Promoting data science from within

Liz Matthews’ guide to building data literacy from the IT team out.

Reading time: 6 minutes

Demand for data science and analytics in the UK has grown by 231% over the last five and half years. That was the conclusion of a 2019 Royal Society report on data science skills in the UK workforce.

At the same time, data literacy remains low. Globally, 74% of employees report feeling overwhelmed or unhappy when working with data.

Europe in general is notably behind the US, India and Australia for overall data literacy, and this so-called “data overload” that today’s focus on data but lack of skills and confidence causes is estimated to lead to a £10bn productivity cost in the UK alone.

The problem is that while data-driven organisations can deliver a higher total enterprise value of 3-5% according to Accenture and Qlik, the number of people skilled in reading, interpreting and working with data aren’t yet emerging from tertiary education at the rate required to meet demand.

This means that in order to fill the gulf between needs and reality, businesses now need to foster resources from within, rather than relying on hiring alone to solve the data skills problem.

The IT team, no doubt the most technically savvy in the organisation, needs to be a vital part of this – spearheaded by the appropriate parts of the C-suite.

This means shifting from working in its own silo to working across the organisation on building innovation and confidence in data science initiatives and data practices in general.

But how do you turn one team into a cross-departmental working group?

Establish the baseline

Before working out how to expand and encourage data literacy within the organisation, it’s important to take stock of the existing skillsets – both technical and non-technical – and find a way to connect these people.

Businesses need to foster resources from within, rather than relying on hiring to solve the data skills problem

By uniting people capable of building new data solutions with stakeholders in other departments, as well as enthusiasts who are intellectually curious about the potential of data, project leads and their IT teams begin to form cross-departmental working groups that have more diversity of thought to address different business scenarios.

Bringing these people together also enables teams to collaborate on what tools are already in use, any complementary skillsets already available in other departments, and a common understanding of the additional tools and resources needed.

It allows for knowledge-sharing that allows pockets of expertise to spread into wider swathes of established best practice.

Then it’s a case of encouraging and supporting these early-adopters of the data science initiative. Internal hackathons with cross-department groups encourages unsiloed approaches to solving problems, and Kaggle-style challenges can help showcase unorthodox solutions to common problems.

These types of events also provide opportunities to highlight work from the IT team and other departments that provides a benchmark for success and encourages innovation towards new solutions.

Work to build together – across the organisation

From this point of an initial early-adopter base, project leads can look to build wider consensus and reduce the overwhelming feeling that many employees have towards data. By having the data science group work with business stakeholders two things can happen:

First, it ensures that the tools the technical divisions of the data science team are working on are relevant for the business stakeholders – that is, they genuinely solve a problem the user has, and in a way that it is intuitive and easy for them to use and succeed with.

Second, it takes the “mystery” out of the data science process for those business users, allowing them to understand better what solutions are or are not possible, and why.

By having technical and non-technical professionals working together in an ongoing dialogue, it allows the entire organisation to play a part in the data science initiative without feeling overwhelmed by an expectation to work directly with and interact with data head-on.

It also reduces the pressure on established “data science professionals” to somehow deliver on the full weight of the data science initiative – a likely impossible endeavour.

On a practical level, this is achieved by organising short workshops to discuss challenges and the best way to solve them.

The more the technical team share their insights with the business, the more skills are encouraged to develop throughout the workforce

By having business users sit with technical teams to explain their challenges, what information they need, and how they can usefully receive it, it means technical teams can work to build solutions that support users with the right data in the right format at the right time.

This helps keep data science tasks manageable for everyone – with a clearer set of expectations.

Meanwhile, it can also help highlight which users could benefit from more training, to extract more value from their roles through interacting with data in new and different ways.

Similarly, the more the technical team share their insights and expertise with the business, the more technical skills are encouraged to develop throughout the workforce.

Build for ongoing change

It’s important that from the outset, the C-suite lead their data science teams in an understanding that innovation is constant, and that this process of change has to span both technical and non-technical users.

Business needs change, which means two things:

Change 1

The technical teams need to be constantly working to integrate and design new tools for the business that deliver on new problems.

Change 2

Possibly more importantly, the business has enough knowledge of and trust in data science to be able to accurately reflect and act on the findings of data-driven insights.

This means that ongoing collaboration has to also deliver training from the IT teams to users, and indeed vice versa, to keep a constant growing culture of data literacy.

It also reinforces that simply externally hiring to fill every skill gap may be unsustainable and costly long-term; if the needs of the business will constantly change, you need your existing professionals to adapt with it or you’ll be faced with an unending task to replace and re-staff each project.



Delivering success

By making it clear that being part of the data science initiative doesn’t have to mean everyone plumbing new technical depths, organisations have the best possible chance of delivering successful data-science initiatives.

However, this relies on constant communication and demystification that cuts through the fear and apathy that business users often bring to the data conversation.

This has to be driven by those within the IT team with the technical awareness of the tools to enable data science, accompanied by the wider community of evangelists throughout the organisation – led by the C-suite downwards.

With these initiatives, IT teams and their C-Suite leaders can be a fundamental asset in both meeting the data science skills gap and building a culture of data science across the organisation.


About the author

Liz Matthews is head of community and education at Mango Solutions


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