July 26, 2022

How are Softer Skills still relevant for Data Leaders? Part 5: Analytics

By Paul Laughlin

We return to the topic of how softer skills show up in the work of data leaders, in this post looking at Analytics. This is the fifth step in the nine-step model for analysts developed by Laughlin Consultancy.

In addition to my work training analysts in those nine softer skills, I also have the privilege of mentoring a wide range of data & analytics leaders. It is from listening to them & noticing common themes that I began to identify how these same stages showed up in the lives of those leaders. In this series of posts I’ve been sharing some of those common behaviours. Tips that have helped them if you like.

The focus for this post is the Analytics stage. Once the data is sourced and prepared, which softer skills do leaders need to create a productive environment for their analysts? What is their role in helping analysts or data scientists produce better & better work? Below I share four common themes that I have seen in data leaders who create such a culture.

Leading an Analytics culture through 4 behaviours

All too often organisations hire technical staff, like analysts or data scientists and then stand back expecting them to work magic. Too few realise the need for leadership skilled in nurturing them. Leaders who are able to spot talent and develop the skills that are missing. As well as a greater level of self awareness, positive energy and regular encouragement, there are other common behaviours I see in such leaders.

Several of these are responses to a corporate culture that would otherwise quench analytical thinking. So, leaders who are strong in their behaviours are also often mentally touch. Able to develop warm relationships or collaboration across their business. But also able to protect their analysts with an environment conducive to quality analysis. Let’s consider 4 such behaviours that I’ve heard.

1) Step back from hands-on work

Perhaps the first signature behaviour that I notice is stepping back. Data leaders who succeed at this step know how to avoid being micro managers. Despite many still being technically capable, even potentially the most skilled coder in the room. They know the need to focus on their leadership & people management responsibilities and create space for the analysts to grow.

This tends to be demonstrated in two ways. First they are effective delegators. Well versed at both clearly briefing what is required and leaving room for the analyst to determine how they achieve that output. By giving a very effective brief (including business need, context, constraints, quality & how it will be used) they guide relevant problem solving. But they are also able to keep silent and allow space for different ways to solve the problem. New analysts may not take the same technical approach but if it works they will also feel empowered. Such leaders also know how to coach not tell analysts on thinking this through. A vital softer skill for such technical leaders.

2) Learn new ways to empower their teams

No organisation stays static. No two teams or individuals are the same. Interpersonal relationships and networks and even ways of working change with new hires and departures. Effective data leaders know this. They recognise that empowering their team to make a difference in their organisation is not a once and done activity. They keep it a focus and adjust their activity to meet current needs.

That all sounds very theoretical, what can this look like in practice? Well, I’ve seen and heard approaches as diverse as the following. Internal events to regularly bring together analysts from different teams to share knowledge. Ensuring ‘back room’ technical analysts or data scientists are also included in presentations to the business. Protecting time each week for analysts to work on their own development (they choose the topic & media). Arranging for analysts to help charities or in other ways make a positive difference in their communities. At the minimum it means such data leaders listen to their analysts regularly and their teams know they want to know how they can help.

3) CPD: keep up-to-date with best practice

This may initially sounds very technical. Like the topic which is not my focus in this post. But my interest here is not just all the technical knowledge that can help a data leader, but the skill to learn it. Effective data leaders who nuture several generations of analysts know how to learn. They have thought not only about the technical topics themselves but the process of learning and maintaining knowledge.

Through effective time management and often personal networks, they protect time for keeping sufficiently up-to-date. That word sufficient is key. Data leaders who are good at this know how to prioritise. They avoid rabbit warrens and time drains. But equally they are not just skimming superficial articles and picking up jargon they barely understand. They have ensured they sufficient foundational knowledge and frameworks to position new technologies or unfamiliar skills. They know how to learn enough (quickly) about those which are not yet relevant. But they also recognise those which are relevant and are at the vanguard of gaining a deeper understanding. Through media that work for them.

4) Protect technical stars from performance management systems

I have written before on the need for it. So, I was not surprised to see that other data leaders also discovered this need. It is a fine balance to walk. Between paternalism on one side & having technical stars undervalued and leaving on the other. Effective data leaders learn to strike the right balance between protecting technical staff and still challenging & supporting them to develop. They can role model and coach some of the softer skills that will help the more technical thrive.

But they also have the pragmatism and mental toughness to recognise what will not work and so work around it. Most organisations performance management systems are designed to identify and reward generalists. Those who classically go on to management & even senior leadership positions. Most such systems fail to recognise or reward mastery in technical specialisms. Even when such skills and people can be critical to an organisation now & in the future. This people skill in data leaders breaches the gap.

What skills help you at the Analytics stage for your team?

I hope these themes I have shared above 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 other practising data leaders. What has helped you with this challenge? What people or softer skills have you needed to create the environment for high quality relevant analytics? If you have a story or tips to share, please let me know.