How to grow your own data scientists: a practical guide for the data-driven C-Suite
Demand for data and analytics skills is increasing, but existing leadership teams admit they often struggle with this. How can organisations upskill their existing teams while ensuring their future workforce is prepared?
Data today is the fuel driving the modern business world. It therefore stands to reason that the ability to read and speak the language of data should be a fairly mainstream skill, except it isn’t - yet.
A 2018 report by Qlik suggests that just 24% of business decision makers were fully confident in their abilities with data.
This is despite the fact that, according to the 2018 Gartner CIO Agenda, CIOs globally ranked analytics and business intelligence as the most critical technology to achieve the organisation’s business goals, with data and analytics skills topping the list as the most sought-after talent.
As more organisations embrace data-driven digital transformation, it’s clear that the need to upskill and resource data science teams has become far more pronounced.
With the gap seeming to only become wider, how can the C-suite continue to leverage data-driven digital transformation if there are insufficient resources to fill it? It’s clear that there’s a gulf between the skills on offer and those emerging from tertiary education, while the demand for data literacy remains high.
As a result, it’s becoming incumbent upon the businesses themselves, led by the C-suite, to champion the drive towards a more data-savvy future.
Fortunately, there is a positive trend emerging, as data science and analytics capabilities are being developed across a much wider range of sectors.
If that innovation is taking place in siloes, however, separated from the business, it’s likely that it still isn’t delivering the results you need - so what should a data-driven C-suite do?
Nurture existing resources
Pulling together existing disparate data science resources into a single, connected community of practice creates a secure foundation for the C-suite from which to grow its data scientists.
Having such a community means the business will have a better understanding of the skill sets that exist within the organisation already, as well as best practice examples for approaching different scenarios and a better awareness of the new tools and solutions that can be used.
Enabling training at all levels of data awareness will be critical, and this should even include training on how to use information to guide decision-making, and other non-technical topics.
All of this provides the most solid basis for working out where the talent pool needs to be extended with new hires or training.
Similarly, it’s important to encourage knowledge sharing and innovation within this community.
Organising team ‘hackathons’ to boost cross-function collaboration and new ideas can be a great example of this, while hosting internal events which showcase successes can help motivate the team to deliver creative new solutions.
Collaboration is key
Building relationships between the data team and the business is critical for two things: ensuring the data science team understands the business’ problems and is producing useful insights, and for helping to demystify the data science process.
Ensuring that the business as a whole has insight into the data available, how it can and cannot be used, and encouraging a dialogue between technical and non-technical professionals will foster curiosity and trust that ensures a productive data-driven culture.
How can the C-suite go about doing this? By organising short workshops that focus on delivering real ideas.
If the existing data scientists can show the business more broadly how data science techniques can lead to real business outcomes that improve success for a business area, it is likely to encourage enthusiasm and optimism about the potential of data.
As a result, this is more likely to drive members of the business team to look further into the types of analytics they might be able to learn and apply, as well as seek out both at-work and external training to support this.
Everyone, to some extent, needs to be data literate
Drew Conway’s famous Venn diagram on what data science is made of stressed the importance of substantive expertise as an integral part of data science – and encouraging a culture of collaboration between business and analytics function ensures that this is the case.
An argument can also be made in the reverse, however. Ensuring that the business has enough knowledge of data science to be able to accurately reflect and act on the findings of data-driven insights is just as important.
Without this, insights discovered by the data science community will not be able to have the fullest possible impact on the business, or, at worst, could even end up being misunderstood and misinterpreted.
Enabling training at all levels of data mastery and awareness will be critical, and this should even include training on how to use information to guide decision-making, and other non-technical topics.
This sort of training, repeatedly reinforced, will be essential for cutting through any data apathy that exists within the organisation.
It’s important that even those resistant to change understand that the business is developing to be data-driven, and that a culture shift will come as part of this.
Having a connected community of evangelists, both in the form of technical experts, and business enthusiasts who can continue to spread the message will be invaluable.
With these three steps, C-suite executives will find it far easier to grow data scientists throughout the organisation and invest more effectively in hiring new talent to fill any skills gaps.
By encouraging a culture of data curiosity, and an awareness of the power and potential of data, as well as an interest in learning more, businesses are creating fertile ground to inspire a new generation of data scientists from any background.
Interested in this topic? Read Strategic data: unlocking HR's secret weapon.
Liz Matthews is the Head of Community and Education at Mango Solutions, a UK based data science consultancy. Since 2010, she has led Mango’s training, marketing and events services, managing the delivery of training to over 1,000 people per year in data science technologies such as R...