Background
So much emphasis has been placed on the need to recruit candidates from science, technology, engineering and mathematics (STEM) backgrounds into the data and analytics function that demand now far exceeds supply – jobs board Indeed identified that there were 2.4 open positions for every one candidate, for example. Data scientists, data engineers and data analysts are necessarily the base on which a D&A team gets built. Finding those practitioners is urgent if you are starting from scratch or trying to scale up to deliver the datafication part of a transformation programme.
But this is only to consider the skills needed during the initial cycle or for a certain size of team. A recent discussion with DataIQ Leaders members identified ten as the tipping point at which scale and skills begin to shift focus. A consensus view was that until the size of the team reaches ten or more, soft skills and humanities do not feature in the recruitment process.
A team of ten is the tipping point when scale and skills begin to shift focus.
Once this size is reached, an important change happens – notably that no one individual is left to work on a project on their own. Instead, a blended team becomes important as more process gets applied and the nature of analytical projects being undertaken moves from the routine to the more transformational.
Another important change also happens as the second generation of practitioners is hired in at a junior level – senior practitioners start to take on more of a managerial role and give up their hands-on involvement (which can itself be a challenge for those inidividuals). Enabling those juniors to have active involvement and support from senior managers makes a big difference to their effectiveness, productivity and tenure.
Scale-up also gives rise to a need to involve members of the existing team in defining the skills set required and also in the fit of a candidate – that elusive issue of “chemistry” between individuals. Achieving buy-in from the team to a new hire will accelerate their induction and rate of productivity.
At this point, recruitment stops being a check-list of technical skills which a candidate needs to possess and starts to focus more on the individual’s character and personality. (This also happens to incumbent practitioners who are often recognised as requiring support and development for their soft skills, such as that offered within the DataIQ Leaders programme.)
Balancing soft and hard skills
The need to re-assess the skills mix and review the performance of an incumbent, first-generation practitioner was highlighted by a leisure and hospitality group which merged its research, data and insight teams into a data science function. The vision was to tackle all of the questions which the business might want to answer across pricing, service, human resources, customers, etc.
At this point, cultural and personality issues started to emerge. The D&A function is only able to move as fast as the organisation’s capacity to absorb and operationalise its outputs. Conversely, analytics can be too focused on techniques, rather than on servicing the business need.
To resolve this, the D&A function in question has changed its culture and switched emphasis from data science to problem-solving where the technique used is driven by the business problem. This has also highlighted the important of soft skills alongside the tools and techniques being deployed. One important consquence resulted from this review of how the function works.
A data scientist who was one of the first recruits during the start-up phase had been considered to be lacking in the necessary soft skills to engage with the line of business – letting that practitioner go was a pending decision. But by looking across the skills base as a whole and considering how to work as a team, rather than a group of individuals, it was recognised that the data scientist could be allowed to continue to practice in a “back room” fashion with little or no client interface, but partnered with a more account management-oriented colleague to act as the client-facing member of the team.
The data and analytics function needs to “feel like a family”.
This cultural dimension within the D&A function is key, with many leaders describing the need for it to “feel like a family”. It is a common mistake, however, to think that “feels like a family” mandates homogenic recruitment of just technically-oriented practitioners — families contain very different personality types, but are held together by a common bond. As well as creating a positive working environment for the function, it is also noted that this is part of the vision which the leader sells to the business and is often what attracts new talent to the organisation.
Senior leaders themselves need to be well-rounded and capable of managing people and teams, as well as assessing their output. As this is not something that is required of junior practitioners, it does not get hired for and therefore has to be both trained for at a later stage in their development and also engineered into place as the function scales up.
Identifying these characteristics during recruitment is not straightforward and may require the use of a structured personality profiling tool.
Recruiting the crossover skills set – Psychology
Shifting the focus of recruitment is a strategy that not only reflects the massive increase on the demand side, but also historical and ongoing constraints on the supply side. A decade-long study of trends in higher education carried out by Universities UK and published in 2015 revealed an encouraging upswing of 38.2% in students taking Mathematical Sciences (see Figure 1) as well as Physical Sciences (24.2%) and Engineering (20.4%).
But at the same time, the number of enrolments onto Computer Sciences courses fell by 28.7%. Across the period 2004-05 to 2013-14, total applications to universities rose by 20%. So the growth in certain STEM subjects is really only running just ahead of the total increase in higher education. More importantly, the scale of pull-through from data and analytics functions looking to recuit has exploded in the same period.
As those D&A functions start to mature and enter their second phase, alternative courses start to emerge as valid alternatives for candidates. Psychology should be considered as one of the primary qualifications to help teams balance out their humanities and sciences backgrounds because it specifically operates in this space.
