How are Softer Skills still relevant for Data Leaders? Part 2: Planning

This post continues our series exploring how the model of Softer Skills needed by analysts also applies to data leaders, this time looking at the planning step. What does it mean for data leaders to plan more? What might they be missing by focussing solely on technical best practice?

In this post, I will share once more from the themes that I’ve noticed whilst coaching data leaders. Whilst listening to all types of data, analytics, data science & insight leaders there have been common themes. Consistent issues that arise. Familiar challenges & concerns. Plus, time and again I have seen such leaders benefit from certain mindsets and time to think on these things.

So, building on our first post in this series that focussed on questioning skills, let’s turn our attention to planning. What are some of the softer or people skills that data leaders would do well to hone? What wider considerations (beyond just having a plan and understanding the technical streps required) do they need?

Do what matters most and break some rules

As I took time to reflect on this topic and what I had seen in the lives of my coaching clients, 4 themes struck me. Each as much an attitude or mindset as a behaviour or visible skill. So, if you are a data/analytics/insight leader, I encourage you to take some time to reflect on this listed below. Which would help you? Are there any that you have neglected? What could you do to develop helpful new habits that serve you in planning better? Here are 4 to consider…

(1) Manage priorities and how you spend your time

Prioritisation is a topic that my mentoring clients often raise. It is a truism that we cannot manage time; nothing we do can make it pass faster or slower, nor give us more of it. However, we can become more intentional about how we use our time & protect time for what matters most. The first step here is knowing that second part. Data leaders need to be close enough to understanding their organisations strategy, plans & current performance to understand what is most important and/or urgent at any point in time. Then, using a tool like the Eisenhower Matrix, practice saying no to work that is neither urgent nor important. Plus, protect more time for the truly important but not yet urgent, rather than letting all resources be taken up with fire fighting.

I’m still surprised how relatively few leaders make us of timeboxing. Many have drifted into being at the mercy of their Outlook Calendars. Often finding their days filled up with other people’s meetings. It can be a revelation for some to experience blocking time out in their diaries for the ‘appointment‘ of completing some important work or just having protected time to think. I recommend this habit for all leaders. Take control of your availability and protect time for the thinking you need to do each week.

(2) Break rules to make a way for your team

Many leaders learn through their careers that it is better to ask forgiveness afterwards than permission before driving through change. Particularly large organisations naturally become risk adverse. Like a boy’s defence system, middle management layers police what is happening and initially say no to anything that does not obey the rules or accepted wisdom. Yet few businesses understand either what their data & analytics team do or could do. So, their leaders need to have the courage to break some rules. Do things differently when what that will enable will serve the larger purpose of the organisation and/or really help their customers.

Leaders are always being watched. In that sense they are always role models whether they intend to be or not. Most data & analytics leaders are seeking innovation & creative thinking from their teams. They want to hear new ideas and see their team breakthrough what have previously been intractable problems. However, to achieve that, their teams need to see that it is both acceptable to make mistakes along the way & to do things differently. Without being rebellious for the sake of it, leaders who role model for their teams a willingness to ‘ask forgiveness afterwards‘ in service of proving a new & better way of doing things – those leaders will inspire what they seek as an attitude in their teams.

(3) Dare to be different (planning to be different)

In these more inclusive times this might sound like a platitude. Aren’t most businesses rushing to say how they value employees of all colours & backgrounds, create more opportunities for women, etc etc? True, but all too many still fail to achieve cognitive diversity. People, especially leaders, who actually think differently. Often this matters far more. Innovation and improvements require fresh thinking. Even highly capable analytics or data science leaders will not solve key business challenges if they cannot look at them differently. It is one of the responsibility of leading knowledge workers to demonstrate critical thinking & develop your team’s thinking skills.

Here again we are thinking about an aspect of role modeling. There is a dragon to be slain here & it is Groupthink. All human organisations tend towards conformity, social norms & urban myths (that will assume where once proved with data). Leaders can help demonstrate different thinking to their teams using many possible sources. Take time out to attend technical conferences & then discuss with your team the potential implications of case studies that impressed you. Grow your network & share experience or even arrange reciprocal visits with data teams in other sectors. How could you show your team you are bringing other thinking into your team & daring to try different ways of working?

(4) Making Agile work for your team

Most data leaders these days can expect to hear their bosses want more agile working. Many boardrooms have swallowed management consultant pitches on the need for this. Like the term Digital Transformation & the overuse of the label AI, it is expected in the lexicon of today’s CEOs. However, too few who pontificate on this topic really understand the history or how different agile development methodologies were developed. Without grasping that they were developed to address historic issued in the approach to software development, they can be misapplied. Couple that with the diversity of approaches now sitting under the umbrella of ‘agile working‘ and confusion should be expected.

Given data, analytics & data science work is fundamentally different from software development what can data leaders take from agile methodologies? Listening to many data leaders who have managed to make a success of this approach, I recommend two key lessons:

1. Customise the method to work for your workload & organisational culture. Do not be too precious about the textbook approach to Scrum, Design Sprints or AgilePM. Rather, apply an agile philosophy to experimenting with such workflows. What works? What needs to be adjusted? What gives you 80% of the benefits of faster more reliable iterative delivery without constraining analytical thinking & discovery?
2. Select methods that suit what your team is producing. For instance, those data science teams producing data products lend themselves to more textbook agile methodologies. Analytics teams responding to ever changing business needs & opportunities may benefit more from the prioritisation & transparency of Kanban Boards. It is also helpful to select and hone your process in collaboration with your stakeholders.

How could you hone your approach to planning with these attitudes?

I hope those reflections are helpful. What helps you with planning effectively the workload of your data or analytics team? How do you manage to your plan, adjusting where appropriate? What mindset or thinking approach has worked best? Which new perspectives or skills have you needed to learn?

If you are a data or analytics leader with the challenge of translating that longer term vision into planning short term workloads, I’d love to hear from you. What have you proven works? What helped or hindered you? Do you have a personal story to share with other leaders like you? Please hare your wisdom here.