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Why and How to Hire for Potential

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Datavant
October 10, 2022

An approach to finding “10Xers” with the will and the skill to grow

Photo by Alena Koval, courtesy of Pexels

Recently, Chris Abbass invited me to join him on Hiring on All Cylinders podcast where I had the opportunity to discuss my own journey into the People space, the opportunities I see today, and how we’ve scaled culture through a period of disruption and uncertainty. Chris’ insightful questions prompted me to expand this conversation into a 3-part series for the blog. Below is part 2 of 3. Part 1 is here.

You can listen to our full conversation. Also available on Spotify and Apple Podcasts.

Why hiring for high-potential is a differentiator

I compare it to the Compounding Effect in finance. I’ve written about this since 2018.

I think personal growth is very similar to compound interest: it tends to be exponential. A lot of people in the first half of their career journey have the capacity to increase their personal skills at a rate of 2 or 3 times a year.

Here’s an example: If you take someone who’s been in the workforce for three or four years, give them another year, along with the right environment and coaching, they could become twice as impactful or capable.

But most organizations in tech, in professional services, and the investment community don’t have easy paths for people in the first half of their career to accomplish 2X and 3X scaling per year in their capabilities. It’s more like 20%, or 50%, if you’re lucky to be in a learning-rich environment.

When you look at the difference between someone growing at 50% expansion of skills year-over-year or 2X expansion of skills year-over-year, and you think of it compounding like interest on investments, after a few years you get people with really divergent levels of impact, performance, and capability.

We’ve always had a view, especially as an early-stage company, that we’re not sure what the future holds (in terms of the unique opportunities and puzzles we’ll be solving). The problems we’re solving today and the problems we will be solving in 18 months might be really different from one another, so why don’t we bring in people who have the ability of 2, 3, or 5Xing their capabilities year-over-year?

We call that “potential” — that will and skill to grow.

Even now, as a scaled organization (with ~1200 salaried staff and 8,000+ hourly staff), the premise is still true: we’re building something that’s never been done before — connecting the world’s health data to improve patient outcomes, which today requires cutting edge technology innovation and human-powered operational excellence — which means all across the org, the challenges of today and the challenges of tomorrow will be different.

So, investing to hire high-growth, high-potential talent helps us have optionality and a range of skills for the future.

What it looks like to evaluate and hire for potential

Aspiring to attract and hire high-potential employees is relatively easy.

The “how” is tricky.

We do a lot of interviewer shadowing and training in the company today, standard tools like structured, criteria-based interviewing and question banks.

I’ve probably conducted 1000+ interviews (I love it), and a few of the traits I focus on that are predictive of success here are:

  • Learning & curiosity
  • Resilience & grit

First, I think learning and curiosity are really critical dimensions. In an interview, I’ll ask questions like, “Tell me something you’ve learned in the last few years.” It could be personal or professional. It could be, “I picked up woodworking during COVID,” or, “I learned a new coding language in my last job.” But the “how” is so much more important than the “what.”

I push a lot on how people learn. I look for signals like:

  • Are you a hands-on learner?
  • Do you want to read a lot of books or blogs?
  • Do you want to go to events and learn from practitioners?
  • Do you want to hit every obstacle you encounter and then ask experts for help?

I believe a calibrated interviewer can tell in an interview how innately curious someone is by spending a lot of time on the candidate’s questions — spend more than 5 minutes of a 30 minute interview on this.

Assess the questions they bring to you. One tactic I use is simply to flip the interview and start with the candidate’s questions first. It can throw some people off, which is not the goal, but the super curious folks will have a list of questions and show excitement instantly. I’ve even seen candidates say, “Oh wow, I’ve never started with questions, this is awesome…”. That’s a great sign.

Second, I also like to push on resilience and grit. “Tell me about a time that you struggled.” Or, “When have you gotten really hard feedback and bounced back from it?”, questions that put the candidate in a place of, “This thing I did was really trying. How did I move through it? Did I react with a teachable spirit and mine the experience for lessons learned, and get better, or did I get beaten down, burned out, and disappointed?”

One of my favorite articles on interview questions is from First Round Review.

There is no best or worst way to learn, though understanding the ‘dark shadow’ of each style is helpful

While no one of the learning styles above is better or worse, I do think there are certain styles that fit better with different managers and different organizations at different moments in time.

If you, as a learner, say in an interview “I want to do it myself,” (I see this a lot with early career software engineers, for example), I will go deep with followup questions, such as:

“Here’s a scenario: you’ve gotten a challenge from your manager. You’re not really sure how to solve it and you don’t know where to start. What do you do? How long do you work alone on your own? When do you start to post in the team Slack channel for guidance? When do you ask your manager for help?”

A lot of different learning models can work, but if someone says, “I don’t want to bother my team and managers, I want to do a lot of reading. I’m going to take an academic approach to it. I’m going to test and fail.” I will ask, “When do you know that you’re wasting your time?” Building a business is a team sport. It’s not individuals doing problem sets in college where you’re not supposed to share.

