Bringing together leaders in the data analytics space, the La Fosse Data Leadership Forum event was an opportunity to examine the biggest challenges hindering this sector. Focusing on the main roadblocks to building out successful teams, we discussed data literacy, stakeholder buy-in, and the universal issue of skills gaps, plus the potential impact of AI.
Poor data literacy
Referring to a lack of understanding surrounding the role of data analytics across a business, poor data literacy is often a result of low visibility or engagement. Other teams are unclear about the work that data analytics teams put in, which can be difficult to demonstrate to those without a deep technical comprehension or a direct connection to the impact it has.
Building successful infrastructure commonly goes unnoticed; it’s only when there’s a failing or weakness within that infrastructure that the wider team feels the absence of good data.
“It’s the role of the data analysts to break down projects, present specific metrics, and connect the work they’re doing with individuals and teams across the business to bring a greater understanding and, therefore, appreciation for data professionals. They’ve got to be their own champions.”
If the business isn’t digital-first, it can be hard to properly present the benefits of data analysis across the many different functions it serves. Begin with a statement of intent or direction: are we answering a specific question or solving a specific problem? Who will be affected by the outcome? Are we building something new or further exploring something that already exists?
Utilising tools such as dashboards can help others to visualise objective data and why it’s such an important component, and demonstrating tangible outputs resonates more with colleagues who are less technically minded, with less understanding of the full data journey.
Risk intervention takes an alternative approach, outlining the possible negative outcomes associated with unavailable data or insight. Whilst many would rather promote positive effects, scare tactics are effective in driving home the importance of good data.
Simplifying concepts can be a great help when communicating value; something that may seem straightforward to the data team isn’t necessarily accessible to all. Using clear steps and outcomes allows others to recognise how data is having a measurable impact on their projects and the business as a whole.
Stakeholder buy-in
Support from stakeholders is key for any business function, but when it comes to elements like data science that are still considered ‘new’, it’s even more challenging to get buy-in.
It’s important to have allies within the leadership team who not only understand the impact of the work, but also communicate this effectively to the wider team. These senior stakeholders want to see real economic value, so using metrics that demonstrate a cost or time saving, or result in increased efficiency and output, can be instrumental in getting the buy-in that the data team needs.
“Whole business concepts are difficult to present – breaking down the data points into specific blocks and mapping projects using those blocks makes them easier to digest.”
Case studies and storytelling are other tools that help stakeholders to visualise outcomes, demonstrating real-life examples of how and where data analysis has resulted in a success story.
When it comes to the more senior stakeholders, putting a flagship product at the centre is often an easy win. Being able to pinpoint areas for improvement with the products that the business relies on generates interest, especially if there are tangible outcomes to the changes being made.
It’s also important to actively promote the data team overall. Building relationships that instil a culture of collaboration, being present, open, and involved makes a big difference with attitudes towards the data function.
Those relationships also establish trust with your stakeholders; facilitating conversations and mutual respect between the different parties means there’s a greater chance that everyone involved will feel they’re working towards a shared goal. Show flexibility with regard to others’ needs and ask for input.
Skills gaps
“Lots of people are technically great, but don’t have commercial understanding and awareness, can’t sit with stakeholders, or drive strategy and growth. Then, on the other hand, you find that most technical people want to stay technical, not be collaborative or client-facing.”
Whilst the skills-gap piece is experienced across most sectors at some point, placing for roles within data can present a specific challenge as a result of the technical and soft skill solution requirement. Those with technical expertise are not always equipped or motivated to manage stakeholders and propose strategy, and those with more understanding of the business perspective can be lacking in technical know-how.
Consider attracting candidates from alternative talent pools outside of data – those with different experiences, different passions, and different skills who can contribute to diversity of thought and approach to work. Those with a numerical mindset, but with the presence and confidence to communicate strategically with wider teams, often perform well.
It’s also important to remember that teams are made up of individuals. There will still be a place for a singularly technical individual and a singularly strategic individual; it’s about ensuring the balance across the division is correct. Team members who want to stay on the individual contributor path provide stability and longevity that should be highly valued.
“It’s about attitude over aptitude – you can teach technical skills, but you can’t teach curiosity, or how to think, or how to approach problems.”
AI and the future of data
AI is already widely considered the next innovation in tech, and its impact on the data landscape is unquestionably an important factor in that.
Building, maintaining, and adapting foundational systems will be a key element of its usability and success; trust in data warehouses will be fundamental, as will understanding the layers of capability and the effect AI application will have on different platforms and functions.
“AI tries to mimic humans, but humans are wrong all the time, so will it become normalised that AI is also wrong?”
The ability to map, analyse, and report on huge amounts of data at speed will potentially change the way these teams work. But as with any new technology, the capabilities and possible failures have not yet been fully applied or explored. Using AI alongside other analytics tools, creating a blended model, could give data analysts the best of both worlds.
Thanks to our event facilitator Kelly Freeman, Head of Data at World of Books Group, special guest Leo Pape, Co-founder at Point Sigma, and to all our attendees.
To find out more about La Fosse’s total talent solution, or for information on upcoming events, contact Kayla Usswald.