How are Softer Skills still relevant for Data Leaders? Part 4: Data

Returning to our series exploring how Soft Skills are still essential for data leaders, in this post we look at the Data stage. To explain that term, let me remind you that this series is considering how a model of Softer Skills for analysts can also help data leaders. Step 4 of that model focuses on the need for analysts to identify, understand and appropriately source the data needed.

In this post, I will consider what data leaders should consider at this stage. As those with leadership & management responsibilities for data/analytics practitioners, what should they be considering? As I’ve mentioned before, I am drawing on my experience of mentoring such data leaders. Without breaching anyone’s confidence, below I share what I’ve noticed as common themes.

Data leaders have many responsibilities with regard to their organisation’s use of data. These include technical knowledge as well as legal & regulatory compliance. But there are also cultural, team & people aspects that perhaps receive less focus.

Beyond the technical, creating a culture of learning from data

Data leaders are charged with creating an environment where their team can learn from the appropriate data. Creating such a place of learning also requires softer skills or people skills. There are many and varied aspects of that challenge, too many to cover in this single blog post. However, I summarise below four themes that I have heard shared by data leaders. Aspects of succeeding in this crucial part of what businesses really need from their data leaders.

So, how are softer skills needed by data leaders to empower their analysts to succeed at the ‘data stage’? Here are those four approaches I’ve consistently heard shared by data leaders.

(1) Implement the systems and controls needed

The first may not initially appear to be a people skill or softer skill matter. However, implementing the systems and controls needed is more than a technical or organisational challenge. If data teams are to benefit from such a way of working they need to buy into the need for such restrictions. Without the people work needed in this regard, analysts will resent such limits and breaches will in time occur.

So, the first softer skill needed by data leaders is to win over the hearts & minds of their teams with a vision of professional responsibility. Selling and then implementing the benefits of appropriate protections and processes that enable repeatable results. Here they can benefit from the vision of Data Science as a new standard. The best examples I have seen of such foundational work inspire their people with a vision of scientific rigour in the way their team will work.

(2) Bring together the roles needed to achieve your data vision

Gathering the breadth of talent needed is the next common theme that I have noticed. Effective data leaders think beyond the structure chart that they inherit or initial investment plans. Softer Skills are needed to consider both potential roles and the individual talents of the people available. I have blogged before on the benefit of shaping roles to the best people rather than vice versa.

Another aspect of this approach is to recognise the power of a more holistic service. My previous post on the benefits of creating a holistic customer insight function considers the power of bringing together diverse technical teams. Labels and capabilities evolve. Since writing that post, the benefits of DataOps and Data Product teams have become clearer. The generic leadership skill here is to be able to step back and see the capability that your organisation needs. Then to plan, beyond existing power structures, how to bring together data, analytics, data science, engineering, ops, consultancy & research teams. Realising the benefits beyond just more joined-up processes, like shared vision, knowledge sharing and improved insight & innovation through such collaborative working.

(3) Achieve and protect greater autonomy

As well as safe ground rules and a gathering of the talents, data leaders also need to provide air cover. Part of their leadership responsibility is to envision their team and wider stakeholders with a vision of the role data can play. Another part is to protect their team from historic controls, political issues & misguided decision-making. Much of that boils down to the need to achieve a greater level of autonomy for the work of their team.

People skills are crucial here. Data Leaders need to become adept at motivating and negotiating with two very different communities. Their first challenge is to win the trust of senior leaders. As I shared on the softer skills needed to secure buy-in, this means focusing on building a brand of commercial credibility & reliable delivery. Then using the trust developed to secure greater operational freedom & independence. In tandem, such data leaders inspire their own teams to make use of such freedom. To use their greater freedom to experiment with new data, build and test new approaches and crucially to deliver visibly new value-adding outputs.

(4) Protect this vital space to test and learn

There is overlap here with my last theme but the emphasis is different. Above I was arguing for the need to achieve and defend such autonomy. Winning the space to act proactively and differently than the wider organisation. To identify and proactively work on data problems or opportunities that have not been requested by other stakeholders. Next, the data leader needs to both hold their ground and embed the culture needed within their more autonomous team.

Holding the ground means protecting their team from outside interference or limitations whilst they are testing and learning. I have shared before why data leaders need to protect their more technical staff from inflexible performance management systems. They also need to protect this more independent scientific way of working from the organisation’s immune system. That is the middle management layer. Well-meaning people who will seek to protect the organisation from past mistakes or identified risks, but who must not be allowed to stifle experimentation. Coupled with that, data leaders need to continue to role model a test & learn (‘fail fast’ when needed) culture in their teams. Rewarding learning, even when it is proving what does not work. Avoiding becoming paternalistic or accepting of mediocrity whilst taking this approach is one of the tensions data leaders learn to manage.

What people skills help you manage this stage as a data leader?

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 use of data which your business needs? If you have a story or tips to share, please let me know.