Both work with clients, and my own experience in creating & leading analytics teams, has taught me that such a tool can help in a number of ways. In this post I’ll explain what I mean by a competency framework and the different ways it can help Analytics, Data Science or Customer Insight leaders.
I wonder if you’ve used such a tool in the past?
Across generalist roles & larger departments, the use of competencies has become the norm for many years, as HR professionals will attest. However, sometimes these definitions and descriptions feel to generic to be helpful to those leading more specialist of technical teams.
But, before I get into overcoming that limitation, let me be clear on definitions.
A dictionary definition of competency explains it as “the ability to do something successfully or efficiently”. In practice, in business, this usually means the identification of a combination of learnt skills (or sometimes aptitude) & knowledge that evidence that someone has that ability (normally to do elements of their job successfully). HR leaders have valued the ability for these to be separated from experience in a particular role, thus enabling transferable competencies to be identified (i.e. spotting an individual who could succeed at a quite different role).
Defining a competency framework
Building on this idea of competencies as building blocks, of the abilities needed to succeed in a role, comes the use of the term ‘competency framework’.
The often useful, MindTools site, defines a competency framework as:
“A competency framework defines the knowledge, skills, and attributes needed for people within an organisation. Each individual role will have its own set of competencies needed to perform the job effectively. To develop this framework, you need to have an in-depth understanding of the roles within your business.”
Given many Analytics leaders have come ‘up through the ranks’ of analyst roles, or are still designing & growing their functions, most have such an in-depth understanding of the roles within their teams. Perhaps because HR departments are keen to benefit from the efficiencies of standardised competencies across a large business, there appears to have been less work done on defining bespoke competencies for analytics teams.
Having done just that, both as a leader within a FTSE 100 bank and for clients of Laughlin Consultancy, I want to share what a missed opportunity this is. A competency framework designed to capture the diversity of competencies needed within Analytics teams has several benefits as we will come onto later. It also helps clarify the complexity of such breadth, as we touched upon for Data Science teams in an earlier post.
The contents of an Analytics competency framework
Different leaders will create different flavours of competency framework, depending on their emphasis & how they articulate different needs. However, those I have compared share more in common than divides them. So, in this section, I will share elements of the competency framework developed by Laughlin Consultancy to help our clients. Hopefully that usefully translates to your situation.
First, the structure of such a framework is normally a table. Often the columns represent different levels of maturity for each competency. For example, our columns include these levels of competency:
- None (no evidence of such a competency, or never tried)
- Basic (the level expected of a novice, e.g. graduate recruited to junior role)
- Developing (improving in this competency, making progress from basic ‘up the learning curve’)
- Advanced (reached a sufficient competency to be able to achieve all that is currently needed)
- Mastery (recognised as an expert in this competency, or ‘what good looks like’ & able to teach others)
Your maturity levels of ratings for each competency may differ, but most settle for a 5 point scale from none to expert.
Second, the rows of such a table identify the different competencies needed for a particular role, team or business. For our purposes, I will focus on the competencies identified within an Analytics team. Here again, language may vary, but the competency framework we use at Laughlin Consultancy identifies the need for the following broad competencies:
- Data Manipulation (including competencies for coding skills, ETL, data quality management, metadata knowledge & data project)
- Analytics (including competencies for Exploratory Data Analysis, descriptive, behavioural, predictive analytics & other statistics)
- Consultancy (including competencies for Presentation, Data Visualisation, Storytelling, Stakeholder Management, influence & action)
- Customer-Focus (including competencies for customer immersion, domain knowledge (past insights), engagement with needs)
- Risk-Focus (including competencies for data protection, industry regulation, GDPR, operational risk management)
- Commercial-Focus (including competencies for market insights, profit levers, financial performance, business strategy & SWOT)
- Applications (including competencies for strategy, CX, insight generation, proposition development, comms testing, marketing ROI)
Variations on those are needed for Data Science teams, Customer Insight teams & the different roles required by different organisational contexts. Additional technical (including research) skills competencies may need to be included. However, many are broadly similar and we find it helpful to draw upon a resource of common ‘holistic customer insight’ competencies to populate whichever framework is required.
