Wait, What did I Miss?
When you follow a process to hire a new position, it may look similar to this. Create and review job description that aligns with the skills and experience needed in the first 90 days of work, check. Know which skills to inspect or train in the first 30 days, check. Avoid interviewing candidates until the job is officially posted, absolutely. Create and review templates to ensure consistent methods of scoring and assessing applicants, check. Remind myself of personal biases, okay. Set an objective to be equity minded throughout the process and diversify the candidate pool. Yes. Okay… so what did I miss? More data.
Data is a very important part of the hiring process that seeks to discover more matches. Without complete data and information, it would be difficult for an organization to prove that they have a fair, unbiased, and equitable hiring process. Sure, favorable information can be shared or displayed in rankings or pictures; however, misinformation can be shared the same way. Data is more helpful when it is complete, accurate, and disaggregated. Read more on this shared learning below.
What is data?
Data is “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation” [1] Whether alone or with a collection, data doesn’t mean much without context. Take for example the numbers 0, 93, 14, 8, 6, and 1. Are these numbers good, bad, or ugly distractions from numbers that may be more important?
What is information?
To transition from data to information, there needs to be a transition from the unknown to the explained. This transition happen through context. When we add context to the data shared above, we gain general hiring funnel information.
*Zero candidates were interviewed before the role posted
*93 candidates applied for the role
*14 applicants were invited to a skills assessment and interview. The pool was intentionally diversified
*8 candidates resume evaluation score met expectations
*6 candidates resume evaluation score was below expectations
*1 candidate was hired, trained, and collaborated with the team
These numbers are valid, but are they good? Sure, there was good work put in to manually review 90+ resumes and help discover more matches based on the tasks that needed completion. However, when I take a moment to filter through my conviction bias, I know there were opportunities to improve.
What is mis-information?
Misinformation is “incorrect or misleading information.”[6] With misinformation, the data and facts may be true; however, the information is inaccurate because it lacks context that transforms the facts and data.
Information needs to be both complete and accurate. It is great to have accurate data; however, when it is not complete, there may still be room to misinform or be misinformed. Using the hiring funnel as an example, let’s unpack how misinformation can happen when data is incomplete.
It was a fact that the slate of candidates invited to advance was diverse based on skills and experience. There was the intention to diversify the pool by gender as well; however, it is also a fact that some names, including mine, are gender neutral. I assumed that one candidate was a specific gender based on their name, and I advanced them because they qualified and diversified the candidate pool. Needless to say, I was incorrect.
In this example, I missed the collection of gender, race, and ethnicity data which would have made applications more complete. Current requirements for small and medium businesses do not require a high level of disaggregated data: however, the current trends in hiring practices points toward more complete data as a requirement. In addition, transparency, awareness, and accountability within recruiting and hiring practices are also trending toward becoming requirements for companies of all sizes.
Public companies must collect an applicant’s gender, race, and ethnicity data in an EEO-1 Report. It is becoming more common for C-suite leaders to share their DEI data, information, and goals publicly. Tim Ryan, PwC’s US chairman, says, “I’m happy to say that, EEO-1 or otherwise, nearly every CEO I speak to now is thinking about their diversity data and how to improve it”.[5]
Leap Here, still considered a startup, will collect gender, race, and ethnicity data on all roles contracted, hired, and recruited going forward. It is highly recommend that small and medium businesses do the same.
Build with Experience, Knowledge, and New Information
Data can tell us -9.8 m/s2 is a speed, information tells us it’s the speed of gravity, and we may assume that gravity always has a downward pull. To add additional context we can leverage the knowledge that gravity is the attraction between two objects. Observation tells us that gravity doesn’t pull everything down. Sometimes… gravity causes things to rise. Consider the tide and the gravitational pull of the moon.
Good application data and good information can help one more decision maker take one more step in confidence. Which can lead to another step in solidifying a better foundation for the future of …more people rising into gainful employment.
Next time gravity attracts you to your desktop or laptop, and you’re accountable for making a good hiring decision, consider raising a few questions or thoughts that could improve performance.
For example:
1 – What information can I stand on to take one more step toward a solution we need? Dependable data and information can help influence action. Leverage complete and accurate disaggregated data to amplify or influence specific decisions that need to be made.
2 – Should I rush decisions based on the data available or should we discuss what seems incorrect “in the moment”? Discover what bounded rationality is. Begin to keep track of the number of decisions made based on a limited perspective or limited information. Think about who is impacted by the decision.
3 – How accurate is the data, really? Inspect what you expect to build confidence and increase impact. When possible, seek to be more aware and move corrections forward.
Resources
[1] Definition of Data
[2] When Does Data Become Information?
[3] The Difference Between Data and Information
[7] Deception and How Fast it Spreads. Who Should Stop It?
