We characterize the skills-profile of researchers at three different stages. Each comes with a map you can use as a reference, benchmark, or provocative comparison:

This was a continued trend in workshop conversations, participant responses, and the clearest signal of all in the skills we broke down by years of experience:

Evangelizing the work is a desirable and important skill at all levels. And its importance down the road only increases, sharply. This is an artifact of the discipline’s emergence and lack of accepted models for how research is arranged in the organization. Our more senior leaders are at the vanguard, practicing and testing ways to frame the work from a program level, learning how to integrate with organizational leadership.

It is always useful to build a capability for evangelizing the research practice. At every level of work, there is a larger context in which the project’s impact depends on others’ understanding of what they’ll get out of the process, and how it will work.  

The nuance, and continual challenge, is that proving the value of the work relies on past success or trust, and a clear understanding of the organization that constrains and enables it. To gain a say in the fate of research, we need to show how our projects will enrich that larger context and explain—repeatedly—past success and the specific outcomes that work led to, in a language the organization understands.

In workshops, we asked researchers to rate themselves on a variety of “human skills”—what we used to call “soft skills”—the real drivers that move our work from ideas to impact. Independent of years of experience and work context, researchers rated themselves fairly well in all of these skills, including “Organizational Diplomacy,” “Stakeholder Management / Communication,” and “Project management.”

Across the board, these researchers also identified their most pressing challenges as gaining trust and buy-in from the team, helping others see the value of the work, and having time to do all the work lined up. It's all "people work"—challenges that need to be addressed with human skills. There is opportunity for a more mindful reflection on how well we work with others. 

We hypothesize a mismatch between how we see these skills in ourselves, and the true range of opportunity available for improvement. Researchers are not a unique discipline in facing the problem, but we are equipped with the disposition to solve it, and well. A majority of us have an open opportunity for growth: to step out a level, to take the human interactions that drive our work as a design challenge, one that starts with understanding what's happening, just like any other complex space we approach in the course of work.

A deliberate focus here will create dramatic improvements in the outcomes of our work.

The importance of developing research questions was a clear trend. A fifth of all 2019 workshop participants rated ‘Develop research questions from team needs’ as one of their top three most-useful skills.

It remains useful because new contexts and more complex projects entail new questions. It feels like a researcher’s magic trick to move us from ‘what we think we need to know’ to ‘what we need to learn’.  

But it’s just craft: we can all develop a sensitivity for how different kinds of questions and findings lead (or don’t) to different project outcomes. That sense can help shape research questions that we know will produce the most tractable kinds of data. With dedicated time for focus and reflection on past projects, and a good understanding of the work at hand, every past experience is available as guide to the way forward.

This skill of getting to the right questions, honed in research, is also broadly applicable. We can take this practice and apply that same critical clarity to other areas of the work or our lives.

The data reveals an interesting gap, an unsolved want. A small number of researchers consider working with product analytics as one of their most useful skills. Over twice as many count it as one of the top skills they desire to learn.

Data is ubiquitous (at times unhealthily so); the tools to work with it simply are not. Product analytics, in its newest form, is a discipline emerging from marketing, engineering, and operations, with a healthy dose of data science. Organizations collect and report on a huge array of measures, but with different fidelity, tooling, and accessibility. It’s not always easy or straightforward, not even for quantitative researchers, and at times it can be legally or operationally difficult to work with the data.

But so many researchers desire this skill: it is a powerful way to re-interpret and amplify qualitative insight, and it is the dominant (read: most-respected) language in modern organizations. Behavioral surveys, analytics, and models can all enrich and amplify a qualitative understanding. Quantitative outputs can drive and help shape high-impact research questions. Finding the right pieces to mix together, and how to do it, is real work. 

How far should you dig into quantitative work? It’s the same as the answer to "designers and code:” first, understand its structure and tempo. Then learn as much as you need to improve or enable your work. You will have a nice moving target to shoot at.

For researchers who had a career goal in mind, we asked, “What are you working towards right now?” Respondents at all levels of experience listed learning-relating aims as their top goal.

Sometimes it’s not easy to find time for learning, and at others there’s too much to learn at once; either way, practice is imperative for growth. Learning built on curiosity and a desire to develop the craft is a core trait of successful researchers. Let us find the time to play, explore, and learn how you best learn to learn the work.

Develop your own useful questions, understand where to focus, inspect, adapt, and start again—don’t stop!

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