Key decisions for implementing skill management
Brent Ross is Customer Success Manager at Ibbaka. See his skill profile here.
Introducing skill management is a priority for many organizations in 2022. What are the key decisions that need to be made? How will you make them?
This post provides a high-level guide to this topic. In it, we help you begin to navigate the introduction of skills as an independent data set in your organization. Everyone in your organization should have a role in maintaining and improving their own skills and the skills of the organization.
Please take our survey on Critical Skills for 2022
Skill Leadership
The need for skill management surfaces in different parts of an organization, for different reasons. These can include a need to …
Quickly search for and locate expertise
Understand skill gaps and coverage for a particular role, discipline or even the entire organization
Make skills searchable for the purpose of forming teams
Define the critical skills needed for success in various roles
Support the professional development planning process
…and there are others.
Skill management solutions, like Ibbaka Talent, can support organizations and their people in multiple ways. The key to understanding which stakeholders are needed to champion the initiative depends primarily on three factors:
The main goal you are trying to achieve
Which processes are impacted
How skill and assessment data will be used
We have found that implementing any dimension of skill management is most successful when the following people are involved:
Stakeholders who will use data to make decisions
People who will be asked to help define skills for domains of expertise
Experts in skill management and development
Goal Alignment & Building Consensus for Using Skill Data
There is plenty of content on building alignment for initiatives that require new technology. Most of this falls under the heading of digital transformation. However there’s one key trap that many organizations fall into when considering technology as an answer to a problem they face - they start writing requirements based on their current understanding of the technology that’s been available to them, or the current solution that is (eventually) going to be retired. This leads to false constraints and poor decision making.
Rather than start at the system level (the lowest level in Roger Martin’s strategic choice cascade), it’s important for organizations to build consensus around what the technology needs to be able to achieve, rather than trying to define how technology might support that goal.
With regard to skill management, we’ve already outlined some of those goals above. A more concrete example of a goal might be, “I need to be able to find people based on a skill I’m looking for, and then understand what other skills and expertise those candidates might bring to our project, so that I can assemble a diverse team and meet challenges we may not be anticipating.”
There’s a more formal format, based on Behavior Driven Development, that we use for use this at Ibbaka. It goes like this:
As a ________ (persona)
In _________ (a company type, business unit, team)
I want to __________ (action you want to take)
So that I can _________ (outcome to be achieved)
If you were to only focus on what the new technology needs to accomplish based on what the old tech could or couldn’t do, it becomes much harder to align people around a common goal. You will end up with an impossibly long list of software requirements that will be costly to fulfill.
Managing Skill Data
The introduction of skill data to your organization can either be transformative or a kind of meta-data footnote to the employee profile, depending on how you handle it. When employees are motivated to keep skill data up to date because it’s being actively used to identify people for projects or to understand their opportunities in the organization, this data becomes a powerful tool to help decision makers understand where to make investments in learning and hiring.
There are things to consider as you think about how to manage skill data:
Skill names and their definitions
How skills are attributed to jobs or roles
Self-assessment data
Peer assessment data
Let’s look at each of these in turn.
Who should create and define skills?
Some skill management platforms require that only a select few people can create and define skills within the organization to ensure consistency. While consistency in identifying and defining skills that are important to your organization should be a priority, we don’t believe a pure top down approach to this makes sense.
Defining skills exclusively from the top down can be costly and too slow to keep pace with the rate of change on the ground where people are doing the work. Moreover, the mechanisms for facilitating this approach to skill identification simply can’t be agile (surveys, annual updates to job definitions, etc)
Employees throughout your organization, especially subject matter experts of all stripes will have a better sense of which skills are needed for their jobs well, and or those skills that are emerging as important for them to produce results.
Combining Bottom-up and Top-down Approaches to Skill Management
Ibbaka Talent takes a top down and a bottom up approach to skill management. We combine these so individuals can identify skills for their profile regardless of whether or not that skill has been identified by the organization as something important for people to have in any given role.