According to Which? University, universities offering psychology degrees usually look for one or more A-levels in biology, chemisty, physics or maths. At the same time, they also look for students to be studying English, history or general studies. Candidates who are successful in their application and earning a degree emerge with a balanced view of both the scientific bases for understanding human behaviour and also the more nuanced view of irrational drivers of behaviour – an ideal perspective for D&A.
Another strong motivator for looking at psychology graduates is their growing number – in 2014, 106,000 qualified, compared to 80,000 in 2007, a rise of 32.5%.
Finding the crossover mindset – The Mediator
Personality profiling is still not common currency in HR and, where it has been adopted, tends to be the preserve of large organisations. There are also criticisms of different profiling techniques that they only measure an individual in a given circumstance or that the tests can be gamed to achieve a desired result.
Despite this, there are reasons to consider using profiling, especially when taking the D&A function to the next phase and seeking to balance out the skills sets and personalities it contains. In this respect, testing for the INFP personality using Myers-Briggs may deliver benefits. This profile is called “The Mediator” which gives a strong clue as to its value in this function.
A quick review of the literature about this personality type will reveal why it is of value. For one thing, it is rare – around 2% of the population – and is therefore unlikely to have been recruited into any team with fewer than 50 members.
More significantly, it is the way in which Mediators listen, interpret and communicate between groups that makes them a valuable addition. Not detail-oriented in themselves, they can embrace passion projects and are quick to understand what these mean to others. Most mediators have schooled themselves to fit into environments in order to avoid conflict – it is their ability to mediate and dilute conflict that makes them valuable.
A look at the career paths which INFPs typically follow, such as this one on the Indeed jobs board, will show why your D&A function is unlikely to already include a Mediator. More overtly creative opportunities are likely to draw them in. Yet the second phase of datification and data-driven transformation involves highly-creative thinking and insights – increasinlgy, these will appeal to this personality type as more jobs emerge.
Why you need to introduce a new type to your team
Individuals who have been hired for their specialist skills were widely identified by DataIQ Leaders members as being among the poorest-performing employees because their skills set is too narrow for them to develop outside of very specific task sets. Successful employees need a much broader range of skills and the ability to take a 360-degree view of the business and its problems.
A good example of this emerging as a specific role can be found in that of the data storyteller (and to a large extent through data visualisation).
This is where the skills developed by psychologists or the character displayed by INFP personalities can come into play. Empathy and intuition are at the heart of storytelling to ensure the audience is understanding the key takeaways from the message. Providing an insight that had previously remained hidden is the sort of outcome that excites these two groups.
As D&A functions mature and find competing for the right talent an increasing challenge in a crowded employment market, it is not just the techniques of recruitment that need to be adapted, it is the nature of the candidates being considered that also has to change. Seeing beyond a list of technical skills to how an individual will engage with the team, its leader and its line of business clients should result in a new profile being considered.
3. Emotional shock and the cycle of grief
Data and analytics practitioners are typically rational types, left brain thinkers who look for evidence and patterns. This is implicit to the personality profile that is drawn to STEM (science, technology, engineering, mathematics) rather than humanities subjects. But there is a consequence of this – they can struggle to recognise emotional responses in others and may not have developed coping strategies.
Once practitioners move into management and leadership positions, they are likely to be exposed to such reactions to a much greater extent, especially if they are driving a data and analytics-led transformation which will impact on the way other individuals in the business work. The DataIQ Leaders discussion provided a number of scenarios when such obstacles have arisen:
- A manufacturing CEO who is not data literate and picks up on any data quality issues as evidence that data and analytics can be ignored.
- A media company which has limited resources and multiple brands, meaning that support has to be withdrawn for a considerable period of time from some of them after they have reached a specific milestone.
- A roadside service provider which has been through multiple, uncompleted transformations and now faces scepticism and an attitude of, “we tried that before, it didn’t work.”
Being confronted with an emotional response can be difficult to cope with, especially if it is a heightened one, such as anger, or one that results in physical upset, such as crying. Understanding why this happens and being prepared, both with personal coping strategies and specific tactics for the individual, will help to mitigate the effect.
There is an underlying pattern to this behaviour which was first identified by the psychologist Elizabeth Kübler-Ross in her book “On death and dying” (see Figure 4). Commonly referred to as the Kübler-Ross model, these phases can be recognised in any situation where an individual is facing an event that causes a shock. Significant changes to work patterns or even the loss of a job are just as likely to be triggers as bereavement.
While data and analytics leaders are not therapists, they do need to be coaches and have the capacity to guide those they engage with towards some degree of coping and resolution. Helping an emotional individual towards catharsis and then, ideally, acceptance can lead to deep feelings of gratitude and even added loyalty towards that leader. Any practitioner who has been through a challenging – or even failed – project is likely to recognise both the emotional curves and the effect which they can produce.
Solutions:
- Accept that data and analytics-led transformation can trigger an emotional response
- Develop coping strategies to help when faced with an individual experiencing shock
- Establish coaching programmes to support those individuals as they process the emotion