On the flip side, if someone says, “I learn best being an apprentice. I want to watch someone do it,” that can work fine too in a lot of different forums. If you do pair programming as an engineering team, or you have a sales model where junior sales people supporting pitches and witnessing a more tenured person working. But then as an interviewer, I want to find out if they work individually enough before they ask for help. If they like to be in an apprentice model and we don’t have a formalized mentorship or shadow program, will the candidate have enough capability and will to do some research on their own? You want people to work independently and autonomously before they tap on their colleagues or their manager for help.

So, there is not a perfect or wrong answer to the question of how a candidate likes to learn best. Yet, I like to vet the worst case scenario or the dark shadow for each type of learning style to make sure that someone’s not going to spend a week alone working on a coding problem when half a day would have been fine, and then they could have asked for help on Monday afternoon instead of waiting until Friday afternoon.

Hiring for potential is only the first step — intentional investment in cultural infrastructure is needed to nourish the potential

There’s no easy answer or playbook here. Three areas I recommend:

  1. Intentionality — don’t wing it, be thoughtful
  2. Investment — it takes time, people, and money to do this — it won’t happen organically
  3. Clarity — know what you are as a company and what you aren’t, what you will do and won’t do. Be explicit. Write things down. Train on it.

You have to keep the right balance of experienced and not-yet-experienced folks with high potential.

First, you have to understand and have a perspective on which types of roles benefit from potential as a primary capability, and which benefit from specialization or expertise. It’s not one-size-fits-all.

For the typical company of a hundred people — for example, tech companies and startups at series A, B, or C fundraising — conventional wisdom might say only a handful of roles should be filled with high potential generalists.

I see it differently. Many more roles than conventional wisdom would suggest can be high-impact if they’re hired for potential.

For this to work, a company needs to have intentional focus and resources. Here are some of the ingredients we’ve found helpful at Datavant over the last 5 years:

  • An emphasis on coaching and feedback (e.g., part of your cultural values or leadership principles) as well as innovation and experimentation (can’t be afraid to fail)
  • Clear expectations for managers (leaders of teams) to include (a) giving written feedback X times a year and (b) doing one-on-ones on at X frequency, which should include feedback or coaching
  • Providing training (from in-house People team, or external trainers like LifeLabs etc) on how to do 1:1s, how to coach, etc

This can range from informal to highly structured. If you don’t have these in place today for a team of 100, you can start a MVP (minimum viable product) approach with 1–2 person-days of effort — it doesn’t have to be a major lift to start.

In part 1, I talked about a few of our principle-based guiding documents. One of them was, “What are the expectations of a leader of this company?”

When we had only ~10 people-managers, we wrote a document. It had four categories:

  • strategy / setting a vision
  • communication
  • managing execution
  • growing & enabling the team

There were tenets that said, “We believe in feedback and coaching as a critical ingredient to this environment.” If you have high-potential team members that you bring in, but you don’t pair that with an environment that facilitates learning, an environment that is okay with making mistakes, you’ll never have people unleash that potential.

We tried to be clear that it’s an expectation we have for every leader, to invest in coaching and to make it okay to make mistakes.

In the second year of the company, a new junior engineer checked-in code that broke Production. Our response? That engineer and a few others fixed it quickly. We tell the story and we joke about it today. Everyone knew who it was, and we tried to make that lore and celebrate it. That new hire engineer didn’t get in trouble for breaking the Production environment. There was no penalty or discipline for it. It was a great hands-on learning moment to fix it. And ultimately we tell stories like that because it shows you do work that matters.

We take off the guardrails. We trust you. And we hire people who seek and thrive with this level of autonomy and judgement.

People will make mistakes and will recover from them. So by setting that norm of, “experimentation and mistake-making is okay as long as we learn from it in appropriate ways,” and then setting expectations for managers and leaders to invest in coaching and feedback, we hope to use those things as fertilizer for the potential so it can really grow and be unlocked.

Many thanks to Chris Abbass and the Hiring on All Cylinders for the opportunity to have this conversation. You can listen to our full conversation here and on Spotify and Apple Podcasts.

Considering joining the team? Check out our careers page and see us listed on the 2022 Forbes top startup employers in America. We’re currently hiring remotely across teams and would love to speak with any new potential Datavanters who are nice, smart, get things done, and want to build the future tools for securely connecting health data and improving patient outcomes.

Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification

AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).

AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.

"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ. 

“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."

Building Trust in Privacy-Preserving Data Ecosystems

As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.

As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health. 

The Power of SDOH Data with Providers and Payers to Close Gaps in Care

Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.

Payers Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

  • Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
  • Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
  • Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:

  • Social isolation
  • Loneliness
  • Transportation barriers
  • Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:

  • Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
  • Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
  • Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.

While accessing SDOH data offers significant advantages, challenges can arise from:

  • Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
  • Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
  • Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.

SDOH data holds immense potential in transforming healthcare and addressing health disparities. 

With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.

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