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Panel discussion recap – Essential Data Skills for Leaders

By July 12, 2021 No Comments
Essential Data Skills for Leaders

The growth of data and increasing exposure to sophisticated analysis faced by senior managers is changing leadership as we know it today.

Jina Melnyk met with three expert panellists to discuss the intersection of essential data skills and leadership skills, and what is needed in order to effectively lead in a data rich world. Below is a summary of the conversation.

  • Dr Kim Nilsson – Co-Founder Pivigo
  • Graham Harrison – Lincoln College Group and Nottingham University Business School Alumni
  • Daniel Sanz – Data Science Instructor and Coach

Many organisations are not fully realising the value of data available to them, and that’s due to a shortage of the data skills throughout the workforce. Only 20% of all employees said they felt confident working with data, and half of all employees stated that they tend to rely on gut feel still in decision making rather than evidence driven insights (Accenture, 2019). It’s evident there is a gap between the highly skilled technical analysts producing insights and the workforce who need to effectively interpret and use those tools to take action.

Where does skills development fit in a successful digital transformation strategy?

Leaders need data skills and to understand opportunities presented by data. They need to show authenticity and role modelling, as well as data led behaviours in their organisations and for their teams. As leaders, we need to enhance our own careers by acting in a more informed evidence led way.

Dan ruminated that we usually think of learning AI and performing at the level of highly technical people in digital transformation, however one skill that is usually overlooked is simply the development of a manager’s ability to be able to understand what technical teams do. Technical stakeholders need to be able to work with non-technical stakeholders, explaining things in plain terms and being able to understand what the business requires.

Kim Nilsson explained that data analysis is going to become one of the essential skills needed in the future, much in the same way the computer and tools such as Excel and Word became essential within an office many years ago. Whilst key skills such as understanding differences between structured and unstructured data are some of the skills that people should have, they are much less tangible and there is often an assumption that you should naturally understand data. Understanding is a harder skill to identify, but it’s also a critical one for today’s organisations. Skills can either tend to be taken for granted or the individual is really left to take responsibility for their own training and companies have a responsibility to identify that.

What are some of the key data skills that can have an impact on performance?

Graham shared with us a useful list of six key sets of data skills that he always looks for in a leader to have an impact on performance:

  1. To have an understanding of the potential value of the data and having a mature data capability, for example, understanding the benefits so that you can be an advocate for it.
  2. To have a broad understanding of the key concepts, for example, an understanding that descriptive analytics looks backwards, the predictive analytics leaps forwards and makes predictions about the future.
  3. To be able to have a good conversation with the data analysts and to ask the right questions.
  4. To have a good understanding of the ethical, legal and moral aspects of data and data science.
  5. To understand that managers and leaders, are the key points in the chain; if dashboards and predictions aren’t taken on board by leaders and used to influence and drive decisions, the best data models in the world will stay on the shelf.
  6. To have meaningful diversity in recruitment. Not only is data science a multifaceted role, but you need a diverse team to avoid bias in your data model.

Are there softer skills that are equally important for leaders to be data led?

Kim Nilsson asserted that soft skills is all about communication, change management and empathy. There is often a need in organisations for a translator role that can relate between data scientists and the executive board to explain what the team is achieving and so it’s important to being able to tell a story with data through visualisations such as reports or presentations. Non-technical people also need to understand that there is a benefit in working with machines such as AI, and is something that will help them in their work. There is huge value in making people understand and embrace change. Finally Kim explains that we do need recognise that there are ethical concerns about what AI can do. It is important for a data leader to have compassion and empathy for the stakeholders involved in a project, considering aspects such as how can everyone get the best possible experience from an AI project?

Additionally, individuals within professional environments often make decisions based on intuition. Graham refered to the psychologist Daniel Kahneman, who says ‘intuition is thinking that you know, without knowing what you do’ however if you’re going to make a call for acquisition, you can’t do it based on intuition. One needs to know what the data is telling you as well as understanding the biases that come into play for all of us such as confirmation bias, availability bias and short-termism.

“My view is that leaders should  be developing and understanding the potential pitfalls of only intuitive decisions and also getting to know their own and their teams conscious and unconscious biases, which is all psychological. I think they’re amongst the most important soft skills around data science and often overlooked.” summarised Graham.

How important should concepts such as machine learning and AI to non-data experts within an organisation?

Leaders do not need to understand exactly how machine learning works or how you implement it, but it may be useful to identify different types and when you would use one versus the other. Overall, everyone should at least understand what is it and why is it important to the organisation.

Graham spoke about algorithms within AI and how the same algorithm with no variables apart from the data could for example predict which patients are likely to develop kidney disease, or could predict whether two people are likely to be compatible partners on a on a matchmaking websites. It is only the data in these scenarios that changes and when that understanding is in place you can then start to match that with the problems the organisation is facing in order to work out what sorts of issues you can solve.

What is the impact for digital leads if the broader workforce ups their game in terms of data literacy?

Overall, you can speed up your development and get to outcomes that really matter for the organisation, however there is a risk then that some people will be left behind. We should be mindful that everyone gets to stay at roughly the same level for the success of the project and the success of the team.

Graham asserted, “I think as the broad workforce does become more informed, it is going to create a sharp increase in demand for data projects. Digital leads do need to be prepared for an onslaught of projects and potentially have really high expectations. I think that success is contingent on digital leads engaging with a broader workforce, helping them to develop an understanding of the opportunities, but also to have an awareness of the limitations of data science and data projects, because ostensibly, every single data science project is a little piece of innovation, and with innovations is it always exploratory.”

What’s the best way for non-technical leaders to acquire new data skills?

Dan suggested that “The only way that you’re going to get those skills is by practising. There’s no substitute for poor practice.” Within the Corndel Data programme guides, key concepts are underpinned with theory and so combining that with practice is going to be the easiest way to gain those skills. Whatever level you need to get to there is an apprenticeship programme or end programme outside of the apprenticeship level to support that learning journey.

“Apprenticeships certainly are one very valuable route”

suggested Graham. Additionally attend events, sign up to e-shots from Harvard Business review and McKinsey insights, sign up for the data science forums in Quora. There is be a wealth of free information and all sorts of ways in which in which non-technical leaders can go about immersing themselves in this exciting area.

 


Thank you to our panelists and all those who attended.

This panel discussion highlighted the importance of data literate leaders in a data-rich world,

This talk is part of Corndel’s Leading with Data webinar series which is designed for mid-to-senior leaders who have an interest in the rising intersection of leadership and data skills.

About the Corndel/University of Nottingham partnership

Corndel has partnered with the University of Nottingham to offer UK employers the chance to develop sought-after data analytics and data science skills across the workforce.

Nottingham University Business School (NUBS) is placed in the top 150 Business Schools globally for business and management courses (QS World Ranking 2020).  In The Economist 2021 full-time MBA ranking, published in January this year, NUBS is placed 3rd in the UK and 55th globally for its MBA programme. 

Corndel delivers brilliant training in Leadership and Technology to the UK’s largest companies. Our training is tailored to each company’s bespoke objectives and delivers measurable benefits. We offer training in Leadership and Management, Project Management, Data Analytics, Data Essentials, Software Engineering, DevOps Engineering and Fundraising.