Hiring and retaining data talent
Our Digital Masters Data Roundtable provided an opportunity for top data executives from a variety of consumer-facing businesses to share valuable insight and learnings with one another. Attendees represented a variety of businesses, from pure-play businesses to companies in transformation, including ASOS, Barclays, Burberry, Countrywide, eBay, lastminute.com, Shop Direct and Sky.
Regardless of the age of the organisation, or sector that it operates in, data science can provide critical competitive advantage. Whether it is analysing and predicting customer behavior to optimise for growth, or identifying and implementing operational efficiencies, companies are increasingly looking to data scientists to help solve key challenges.
As a result, this talent is in exceptionally high demand, and finding and retaining the best people is a constant challenge. Companies first need to understand the specific mindset of the data scientist to create compelling propositions that prevent them from thinking “I could do this job anywhere”.
Employers need to move quickly when they uncover impressive candidates, circumventing standard hiring processes where necessary with technical aptitude tests to fast-track hiring. Organising events with the wider data science community, and supporting part-time academic learning are also great ways of developing an attractive environment for data talent.
As Data becomes more established as a function, questions arise around where it fits within an organisation. A key challenge is deciding who should own data, or even if there should be centralised ownership at all. Whilst single ownership can optimise expertise, different stakeholders across the business often require a variety of specific insights. A matrixed model, where certain data remains centralised, and other parts are federated, is perhaps the answer.
It is vital for data scientists to have access to the key business decision makers in order to succeed. In more digital businesses, they will often work alongside product managers to combine skills sets for best results. Legacy businesses could learn from this approach.
Wherever data sits within a business, it still needs a C-Level champion to translate to the boardroom. The propensity for hiring a Chief Data Officer has increased over the past few years, and effective data leaders should be able to translate “what the data is saying” to “what customers need”, separating results from technical complexities. This can help achieve buy-in from senior management, and the use of data to drive the business forward.
