Paul Davidson and Rohan Seth started Clubhouse, an audio-only social network where people can listen to live chats and discuss various topics.  Starting in 2020, the company became a unicorn very soon with a billion-dollar-plus valuation.  In the book SuperFounders by Ali Tamaseb, the author states that they have nine failed startups between Seth and Davidson.  Learning from failures and refining the strategy is becoming a very critical soft competency.  A while back, Eric Schmidt – Former CEO of Google, stated, “Give me 10 or 20 Google-like Engineers, and I will increase the revenue, reach, profitability and scale in any business ”.  Is Eric Schmidt right?  It is not about being right or wrong, but it is becoming more apparent that companies need to move towards an AI Savvy talent.  (Not just engineers but across all functions).  So you want to build an enterprise that understands customers, data flows, systems (technology) and develops common patterns to predict (ML models).  The critical thing to understand is that the entire organization needs to work for it.  One of the reasons Netflix ML models are compelling is that they study customer interactions data intensely, and both software and non-software talent are looking into it.

We have emphasized this throughout 2021.  With the retention of existing talent becoming an issue globally, talent acquisition at a rapid pace needs to happen.  One idea we have developed at Draup is to break down this complex problem into meaningful channels and see how we can make incremental victories.  How can we organize efforts in a way it can be concentrated and specialized.  The following are our initial thoughts, and we realize you may be thinking along the same lines in some components.

What if we plan Talent Acquisition across these channels in a very targeted way?

When Facebook (Now Meta) lost its Chief Product Officer, Chris Cox, in March 2019, the product roadmap took a serious hit.  Facebook tried several strategies but went back to Chris Cox and resolved the differences and brought him back. This is an example of senior talent, but sometimes the value of Boomerang talent is exponential.  A 100 member software team loses about 10 or 15 people or more in a year.  Over five years, that is 70+ resources.  Where are they going, and what will it take to bring them back?  Detailed analysis on the causes of why they left and what has changed in the meantime could be a beneficial strategy for talent acquisition

Techniques and Tools:  Root Cause Analysis, People Network Analytics, and Draup Detailed Profiles

We wrote about this last week extensively.  Recruiters should be building a database of smaller universities and community colleges, and the more prominent universities.  New ways of accessing talent through emerging social media like Clubhouse need to be understood (by joining the relevant channels of discussions).  It is also equally important to understand global early career initiatives.  Here is a sample list for your review

• Miami Dade College (MDC) partners with SoftBank and Correlation One to expand access to data science training and in-demand careers such as cybersecurity, cloud computing, and more
• Microsoft has partnered with JA Asia Pacific and CloudSwyft to launch a regional skilling program focusing on students and early career professionals looking for opportunities as data analysts, data scientists, development operations, software developers, and IT support specialists

Draup analysis shows over 75% of the startups fail to gain the right momentum.  This is a good potential target for companies. We have launched an app called Startup Scanner (still evolving), but we track over 100K startups here.  In the London area alone, there are 1000 startups with limited momentum.  Here is a snapshot of startups with less momentum and good analytics, AI, and Big Data skills. Each region has a share of these startups.  Designing a proper messaging strategy and database of such companies will be useful

 As Digital and Auto ML emerge, several skills become easier to learn.  We believe the following skills can be easy to learn for Returning Mothers, Veterans, and other talent segments.  Historically we have had programs to target such talent segments from a broader initiative.  But we have to change the paradigm and be very specific on the skills to use the talent. 

• Citizen Developer
• Digital Marketing
• Cloud Support Professionals
• Data Annotation Analyst (Supporting training data for ML)
• Cybersecurity Monitors
• Data Center Operations

Several such roles and skills are emerging across functions.

 In all large labor economies, there is a refresh of jobs worldwide.  In the US alone, estimates show that about 18million plus people have quit their jobs beginning April 2021.  But how can we plan and target talent differently? Here are some of our thoughts

• Can we hire quitting Flight Attendant talent to develop personality development?  After all, who better to handle challenging situations around business
• Can we hire quitting front-line retail talent for digital contact center work?  The skills required are the same – we need to add digital literacy
• Can people quitting Truck driver work be hired for Supply Chain monitoring roles?
• Can we bring Nurses who are tired and grieving for managing employee advocacy aspects within health plan management in large companies
• Many Software Developers are quitting for more meaningful purposes – can we bring them and make them digital leaders for purposeful initiative?
  • A US Bay Area software developer in Consumer Software may be willing to join a Healthcare Software company as it involves curing diseases

 Should we relook at all the jobs and see if there are opportunities to hire differently?  Should Call Center and Case and Ops Work be separated (historically, they have been together).  Rather than finding an Actuary with a data science background, should we hire one data scientist who works with a team of Actuaries?  Should we have a Robotics COE that works with multiple warehouses instead of scaling talent across all locations?  A while back, Starbucks did something like this in a difficult to scale Job Role.  Starbucks had a job role where an Engineer would travel to a store and upload the recipes post operating hours in each store.  This is a cumbersome process where huge IP is transported to each store to the machines.  Also, they needed traveling engineers as loading the recipes and testing is not that straightforward.  They brought all the machines into a cloud, and as a result, a simple push of the button delivers.  Companies have made a significant investment in the cloud.  But have they redesigned the jobs to seek such efficiencies?  This aspect is a Gold mine area for companies