Data Conference 2020: Stand out from the crowd

To lead the next ‘gold rush’, senior executives need to understand the value of data, and ensure their organisations have the right people with the right skills, writes David Lee.

Most senior business executives lack a real understanding of the value of data, but those who do grasp the opportunity can lead “the next gold rush”, the conference’s Enough About Data, Let’s Talk About People session heard.

Darren Seymour-Russell, head of data at Mudano – part of Accenture Applied Intelligence – said that strong data leadership was not well embedded in the corporate world.

“We find understanding of data decreases quite rapidly as we go up the ranks in organisations on the whole and that concerns us,” he said.

“Leaders who don’t understand the power and tremendous potential of data… are losing competitive advantage to leaders who do.

“Fortune favours the brave in this world. We already have standard applications in data; the next gold rush will be those organisations and businesses who discover totally new applications, new ways of doing things, more efficient ways of doing things, to give even greater insight.

“Great leadership is empowering your organisation to take those risks, learn from mistakes and keep trying until you find the next unicorn application of data and the technology around it.”

Katy Guthrie, head of data science at trade association ScotlandIS, agreed that strong data leadership was “patchy”, and added: “There are not as many chief data officers [CDOs] as we would have expected. Even then, a CDO tends to be embedded in IT when it’s more a business role.”

Firas Khnaisser, chairman of DMA Scotland, told the virtual conference that there is a big role for “data people becoming better storytellers and telling that story back to the business”. This would help improve internal talent pipelines in companies, he stressed.

Khnaisser also said that untapped talent was the silver bullet to drive data skills forward: “We must stay open to non-data people from other fields. Data is a very open space where other skill sets are appreciated.”

Seymour-Russell argued that this demanded radical action, saying: “We need a talent pipeline coming in but there are far too many barriers on gender, economic circumstances and background. Until we really take them down, I think we will struggle.”

DMA Scotland this week publishes its new Democratisation of Scotland’s Data Talent paper. Khnaisser said we had to accept that inclusivity and diversity were “not just tick-boxes”.

He also urged delegates to see the people beyond the data: “All the metrics sitting behind data-driven innovation have people behind them.”

Katy Guthrie said data skills were constantly highlighted as an economic priority, but called for a broader view.

She said: “When we look at data science and AI skills, we often do that at the expense of data management and engineering skills, which are less glamorous but vitally important to maximise value.”

Guthrie also stressed that “meta-skills”, such as critical thinking and the ability to self-learn, were crucial: “Without the ability to fully grasp the problem, evaluate options and communicate outcomes, technical skills are not much use.”

She also highlighted the need for data skills in non-data jobs, and said Borders College had acted on this by embedding data modules in hospitality and beauty therapy courses. She added: “Tech and data skills are a massive enabler for our economic recovery and we need to upskill and reskill our current workforce and the workforce of the future.”

Seymour-Russell said skills training had to be appropriate: “Training for a beginner data scientist will be very technically-based. Training for senior executives will be much broader and lighter.”

He added that the way training is delivered is also crucial, whether that is structured, social or experiential, saying: “The ideal combination varies, by person, by role and age, but we’ve had real success in using experiential learning.

“We bring people in to show them what it’s like to be a data scientist and teach them the basics of data science, machine learning and artificial intelligence; then they get to go away and experiment.

“With senior-level execs in particular, that’s a very effective way of embedding learning and keeping it relevant.”

A message from the Editor:

Thank you for reading this article. We're more reliant on your support than ever as the shift in consumer habits brought about by coronavirus impacts our advertisers.

If you haven't already, please consider supporting our trusted, fact-checked journalism by taking out a digital subscription at https://www.scotsman.com/subscriptions.