Improving your practitioners’ soft skills from levels 1/2 to levels 3/4
Background
Talent acquisition is difficult. The pipeline of available candidates graduating from relevant degrees, Masters and PhDs continues to be constrained due to historical factors, even as academia accelerates its provision of business analytics and data science courses. According to the jobs board
Indeed, demand for knowledge workers able to undertake artificial intelligence and machine learning projects has risen nearly five-fold in three years, with 2.3 open positions for every available applicant.
One response by recruiters and employers has been to hone job specifications ever more tightly. Job ads can resemble shopping lists with extensive and highly-specific technical requirements, written in the hope that such a targeted approach will catch a specific candidate, rather than spreading the net broadly and hoping it will catch them among the shoals of potential employees.
Two problems arise from this approach. The first is that, in reality, what the organisation needs may be a more general data analyst rather than the highly-specialised skills of a data scientist. The second is that it pays no attention to soft skills which will become fundamental to the way in which data and analytics practitioners engage with stakeholders across the business as well as within their own function. Brilliant PhDs are only as valuable as their ability to translate their knowledge into deliverable solutions, for example. Technically-proficient data engineers only bring value if they can understand the business problem they are asked to resolve.
This guidance whitepaper considers how organisations should map soft skills, support teams in developing them, incentivise improvements and measure these abilities from the top down. It is intended to help DataIQ Leaders members to improve their scores on these factors when undertaking a CARBON™ assessment.
Create a skills map for your current needs
If talent acquisition is difficult, it is made all the harder if your organisation does not know what skills it is looking to recruit or needs to develop as practitioners progress in their careers. It is typical of companies that are at Level 1 (Aware) in their strategy that there are no skills maps in place. Even at the next step, Level 2 (Repeatable), skills tend to be defined as and when a need arises. The problem with this is that there is no consistency across hiring or any ability to repeat successful processes.
Starting to create a skills map is a significant step up and takes an organisation towards Level 3 (Defined). Here, core skills sets are set down and recognised as essential. The gap at this stage tends to be in not aligning those skills with specific roles or failing to see any overlaps or gaps. Mapping skills should therefore be seen as an early part of creating job families and hierarchies.
Once each role within the data and analytics function has been defined and the skills required to carry it out have been identified, the organisation will be at Level 4 (Managed). Only the cycle time for maintaining these definitions prevents the skills map from being an optimal strategic tool – if this is introduced and then shared with HR during recruitment, the company will achieve Level 5 (Optimised).
Look forward at the skills map for the future
Skills required within the data and analytics function are constantly evolving, especially technical knowledge as new applications arise and potentially new techniques are developed in academia. Even soft skills need to be kept up-to-date – the internal culture of an organisation will mature (or regress) as it moves to be data-driven and adopt evidence-based decision-making. How the function interacts with the business needs to flex as this happens.
By failing to look ahead, organisations can find themselves stuck at Level 1 (Aware) with no view as to what skills they will need even at the one-year horizon, let alone three to five years out. Even those at Level 2 (Repeatable) are typically only considering their skills requirement within the current 12-month cycle, rather than considering what will be needed next once those open roles have been filled and new hires have begun their career progression.
As the data and analytics resource grows and becomes more capable, needs will open up that can be identified some distance out. This is typical of a Level 3 (Defined) organisation which makes ad-hoc guesses at what it will need to recruit for in the next 12-month cycle.
Maturity really only dawns within Level 4 (Managed) as they set a rolling horizon for one year out, planning what needs will be addressed next year while recruiting for this year, rather than waiting to be within that window before deciding. This is clearly a more planned and structured approach that can also be applied to developing existing practitioners in the second or third years, as well as identifying where managers and leaders need to be planning to build on their skills base. Once this horizon line gets set at a distance of two to three-years – aligned with business strategy planning and recognising the potential to develop talent within apprenticeships or while still pursuing graduate studies – the company will be at Level 5 (Optimised).
Put in place support for soft skills development
At the heart of DataIQ Leaders is the perception that soft skills are often neglected as a result of the emphasis on technical competency within the data and analytics function. Evolving technical skills is an important and ongoing challenge, but it will not yield the expected benefits if team members are unable to engage with each other or stakeholders across the business. Even leaders will find their career progression checked if they step around from behind the screen to in front of it without the requisite abilities, from emotional intelligence to inter-personal relationship building. Many chief data officers, chief analytics officers and their peers find this a difficult stage.
In the early stages of building a team, there will typically be no focus at all on soft skills (Level 1 – Aware) or only ad-hoc support (Level 2 – Repeatable), such as buying-in some training. Practitioners will have to find their own ways of building these competencies, assuming they are conscious of the need to possess them, which may not be triggered if the organisation itself is not aware.