If all that sounds very subjective, it is. However, more rigour can be brought to the process by the tool you use to assess individuals or roles against that table of possible scores for each competency. We find it helpful to deploy two tools to help with this process. The first is a questionnaire that can be completed by individuals and other stakeholders (esp. their line manager). By answering each question, that spreadsheet generates a score against each competency (based on our experience across multiple teams).
Another useful tool, especially for organisations new to this process, can be for an experience professional to conduct a combination of stakeholder interviews and review of current outputs. Laughlin Consultancy has conducted such consultancy work for a number of large organisations & it almost always reveals ‘blindspots’ as to apparent competencies or gaps that leaders may have missed.
However you design your scoring method, your goal should be a competency framework table & consistent audible scoring process. So, finally, let us turn to why you would bother. What are some of the benefits of developing such a tool?
Benefit 1: Assessing individual analysts’ performance
All managers learnt that there is no perfect performance management system. Most are, as Marshall Goldsmith once described them, stuff you have to put up with. However, within the subjectivity & bureaucracy that can surround such a process, it can really help both an analyst & their line manager to have a consistent tool to use to assess & track their development.
I have found a competency framework can help in two ways during ongoing management & development of analysts:
- Periodic (at least once a year) joint scoring of each analyst against the whole competency framework, followed by a discussion about different perspectives and where they want to improve. In this process remember also the greater benefit of playing to strengths rather than mainly focussing on weaknesses.
- Tracking of development progress and impact of L&D interventions. After agreeing on priorities to focus on for personal development (and maybe training courses), an agreed competency framework provides a way of both having clearer learning goals & tracking benefits (did competency improve afterwards).
Benefit 2: Designing roles and career paths
Analytics & Data Science leaders are largely agreed that a mix of complementary roles are needed to achieve effective teams. However, it can be challenging to be clear, when communicating with your teams & sponsors, how these roles both differ & work together.
Here again a consistent competency framework can help. Scoring each role against the competency maturities needed, can enable a manager to see how whole team scores or any gaps still left. It can also help in more objectively assessing candidates suitability for different roles within a team (e.g. are they stronger at competencies for ‘back office’ modeller or ‘front of house’ consultant type roles).
If that benefit provides more consistency when considering peer-level opportunities, this tool can also help guide promotion opportunities. It can help you define the different competency maturities needed, for example, by junior analyst verses analyst verses senior analyst verses analytics manager. Such clarity enables more transparent conversations between analysts & their managers (especially when one can compare & contrast an individuals competency score with those needed by different roles).
Seeing how those competency profiles compare at different levels of seniority for different technical roles, can also enable a manager to see options for career development. That is, there are often options for junior members of the team (rather than a simple climb up the functional ‘greasy pole’). Examples might be: development of statistical skills to pursue a career path in the modelling roles; development of data manipulation skills to pursue a career path towards Data Engineer; development of questioning & presentation skills to aim for a business partner role, etc.
Benefit 3: Identifying your team L&D priorities and where to invest
Used together, all the elements mentioned above, can help an Analytics leader identify where the greatest development needs lie (both in terms of severity of gap & number of people impacted).
Comparing the competency profiles for roles needed in team, with current capabilities of role holders, can identify common gaps. Sometimes it is worth investing in those most common gaps (for sufficient numbers, it’s still worth considering external training).
Then you can also compare the potential career paths & potential for development that managers have identified from conversations. Are there competency gaps that are more important because they help move key individuals into being ready for new roles & thus expand the capability or maturity of overall team?
Much of this will be subjective, because we are talking about human beings. But having a common language, through the competency framework tool, can help leaders better understand & compare what they need to consider.
Do you use an Analytics Competency framework?
If you are an Analytics or Data Science or Customer Insight leader, do you currently use a competency framework? Have you seen how it can help you better understand the capabilities of individuals, requirements of roles & how both best fit together in an effective team?
Do you have the means to have meaningful career path conversations with your analysts? Being able to do so can be key to improving your analyst retention, satisfaction & engagement with your business.
I’m sure there is a lot more wisdom on this topic from other leaders out there. So, please share what you have found helpful.