Skip to main content
HomeBlogData Science

10 Simple Rules for Being a Good Research Partner

These 10 simple rules will help researchers and practitioners in data science overcome the frustrations they might feel when working with each other.
Jan 2018  · 6 min read

Half a century ago, C.P. Snow decried the gulf between the two cultures of science and humanities. There is just as great a gulf today between researchers and practitioners, which frequently makes collaboration unproductive. The ten simple rules described below cannot close this gap, but may help mitigate the frustration it creates.

(Note: "researcher vs. practitioner" is not necessarily congruent with "academia vs. industry", and many people are and do a mix of both, but the distinction is rhetorically useful.)

5 Rules If You Are a Researcher in Academia

1. Remember that companies work in weeks, not seasons.

Academic semesters are rooted in the seasons of an agricultural era, but practitioners in industry have to work at a more accelerated pace. In the time it takes you to write a grant, a company might develop and release two new versions of their product in order to keep up with their competition. Discuss timescales with your industrial research partners early on, and be realistic about how slowly things will proceed.

2. Be open

Research is of no use to practitioners who cannot easily find it and read it. While Jimmy Wales (the founder of Wikipedia) may not actually have said, "Open information drives out closed," the principle holds: with so much information freely available on the Internet, any paywall or login barrier put between you and your hoped-for audience will send a large number of people elsewhere.

More importantly, these barriers send a clear signal that you do not care if practitioners read your work or not: as one colleague observed rather sourly, it's the equivalent of inviting people to your house for dinner and then expecting them to pay for the drinks.

3. Value action over insight.

Paraphrasing Marx, the goal for practitioners is not to understand the world, but to change it. "We know X" is much less useful to them than "we can do Y". When presenting your findings, you should therefore focus on how someone might act on it.

One way to do this is to add slides titled, "What Difference Does It Make?" at strategic points in your presentations. If you can't think of what to write next, you may want to rethink what you're focused on.

4. Don't hesitate to sacrifice detail for clarity.

Understanding doesn't have to be complete in order to be actionable. For example, atoms aren't actually little colored balls connected by springs, but that's still a useful model in organic chemistry. You may need to hedge conclusions with qualifiers in order to get your work past Reviewer #3, but those "maybes" and "howevers" can often be omitted if they don't change what practitioners should try next.

5. Apologize in advance for the state of academic publishing.

Modern academic publishing isn't actually a conspiracy by a handful of large companies to line their pockets with government money that could and should be used to lift researchers out of penury, but it is functionally indistinguishable from a system that was. The best way to prepare your industry partners for its Kafkaesque production pipelines and interminable delays is to have them watch Gilliam's Brazil.

4 Rules If You Are a Practitioner in Industry

6. Remember that universities work in seasons, not weeks

The timescale mis-match decribed in Rule #1 is due in part to the fact that academic researchers are almost always multi-tasking, and that many of those tasks are things they've never been trained to do. As students, they juggle several courses at once (which effectively means that they answer to several bosses who don't communicate with each other). Later, they are responsible for teaching, writing grant proposals, and administrative duties.

Collectively, this mean that their "work week" is only a few hours long, and that they will often appear to move at a snail's pace. Be as sympathetic as you can: they are even less happy with the situation than you are.

7. Remember that academic success is measured in publications, not sales

University presidents routinely make about the economic value of research, but the only things that truly matter for academic advancement are publication, publication, and publication. Researchers are not given grants or tenure for doing things that are "merely useful", even if doing so requires a deep understanding of subtle complexities and months of hard work. For all the jokes practitioners make about the ivory tower, academic life is hard, uncertain, and poorly paid. People stay in it for the love of new knowledge; respecting their priorities is essential to building a productive relationship. (That said, practical problems often do unlock the door to genuinely new research topics by pushing researchers out of their comfort zone.)

8. Do the background reading

H.L. Mencken once wrote that, "There is always a well-known solution to every human problem---neat, plausible, and wrong." Your problem is almost certainly one of those, and is almost certainly more complex than you first realize. While Rule #4 tells researchers to sacrifice detail for clarity, this rule asks practitioners to make an effort to grasp at least some of that detail so that you don't waste time reinventing wheels and so that your research partner can think, work, and talk at full speed.

9. Don't overstate what has been learned.

This rule is also a complement to Rule #4. The "maybes" and "howevers" that researchers are so fond of do sometimes matter; if your research partner has found that regular doses of a new drug seems to slow tumor growth in lab rats, do not embarrass them by claiming that they have discovered a cure for cancer.

1 Rule For Both

10. Apologize in advance for the state of your data

The final rule applies equally to both researchers and practitioners. Files' names and locations, the meanings of column headers in tables, how those tables relate to one another, how missing values are represented and handles: everything that has made sense to you for years will suddenly seem a little foolish when you have to explain it to someone else. Apologize in advance, and then forgive yourself, because no matter how bad your data is, theirs may well be worse.


An old proverb says, "If you want to go fast, go alone. If you want to go far, go together." Researchers and practitioners can each do great things on their own, but both are better able to solve big problems---problems that really matter---if they find ways to work together.



Top 10 Data Science Tools To Use in 2024

The essential data science tools for beginners and data practitioners to efficiently ingest, process, analyze, visualize, and model the data.
Abid Ali Awan's photo

Abid Ali Awan

9 min


5 Common Data Science Challenges and Effective Solutions

Emerging technologies are changing the data science world, bringing new data science challenges to businesses. Here are 5 data science challenges and solutions.
DataCamp Team's photo

DataCamp Team

8 min


Four Ways Your Team Can Start Leveraging Data Science

Becoming a data-driven organization can significantly help you make more effective resource allocation decisions, but this requires nurturing and fostering a company-wide data culture. Here’s a rundown of various data science practices that you can quickl
DataCamp Team's photo

DataCamp Team

7 min


12 Best Practices to Grow in Your Data Career

While no two data science careers are exactly alike, there are some best practices that should work for everyone. Here’s our essential guide for developing your skills and reaching the next stage of your career.
Adel Nehme's photo

Adel Nehme

12 min


Ten Lessons for Future Data Scientists

Considering a new career in data science? This guide will prepare you for the journey.
Javier Canales Luna's photo

Javier Canales Luna

13 min

Data Cleaning Checklist@1x.png


[Infographic] Data Science Learning Checklist

Use this handy checklist to guide your data science learning journey.
DataCamp Team's photo

DataCamp Team

4 min

See MoreSee More