Typically, this understanding only starts to emerge once the data and analytics function develops a level of “consciousness” – that is, when it starts to identify the tasks it wants to undertake for the business pro-actively, rather than being reactive to business requests and delivering routine reporting. This is when soft skills development comes into focus as practitioners start to engage with the business or find themselves embedded within lines of business.
At this point, soft skills support may be provided on request from individuals in Level 3 (Defined) organisations, while the next step will see the function leader provided with a budget for this area (Level 4 – Managed). Ultimately at Level 5 (Optimised), maturity within the function and the organisation itself should lead to a commitment to soft skills training, such as through membership of DataIQ Leaders, or even the creation of an in-house academy which includes this alongside technical skills development.
Put in place incentives for developing soft skills
While measures of soft dimensions of work are notoriously difficult to take, the fundamental importance of soft skills to the impact which the data and analytics function will have on the business mean their quality can not be ignored. While Level 1 (Aware) companies will not have any such metrics in place, some informal measures will start to emerge at Level 2 (Repeatable). Typically, these are likely to be internal KPIs set by the leader of the data and analytics function, but which are not communicated externally. The rewards for meeting them are likely to be soft themselves, such as awards or team away days.
Incentives that have a genuine impact on practitioner behaviour and engagement with training only really come into play at Level 3 (Defined) when they are defined by the leader and shared with HR. If metrics form part of the employment contract, they become meaningful. An important evolution of this is for the data and analytics function leader to have discretionary budget to assign as financial bonuses to team members (Level 4 – Managed). Optimally, this will become a formal part of the assessment and reward structure within the organisation at Level 5 (Optimised).
Assess data and analytics leaders for soft skills
Chief data officers and chief analytics officers are often pioneers within organisations, establishing a new practice for which there is a vision and clearly-defined set of goals to deliver for the business. Rarely are CDOs or CAOs viewed through the lens of their soft skills – how they engage with other leaders across the business, how well they manage and communicate with their team members, etc. With no assessment (Level 1 – Aware) or only informal measures (Level 2 – Repeatable) it is possible to miss out on the benefits of a data and analytics function because its leader is not achieving a positive engagement with stakeholders or has the wrong personality type to fit in with the organisation.
Leaders may start to assess their own soft skills at Level 3 (Defined) because they want to understand more about themselves as a manager. Eventually, these need to become specific metrics that the CDO or CAO is assessed against, with that performance being part of how the business views them (Level 4 – Managed). Ultimately, the organisation as a whole should embed soft skills metrics into the way it assess leaders across every function (Level 5 – Optimised).
Assess data and analytics practitioners for soft skills
During the initial phases of building a data and analytics function, or in the early years of a practitioner’s employment in it, there will often be no assessment of their soft skills (Level 1 – Aware). At some point, ad-hoc measures may arise, but these are often just sense-checking their fit within the team or chemistry with other members (Level 2 – Repeatable), rather than being based on an understanding of the appropriate framework of soft skills required.
Often, it takes some failures – either hires that do not work out or projects that run into difficulties – for the data and analytics function to become aware that it may need to put more assessment in place for soft skills where these were a contributory factor (Level 3 – Defined). As the function matures, so does the way it assesses candidates and practitioners for soft skills to ensure they align with what is required and are helping to future-proof the organisation (Level 4 – Managed). The ultimate level of maturity in a Level 5 (Optimised) organisation is for continuous assessment of all practitioners against these metrics.
Assessing the soft skills of the data and analytics function from the outside-in
Just as knowledge management is one of the most under-utilised ways of building long-term value for an organisation, so the external assessment of how a function behaves is one of the least practised methods of ensuring a good fit with the strategy of the business. DataIQ Leaders has recognised this and the next development in our toolkit, CARBON™ 360, will layer in assessments for the lines of business and stakeholders which the data and analytics function is serving.
The majority of organisations will have no such reporting in place and are likely to score as Level 1 (Aware). If there has been a negative experience with a project, they may have moved to Level 2 (Repeatable) where post-resolution investigation was applied to identify to what extent soft skills and people issues were the problem. If attempted, this is usually best undertaken within a “no blame” framework.
As the function leader, the CDO or CAO in maturing to Level 3 (Defined) is likely to want feedback on personal performance by the team from their internal clients. This can then become a shared process at Level 4 (Managed) where metrics are agreed in advance of projects and communicated across functional boundaries as well as vertically. Optimally at Level 5 (Optimised), this process of outside-in assessment can be formalised and shared as part of the agreed engagement and project management structure, with a built-in vision to use it as a level for continuous improvement.
