complex for business interaction or such a requirement being an unwelcome distraction. But, as I’ve argued before with regards to domain knowledge, separation also has dangers. Even a brilliant statistician cannot produce accurate relevant analysis or models if they do not understand the real world application of the data they are working with.
But I think this malaise goes deeper. It reflects a mindset that I have (in a previous career) seen already infect IT departments. I mean a mindset that can find interaction with the business challenging and so retreats to hide behind complexity or process. But the best data leaders don’t focus on covering their bums. Rather, they role model for their teams that there is a need to “get out and about“. To stick their nose into important business projects/meetings. To visibly spend time building relationships with key stakeholders. To communicate latest business new/priorities/context to even the most technical roles in their team. Don’t accept that your modellers or DBAs can’t engage with the business, help them develop.
4) Inspire your team to learn from others (insight is a team sport)
Cooperation is a behaviour and attitude that all successful data leaders value & encourage. It also has particular relevance to customer insight generation. Inexperienced analysts or data teams will often try to complete this activity themselves. To rely on their understanding of customers, their business plus the data and research available to them. However, the most engaging and used customer insights are normally identified by cross-functional teams. So, it will help analysts if they have developed a network across the business and a reputation with stakeholders.
Here again data leaders can role model what will help their people. Taking opportunities to work in partnership with other leaders across the business. Sharing with their teams what they have learnt from others and the benefits of cooperating. I have often heard that this simple behaviour changes teams. Like all teams, analysts & other data roles are looking to impress their leaders. So, if they see a leader who values learning from & collaborating with others across the business – they will do the same. The best data leaders I have known both do this and coach it in their people. They also capture this behaviour in the Competency Framework (to link back to that).
What skills help you at the Insight stage for your team?
I hope those 4 tips ring true to other data leaders. Perhaps they prompt your own thinking or convict you of actions you need to take. Either way, I encourage you to prioritise just one thing you will do differently as a result of reading this post. Then put that idea into action within 2 weeks.
It would also be great to hear from the experience of leaders in developing insight generation skills. What has helped you with this challenge? What people or softer skills have you needed to create the environment for finding high quality customer insights? If you have a story to share, please let me know.