How Softer Skills are relevant for Data Leaders, Part 9: Solution

The editorial themes of recent months have distracted from me completing a number of series, including this one which was missing a focus on the Solution stage. This blog post concludes a 9-part series that has shared the lessons I have learnt from mentoring many different data leaders. Spotting the patterns of how softer skills, which enable analysts to succeed in their roles, apply to data leaders too.

Loyal readers will recur that I have already shared the data leader versions of my first 8 softer skills:
1) Questioning
2) Planning
3) Obtaining Buy-In
4) Using appropriate Data
5) Undertaking robust Analysis
6) Generating Insights
7) Negotiating Sign-Off
8) Communicating a Visual Story

In this final post of the series, I turn my attention to delivering a useful Solution. When training analysts or data scientists in this stage we focus on closing the loop. This involves the twin themes of delivering to the real business need and establishing effective feedback loops. The former is measured by revisiting what was uncovered in the Questioning stage. However, the latter has a longer-term focus and draws on the Scientific Method to ensure learning for improvement. Let’s see how these principles manifest in the work of data leadership roles…

Joining the Dots = being a connector

As part of their Solution delivery softer skill I often hear that data leaders need to be effective connectors. By that I mean they need to lean into the cross-functional responsibility. They need to think & act beyond organisational silos.

Reporting lines can vary depending on sector and organisation. But many data leaders have responsibilities that lend themselves to such a broad perspective. Perhaps they have responsibility for customer analysis, insight or metrics. Equally, they may carry a remit to enable the whole organisation to be more data-led or efficient (via the deployment of AI/automation). It can feel like a heavy workload, but it does lend itself to having a broader perspective than other leaders.

Coupled with a scientific mindset (which is good at pattern matching), data leaders can be well placed to ‘join the dots’ across the organisation. Opportunities include identifying customer experience issues or end-to-end process inefficiencies. But can also mean spotting where learning in one area can help another or use cases for innovation. In all these cases data leaders would do well to develop their abilities to influence others to take action on such insights.

Longer-term thinking, especially talent development

Short-termism is a curse of modern organisations, especially publicly listed companies with short-tenure CEOs. Secure data leaders will notice that their skills are very much in demand in the market & so be able to take a longer-term view.

The benefits of taking the long view can be manifest in many of the challenges data leaders face, from investing in the data infrastructure needed for future growth to a data strategy that focuses on beginning data journeys. But one of the applications of longer-term thinking that I have most commonly seen serve data leaders is related to recruitment and talent development.

We all hear horror stories of how difficult it can be to recruit the talent you need for a range of specialist data roles. However, part of the reason for this is the focus on quick fixes and immediate needs. As well as such fire fighting, wise data & analytics leaders invest in developing a talent pipeline. This can take different shapes. Some partner with local schools or universities & even get involved in education to help better fill the pipeline from the start. Others build effective academies for internal training and development, so they can grow their own more efficiently. Either way, I hear leaders later reap the fruits of their labours & never regret investing in such a pipeline.

Hold yourself and your team to a higher standard

Many experienced data/analytics managers or leaders bemoan how unfair their lot seems compared to peers in other functions. All too often it can feel like colleagues in other specialist areas (like IT, Finance or Legal) do not need to be both technically expert and business savvy. What can I say? No one said life was fair. But more seriously, those who recognise such a higher standard can excel.

Even if it sometimes feels like more is expected of data or analytics leaders (all the way up the tree) it can be worth it. Time and time again I have seen that successful data leaders embrace such a challenge & motivate their teams to do the same. Charismatic motivational leaders can make an essential difference with such a focus. Encouraging their people to focus on the prize ahead and the opportunity they have to transform their organisation & be proud of the result.

Such leaders hold themselves and their teams accountable for not just the delivery of their technical output but their impact on the wider organisation. The goal is not just delivery but transformation. Not just doing their job but making the world (at least their organisation) a better place as a result. Such an end result focus is also an example of being ‘other focussed’. A form of Servant Leadership if you like. Although it is challenging it is also fulfilling work. Once data leaders can achieve that, they will retain & develop technical talent far better than their competitors.

Your thoughts on the series?

I hope those reflections on the themes I have noticed from coaching many different data leaders were helpful. Now I close this series of nine posts, I am keen to hear what you think.

Many data leaders tell me they read this blog. So, do my thoughts ring true with your experience? Are there other skills you would reference as essential? Are there any I have listed at which you have excelled? If you have experience or even wisdom to share, I’d love to hear from you.

Meanwhile, keep up the good work. It may not always be appreciated, but data & analytics leaders are building the capabilities essential for their organisations to survive in a changing world. Your work does matter. So, you should be proud of it. Keep up the good work all.