This approach can dramatically reduce time to value, because it allows you to crowdsource your understanding of the skills that are important to people on the ground. It also allows the organization to maintain a dynamic and evolving view to skills throughout the organization as these change over time. This makes room for future flexibility to use the and describing roles.
If your organization wants to understand skill gaps and skill coverage, there does need to be a top down effort right off the bat - which involves defining a basic competency model that articulates which skills are most relevant to the key roles, competencies, or behaviours for which you want to measure coverage. Subject matter experts and operational leaders should be in the driver’s seat to understand skills in the context of the myriad roles people play in your organization. But this shouldn’t prevent employees from playing a role as well.
In Q1 of 2022, Ibbaka Talent will support building competency models and role descriptions from the ground up - using skill profiles of your people as a starting point.
How to address the consistency challenge in identifying skills?
Ibbaka curates skills for its customers as a service, which includes eliminating duplicates or skills that appear to be the same but may have been named differently (E.g., Excel and MS Excel). In Q1 of 2022, it will become possible in Ibbaka Talent for organization managers to appoint anyone as a skill curator - someone who can create skill definitions that are specific to your organization, as well as assign skill categories. Organizations can also appoint editors for specific competency models, which also confers skill definition privileges for any skill in a model.
To sum up the Ibbaka approach to skill curation:
Individuals control their skill profiles. This can help improve the organization’s skill profile over time.
Distributing the ability to identify and define skills to the people who understand those skills reduces time to value.
Build competency models to build an understanding of skill coverage, and to help people understand which skills they need to have to to develop for particular roles
Creating and Using Skill Assessment Data
Ibbaka Talent collects several types of assessment data - self-assessment data, peer assessment data, manager assessment data and expert assessment data. All of these types of data are collected on a five point scale.
Self-assessment data is:
Routinely collected from the individual whenever someone adds a skill to their profile
Displayed whenever aggregated peer assessment data is not available
At the discretion of the user to share or not, based on a profile setting
Peer-assessment data is:
Collected on a skill by skill basis
Collected at the discretion of the end user, but can also be facilitated by a manager as well if the organization so chooses
Displayed as a single rating once three or more peer assessments have been offered
Aggregated with the help of an AI that measures confidence level in the assessment, based in part on the likelihood the person giving the feedback has the skill and is also proficient enough to provide an assessment
How your organization chooses to use these types of data is entirely up to you. However, because it can take time to establish a rhythm of collecting such data (especially if the organization is not used to collecting peer assessment data), there are a few things we recommend, depending on how much skill assessment is already a part of your culture.
Self-assessment data is not always accurate, some people underestimate their expertise, others are prone to exaggerate.
Peer assessment data collected is much more reliable than self-assessment data, because our AI is doing all kinds of calculations in the background to understand how likely it is that someone has the skill at the level proficiency indicated (this is true for the person being assessed and for the person giving the assessment).
When used in conjunction with self-assessment data, peer assessment data can be a powerful tool for helping the individual to reflect on their skills.We recommend using self-assessment data and peer assessment data as a reflective tool for individuals and managers to identify gaps in self-perception, confidence, and as a way of directing professional development. Self-assessment data and SkilRank are combined for the purposes of skill-based searches, with SkillRank being the data point we rely on, if it’s available.
Introducing skill management to employees
Our guidance here is fairly straightforward - we believe in executing a phased approach where customers start small and build fast, rather than trying to orchestrate a ‘big bang’ roll out where everyone jumps in at once. This is because introducing skill data (and the platform to manage it) is as much a cultural adjustment as it is a technological one.
Here are some different ways you can think about where to start. Choose a business unit or function where one or more of the following are true:
There is a strong motivation to leverage skill data to understand available expertise and/or skill coverage
Employees are hungry to understand the skills that will make them successful in their careers
There is urgency to keep up with a changing, technical landscape of skills that needs to be understood
The stakeholders are ‘friendly’ to change, and there is a high willingness to try new approaches to things
To talk with us about introducing skill management to your organization, please contact us at info@ibbaka.com.
Posts on Skill Management
Key decisions for implementing skill management (this post)
