Why We Need a Data Model to Ensure Pay Equity

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A few weeks ago, we had Sun Tui (http://www.inspiredchange.global/team) as a  guest speaker at one of our internal research communities who was there to talk about her work as a neuroscience researcher, therapist and executive coach.  The topic was ‘The Neuroscience of DE&I’.  Equity and Inclusion as an Outcome of Diversity The first thing she did was to say, With respect, Gartner (and the rest of you corporate types) have got this backwards.  Its not as we all say, Diversity, Equity and Inclusion. The proper way to think about it is this that diversity is an outcome of equity and inclusion. Small, but profound difference and the audience was interested and impressed.  Our guest spent the rest of the 55 minutes talking about the neuroscience.  So why is ‘equity + inclusion = diversity’ such a significant shift? Overcoming Our Biases with Data We have known for a long time that ‘hiring for diversity’ doesn’t work.  Differences between people make for less harmonious relationships, UNLESS there is equity and inclusion.  We may bring as many different kinds of people into our organizations as we wish, assuming we can find people with the right qualifications.  Simply stated, in neuroscientific terms, our brains respond to novelty & difference in all kind of ways, good and bad. People who are different or who we simply do not know cause us to pay more attention & be more careful.  Its just normal.  But left to our own devices, without an awareness of how our brains & emotions work, we will naturally gravitate to ‘people like us.’  It isn’t necessarily racist – though it can be – or sexist – though it often is. We make assumptions and judgements and which in turn cause us to behave in certain ways that are not obvious even to ourselves.  This is the root of unconscious bias.  Which is unconscious.  Which means we don’t know its there.  Unconscious bias arises from our limbic systems:  our emotional brains.  Can we train ourselves not to be biased:  sure, but it is by definition going to be a struggle, requiring constant vigilance and monitoring of our own internal states.  Should we try?  Yes.  But overcoming our own unconscious bias is by definition, difficult. We Need a Data Model There is another way. We can use our intellectual capabilities.  We can create processes that override our unconscious biases by specifying that we need to see a diverse slate of candidates for hiring and promotion.  We can use math.  If there are no women on our short lists, we should ask why.  The answer ‘there aren’t any that are qualified’ is usually a mistaken assumption.  To fix differentials between people with similar roles and skills, we should investigate why that might be.  By observing the world in a scientific way, we can create hypotheses and test them, using the observable data. In order to achieve pay equity we must describe people (employees in this case) as data models.  So both job related and non-job related data about people will be recorded:  gender, age, race, education, experience, job role, tenure to name a few.  Once we can see and aggregated data, we can then seek explanations for any anomalies or patterns that we see. As we continue to work on the EPIC framework, we are going to ask for your comments and feedback.  If you are so inclined, please comment on one or all of the following: What do you think of Employee-as-data-model as As a way to understand pay equity ? What variables would you include or not include if you wanted to understand pay differences (Gender, race, etc.)? In many instances we are asking for data we don’t currently collect or have access to.  How would you solve this problem? Do you think that if people see the connection between volunteering data and some benefit to them, would that make a difference?

Source: Gartner Blog Network On:

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