Companies are fighting for the best data science talent, and it’s a battle fought in every sector. Using Big Data to drive efficiency and gain meaningful forecasting insights is transforming the supply chain, infrastructure and energy sectors. When Organisations fail to build a strong data and analytic competency across its business functions, they get left behind. So, whether its data on weather, social media, geological activities, or material handling – the challenge for business is not only to access the vast pools of data, but make sense of it in a meaningful and actionable way too. In the 21st Century, data, is like oil in the 18th century – ‘An immensely valuable but largely untapped asset’ (Wired 2014).
Recruiters need to start prospecting for digital talent
Tapping into that asset requires highly specific and hard to find skills. Organisations trying to build their own analytic capabilities, quickly realize the scarcity of properly qualified digital talent. Identifying, attracting and retaining Data Scientists needs to become an integral part of HR strategy if you want to stand a chance of accessing the right skills.
What skills do companies need from Data Scientists and why are they so hard to find?
An understanding of maths, data and analytics alone is not enough to translate any real benefits to business. It is the combination of technical skills and business acumen that is key to success. Lets take a look at those skills:
• Understand business processes, people and systems – Data is only worthwhile if its channeled into actionable insight. Without understanding the business processes and systems of the organisation, data lacks meaning. Business acumen is essential.
• Technical knowledge, including scientific and mathematical – Ideally this should include both a knowledge of statistics and code alongside an understanding of bias, causation, forecasting and significance testing.
• Communication Skills – The value of data and statistical insight rests on the ability to communicate and inspire the business leads of the organisation. Don’t underestimate the importance of being able to influence front-line business operations.
• Imagination and Creativity – Transforming data into a story and consequently, giving it both context and framework, is part of the alchemy that shifts big data into an understandable business narrative – accessible even to the lay-person.
The real battle ground for Data Scientists
For many organizations, it’s not just the difficulty finding people with this unique set of skills that is problematic – it’s the competition they face attracting them to their company. The skills may be transferable, and data scientists and analysts do move relatively seamlessly from one industry to another, but this talent pool may not see their natural habitat as working for an oil and gas major. Attraction strategies must recognize the competition – the likes of Google and Microsoft. They are offering the high salaries, the flexible working conditions, and community of like-minded ‘Superheros’
A recent McKinsey Global Institute Report estimated that the US alone will face a shortage of 250,000 data scientists by 2024. Satyam Privadarshy, Chief Data Scientist at Haliburton and who has vast experience of building data science teams recognizes that the oil and gas business is facing a major challenge with the talent gap. He notes that they are ‘competing for talent with startups and large tech firms’ and advises that growing a team means ‘giving them their space…or they move’ (JPT, 2//2/17)
How do you attract data scientists and analysts into the energy, infrastructure and supply chain sector?
• Upskilling existing workforce – Large organizations building their own analytic capabilities should look at investing in their existing staff. We find that effective teams are built around core skills and competencies. That’s why companies need to clearly identify what those are and provide training to bridge gaps in skill sets. Providing internal talent who have engineering or technical expertise, with the ability to move around in different functions, broadens their knowledge. Using, and upskilling existing staff has the benefit of retaining and utilizing already acquired industry expertise.
• Cross industry Hiring. Sector experience is not the critical factor in building a successful data science team and therefore should not be at the top of the requisition list in a sourcing strategy. Organisations that put critical functional and behavioral competencies before sector expertise are likely to have more success.
• Engaging with a passive talent pool. ‘Super-Heroes’ won’t seek you out which means your sourcing strategy must be proactive. Your message should be both compelling, and truthful. Easy enough if you have time to tap into passive markets. Then there’s the challenge of changing the mindset of the assessors which can be tricky. Understandably their default position is often to reject anyone without sector experience. They may have little practice in competency based assessment too, therefore needing training themselves. Engaging an external recruitment partner to manage all or part of the process is one solution. As is ensuring that all assessors have received rigorous assessment training.
• Attracting graduate talent – Building strong relationships with Universities in Maths, Statistics and Data Science is crucial to initiating early conversations with potential Data Scientists. Getting graduates with technical expertise on board at an early stage and investing in their development ensures a competitive advantage.
• Promotion of industry innovation – Companies need to improve the way they market opportunities for digital talent. They are quite literally transforming industry. Talking about the way data is improving ‘operational safety’, or ‘predicting production’ doesn’t capture the exciting ways data is being used. Ultimately, companies need to start telling the stories behind these innovations and future digital aspirations.
Telling your big data story and shaping the narrative
Atkins, the multinational engineering giant, are communicating their digital aspirations for the future particularly well. Their vision of being ‘digital by default’ (Richard Cross, Atkins CIO) is especially relevant here. Take a look at the future of digital engineering for Atkins in 2020.
To compete for this highly skilled, qualified and sought after talent pool, there must be an effective HR Strategy. A strategy that is closely aligned to the commercial goals of the business, and with eyes firmly focused on the future. A very exciting future!
Can we help?
Are you developing your digital talent pipeline or interested in hiring a data scientist externally and need some help? To discuss, please contact James Usher, Managing Director – email@example.com or call (0)161 967 9670