I wrote about how having a very big vision alone is not sufficient, and we need to break that into smaller chunks of actionable projects.  A very big challenge can often be broken into simpler chunk of questions.  Of course, designing these questions correctly is tricky. For example, let us take the problem statement “Does communication about the importance of diversity really help in Diversity hiring?”.  If someone challenges the efficacy of this, in an enterprise, what will we do?  This is where the concept of Micro-Experiments will be useful. Micro experiments break down the problem into manageable chunks of questions. The first guiding principle in such a question is what sort of data sets will help you in proving or disproving this hypothesis? If there was a definite date when we decide to communicate the importance of Diversity, then we can do a before and after analysis. But the challenge is multiple variables would have changed in an enterprise. When I was reviewing the literature, I found the dissertation work done by Jeffrey A.Flory et al. very useful in this regard. The researchers did something very unique. They designed a control and treatment group on the career website visitors applying for jobs.

The control group did not receive any message about the importance of Diversity, but the treatment group received messages about the importance of Diversity on their career application pages. Their analysis suggests that signals valuing workplace diversity drove up applicant pool interest (statistically significant).

Let us look at this graph below. Promoting diversity (different messages of diversity) across the treatment groups always drove up the applicant rates.

From work done by Flory, Jeffrey A et al. – Increasing Workplace Diversity – University of Chicago

A long drawn data experiment got reduced to a micro digital experiment and analysis done in a reasonable time. Now with the digital assets at our disposal, we can run brilliant experiments. We just have to be very creative. Many times such experiments are not expensive. HR should only have to think about questions and many ways the technologies to implement this are less expensive these days

One of the CHROs asked me a very deep philosophical question last week. We are making significant investment in Learning and development. But how do we know that we are building a very successful learning environment? This question sort of haunted me all week. I was looking for some answers to look at the type of research conducted by practitioners. The British Journal of Education Technology had an interesting research conducted by Researchers Teresa Schafer et.al.  The researchers ran some experiments across different settings. The study almost narrowed down that the level of interaction among learners is a critical factor in enabling learning.  This level of interaction was measured in terms of comments by the learners. But that alone is not the beauty of this research.  The researches appointed moderators who can post comments – not only on the subject areas but also on the emotions of learning a new course (so there is a technical coach and an experience coach).  There is another aspect they built-in. The experience coach also encouraged people to post comments about their concerns, which triggered comments like “In this job, I am noticing massive changes in the way things are done). So addressing both the technical aspects and the experience aspects enabled in significant collaboration, and the researchers documented meaningful participation among targeted learners.

The role of moderators is not well understood by many learning organizations. That is why this study can be very useful for you.

Another interesting research we conducted sort of made us think, why Recruitment (especially towards complex roles) is very complex. 

Platforms such as LinkedIn have provide Recruiters great service but the talent dynamics have gotten very complex in the last few years.  Key word search and Boolean searches are not effective.

We conducted a study last week on Principal Engineer. A principal engineer is a very advanced software engineer role and they often build very complex systems.

In such roles, the recruiter almost need a matrix like this. Understanding the domains across a principal engineer operates is very crucial. Principal Engineer operates across Search, Robotics, Security, Hardware, Embedded Systems, Network and other similar areas.  This is where standard recruitment platforms will fail.

For example, if you search using the standard Boolean, only very few profiles will show up.

Here is a table that may be useful for you.  (We can build similar cheat sheets for you based on your requests)

From Draup Research – 2020-July