the elusive data scientist

#ShiftHappens: Why We Can No Longer Rely on Developing Skills for Healthcare Analytics

Greg Nelson Our Thots

Developing Healthcare Workforce Competencies

Recently, we presented at a healthcare analytics conference where we shared our perspective on “the elusive data scientist” – an overused, often misunderstood moniker used to describe those that have the technical, quantitative, methodological, industry domain knowledge and the soft-skills required to spin data into change.

Our perspective is perhaps a bit different from most in that we strongly believe that it won’t be the resume-building, certification seeking technocrats that fundamentally change the face of healthcare, but those that are competent to effect change – this will require more than skills.

We note a significant shift in the core competencies organizations is looking for in healthcare analytics. There is a movement away from the traditional mindset of “skills” building where tools and technology knowledge took center stage. This is being supplanted by the new “data mindset” where organizations are looking for those with modern competencies that include design thinking, innovation, analytical product management, storytelling and understanding of health policy, EHR workflows and the application of ethics.

Shift #1: Healthcare Analytics Competencies Trump Skills

Often, the “data scientist” is seen as the warrior – the lone Spartan – who carves his way through data to conquer his would-be foes. Again, we think about this differently. The data champion is a participant in a robust analytic process that helps build analytical capability for the organization. As you can see in the figure below, our view of the analytics lifecycle is broad and requires skills that go well beyond the capabilities of a single warrior.

ThotWave's perspective on the healthcare analytics lifecycle

ThotWave’s perspective on the analytics lifecycle

In this lifecycle, there is a myriad of competencies that includes the technical skills but puts them to work to solve real-world problems. For example, the behavioral view of these often include:

  • Data wrangling
  • Data story-telling
  • Framing questions
  • Dynamic problem solving
  • Communication and results explanation
  • Project Prioritization
  • Navigating the business context
  • Data journalism
  • Choosing the appropriate quantitative methods
  • Dealing with missing data
  • Presenting data results
  • Determine what’s important in a dataset

In the context of healthcare analytics, we differentiate competencies from skills in the following way:  Competencies include the totality of a person’s ability to masterfully execute their role. They are comprised of the right mindset, the right skill-set and supported by skills in toolsets that enable people to successfully perform their work.

Competencies refer to a person’s ability to do something successfully or efficiently.

Whereas, skills are specific learned activities that are necessary but not sufficient to perform a role.

Skills support someone’s ability to do something well.

We offer the following to describe what we mean by analytics competency:

Analytics competency relates to the knowledge, skills, abilities and disposition required to successfully turn data into actionable interventions.

Shift #2: Developing Healthcare Competencies

This shift in desired competencies is being reflected in the growing talent gap across all industries. In fact, within healthcare, HIMSS recently reported large disparities between the priorities of healthcare providers and the consultants, vendors, and innovators who serve them. In the 2017 HIMSS Leadership and Workforce Survey, they found that while there was a lot of lip-service being paid to value-based care (including population health), connected health, precision medicine, and interoperability (all key applications for analytics), many providers are “still stuck on the basics of EHR optimization, data governance, and securing the staff they need to extract and analyze their stores of big data.”

Burning Glass in collaboration with IBM (in a soon-to-be-published report) finds that the fastest growing job in the US job market can be found in healthcare: the clinical health analyst. Surprisingly, this role is growing faster than the “data scientist, data engineers, and Chief Data Officers. (Steve Miller, IBM personal communication.)

Shift #3: Moving From Training to Talent Development

This reality is changing the way many organizations nurture talent. When most people think of training, they think of an off-site location with a computer for every two people or a freezing cold hotel conference facility with 200 people telling them what they need to know. We must think differently about developing people – what we refer to as analytic talent development.

It doesn’t have to be a one-time educational seminar, technical training workshop or lecture. If we seriously want to get better at analytical capabilities within our organization, we must invest in selecting the right people, organizing our teams for an effective and efficient analytics lifecycle that is customer focused and integrate agile project methods with talent development and performance management built into the assignment and assessment of outcomes.

Instead of chasing the high-priced data scientist, organizations are increasingly investing in modern approaches to talent development and adopting novel strategies. These include several approaches pioneered in our Healthcare Analytics Academy:

  • Prescriptive Microlearning – where students learn in small bite-sized chunks every day as part of their everyday routine
  • Analytics Master Classes – where an organization’s cohort of analytics professionals can learn together and apply their learning to specific work products as a team
  • Design thinking inspired hackathons and innovation challenges – that create healthy competition to not only solve analytic challenges within the organization but support the development of staff, infuse creativity through productive problem solving and inspire solutions.

While we still see organizations take advantage of traditional methods such as instructor-led workshops and eLearning for some content, we take the movement to ongoing, enriched learning as a sign of the times.

ThotWave’s Mission is to Mentor the New Normal in Healthcare Analytics

At ThotWave, our mission is to help organizations improve their analytics maturity. We do that by aligning leadership, culture, talent, technology, processes and data to support the realization of their analytical aspirations. Our perspective on the relationship between organizational capabilities and data and analytics strategy is illustrated below.

Note that there is clear line-of-sight with the data and analytics strategy to the organizational strategy. This linkage helps inform an organization’s capabilities, define the business processes requiring support, and inform how to organize staff to deliver on analytical capabilities. From there it is critical to understand what a team is good at and where growth is required.

That’s where our knowledge and skills assessment comes into play. Recently we announced the latest release of the Healthcare Analytics Competency Model which is part of a Talent Development Program for both individuals and healthcare organizations that want to improve their analytical capabilities.

This program includes the ThotWave Healthcare Analytics Skills Assessment and Interest Inventory© to measure current competency levels across nine healthcare analytics knowledge domains. Gap reports highlight improvement opportunities for individuals as well as the aggregate needs facing teams as they transition from their current capabilities to an ideal future state.

Equipped with a strong understanding of the opportunities, ThotWave can staff project teams, help individuals on their own personal roadmap, and build institutional knowledge through formal and informal knowledge management strategies.

As we consider the amount of time it takes to onboard a new employee and brings them up to speed on the organization, the vocabulary, and culture of your organization, the technology, and the processes, we need to think differently about how we develop and nurture talent. In order to develop data fluency and develop talent, we need to move to a model where learning is a continuous process and not the chilly conference room with the notebook of slides that will gather dust in our cubicles once we return.

Learn More About the Talent Development Program