The Whats and Hows of Data audit — People Components
Expectation Setting:
I hadn’t realized how complex the assessment of this component is because it might include different aspects such as competencies (skills), competency, culture, capabilities, and more.
Then there’s the people component, which is usually so intermingled with the process component that they often get reviewed together. In this article, I’m focusing on the approach to review individual skills — not the dynamics of work groups or broader workforce practices.
Why a Data Audit?
Because today’s topic is how to access the people component in the PPT model, as described in the article “The Whats and Hows of Data Audit — Technology Components.”,
let’s revisit why you might be interested in conducting a data audit using the CCSS matrix, a matrix used to access knowledge acquisition:
- Unconscious Incompetence: You don’t know what you don’t know.
- Conscious Incompetence: You know that you don’t know.
- Conscious Competence: You know how to do it.
- Unconscious Competence: You do it so effortlessly that you no longer have to think about it.
So, you might know that you have issues with your data infrastructure but can’t quite pinpoint them (conscious incompetence) or you might not even be aware of gaps or inefficiencies in your data operations and infrastructure (unconscious incompetence). That’s where a data audit comes into the picture to reveal those hidden deficiencies in both your technical infrastructure and your team’s data skills that would otherwise remain unnoticed.
What to Assess?
Like I already commented, there are overlapping concepts like capabilities, competencies, and skills, and there isn’t broad agreement on these definitions, but generally speaking, capabilities answer the question, “What can you do?”; capacities answer the question, “How much can you and your team handle?” Finally, competencies — a focus of this article — answer, “How well can you perform a task?” Competencies are the concrete, job-specific, and measurable skills and attributes that can be easily measured.
Katz & Kahn (1986) grouped competencies into:
- Technical/Functional Competencies or knowledge, attitudes, and skills associated with the technology or functional expertise required to perform the role.
- Managerial Competencies or knowledge, attitudes, and skills required to plan, organize, mobilize, and utilize various resources.
- Human Competencies or knowledge, attitudes, and skills required to motivate, utilize, and develop human resources.
- Conceptual Competencies or abilities to think at an abstract level.
Over time, slightly different definitions have emerged that agrregate the above-mentioned levels — for example, leadership/managerial competencies (needed to perform managerial work and processes), core competencies (behavioral expectations aligned with the organization’s mission and values), and technical competencies (the specific skills needed to perform tasks effectively).
Now, let’s add another layer of complexity — skills development.
Bloom’s Taxonomy, published in 1956, outlines that skills development progresses from simple recall to more complex processes (knowledge, comprehension, application, synthesis, and evaluation). Revised versions that appear much later, rename these levels to Remember, Understand, Apply, Analyze, Evaluate, and Create.
By combining Katz & Kahn’s framework (types of competencies) with Bloom’s Taxonomy (levels of competencies), we can assess existing data-related skills and skills development of data people.
How to Assess?
Now as there is a framework, how to put it practice. One way to do so is to frame each definition into a set of questions. For example, the definition of Technical skills on the awareness stage is the ability to ‘Remember, retrieve, recognize basic technical terms, fundamental tools and standard procedures.’ Hence, you could transform this definition into:
- I can easily recall basic technical terminology and recognize the main tools required in my field.
- I am familiar with the standard procedures used in data processing and analysis.
to rate each statement on a scale, for example, using a Likert scale:
5: Strongly Agree
4: Agree
3: Neutral
2: Disagree
1: Strongly Disagree
and calculate the average score of each competency level and competency type to get a quantitive measure of where team members currently stand within that level.
The people part was the most difficult to put together. My feeling is that it will be revised several times until there is something practical that can be easily applied to access the org state. But for now, we are off to the Processes component in the PPT framework.
If you think we could work on assessing the state of your organization, feel free to ping me at https://www.linkedin.com/in/eponkratova/
