The evolving role of data professionals
Back in the year 2000, I was a fresh-faced graduate, starting out in my first role in data and analytics. I joined Capital One UK as a statistician with the responsibility for solving problems using data and algorithms. The role was narrow (responsible for a small slice of a large end-to-end process) but also deep (requiring a strong understanding of scientific method, coding skill, and statistical expertise). It gave me a good understanding of a specific area of the organisation and a strong appreciation for the importance of data quality and defined processes.
Since that point, the expectations of data professionals have exponentially increased. The gigantic leaps forward in processing speed and the promise of seemingly limitless data storage mean that processes which would have taken days to run, can now be completed in minutes. The huge progress in these two dimensions has also opened up the opportunity to use different algorithms in ways never previously expected. This has led to an ever-greater demand and expectation that data and algorithms can revolutionise businesses.
Finding the unicorn
The challenge for organisations is ‘how do you find the unicorn?’. Someone who understands the business goals to prioritise impactful projects, can wrangle complex data, writes high-quality, error-free code, has an extensive knowledge of machine learning algorithms, is adept at model deployment to ensure decisions are correct and efficiently calculated, and can explain complex insights or models to a range of groups. The list is endless. The reality is that finding someone who can do all this is impossible – rather than searching for unicorns, organisations should look to build a unicorn team.
When talking about the make-up of a successful data team, I often use the analogy of a ‘broken comb’. You are looking for experts who bring an in-depth understanding in a relevant area of expertise. This could be anything from the ability to write elegant, efficient code to the skills required to influence key stakeholders through storytelling. It might include a rigorous approach to building and managing a process, a deep passion for understanding and meeting consumer needs, the ability to understand the strengths and weaknesses of a range of analytic methods, or the fortitude to manage diverse stakeholder relationships. All these aspects are important and whilst I would expect a deep understanding in one, there also needs to be awareness and respect for the others. Every team member should embody two key qualities: curiosity to understand the ‘why’ and an appreciation for the strengths of others in the team, ensuring an effective team where each individual, or ‘tooth’, contributes their skills and knowledge to finding solutions.
Building and developing a diverse team
So, how do you build this dynamic and diverse team? Firstly, you need to ensure you have a wide range of different channels open to you. The strong temptation when building out a data function is to target the technical skills and hire experts in algorithms. Team members with this expertise are important, but my advice would be to focus initially on two types of people. Firstly, you need people who can really understand the business strategy and how data and analytics can super-charge it. Secondly, you need all-rounders who can try their hand at all aspects and help train others. After this, you should look to add to the team from as diverse a set of sources as possible.
Finding the team is only the first step. A successful data and analytics team continues to evolve. This means having a focus on skills development and innovation. It is essential to have a well-structured approach to ensure all members of the team feel they have the space and support to evolve and adapt their skills to meet their career goals. People leaders play an important role here, helping every member of the team have open discussions about their aspirations and providing opportunities to experiment in a supportive environment.
The future
One thing is certain as we try to understand where the world of data and analytics will go in the next ten years. It will not stay the same. A curiosity for what’s next and a focus on developing new skills to meet these demands will be essential for the next generation of data professionals and organisations looking to succeed through the use of data and analytics.
