Building a high-performing data team: How to attract, develop and retain a best in class analytical function

According to Gartner, ninety percent of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency. However, many companies are struggling to staff their data teams, especially in core technical positions.

There is no doubt that we have an acute data science talent gap, which is exacerbated by the inability of organisations to effectively retain and develop data scientists. A recent skills survey, conducted amongst data professionals in the UK, revealed that over half of respondents planned to move roles within the next year. Over a quarter (28%) were frustrated at a lack of access to the right tools and over 50% said that they have no internal data science community within which to share an active role.

When competition for the best data science talent is so high, and data professionals are so few and far between, attracting, developing and retaining the rising stars is no mean feat.

Attract

If an organisation does not have a data-driven culture, it’s difficult to attract the right talent. Data experts are in a position to be picky.

Like any other component within a successful data-driven business, an efficient hiring strategy starts as an empirical process. Once a data-driven culture has been established, it’s important to be able to understand the different roles required to fulfil both short-term and long-term goals.

Adapting your hiring strategy and refining job specs can have a positive impact on the ability to attract the right people. A strong team requires complementary skillsets and personalities, so being clear about what you are looking for from each role is important.

Some of the core roles of a cross-functional data science team and key technical skills to look out for and assess include:

-        Data Scientist – machine learning, statistical modelling, predictive analytics (Python, possibly R or other languages)

-        Data Architect (platforms and environment: AWS, GCP, Azure)

-        Machine Learning Engineer/Back-end Software Engineer (Java/Scala, Python, SQL)

-        Data Engineer and ELT Developer (Python, Java/Scala, SQL)

-        Data Visualisation Developer/Front-end Engineer (JS, Tableau, Qlik)

-        DevOps Engineer (and Sysadmin, DBadmin)

In addition, all team members will need a working understanding of Git, Linux/bash, Docker, SQL, and Agile/Scrum working.

Defining how you are going to measure a candidate against the requirements of your job spec, organisation and team is critical – this should be done objectively and thoroughly, with data at its heart.

Develop

Any good data scientist will come with a wide skillset and will also be keen to learn and develop. Naturally curious and investigative, they are predictive operators who can move readily into a setting where they can deliver new value. If a company can define a clear vision of its data landscape as well as the outcomes it’s seeking to create by using data science, this is highly motivating. Giving a data scientist the empirical milestones to predict their own progression through the business is a sure-fire way to keep them committed.

Harvard Business Review described a data scientist as ‘a hybrid of data hacker, analyst, communicator, and trusted adviser’. Generally, it will be rare to find people with all of those skills combined, so providing the opportunity and resources necessary to visualise their own career path will keep your data scientists from getting itchy feet.

Retain

The success of a business transformation can be measured in the mindset of the team. A team in which every individual looks first to the data for a solution to any problem is a team that has embraced the concept of a data-driven mindset. This team will assess the data impact of change to a business process in terms of business results.

Productionising data analytics, i.e. bringing data into the everyday routine of your business, depends on every member of the team having a data-driven mindset. Embedding this data-first approach as the norm is key to success.

When the data science team feels that everyone within the business values data and the insight that can be derived from it, then the team will feel it is a critical cog in the organisational wheel, with purpose, respect and drive.

A culture in which every individual has the freedom to suggest solutions to problems and is empowered to make their voice heard is a powerful contributing factor to employee satisfaction. Known as a closed loop culture, it is synonymous with talent retention, an inclusive structure and a successful driver of business transformation.