I’ve been thinking a lot recently about the practice of data analysis in different settings and how the environment in which you work can affect the view you have on how things should be done. I’ve been working in academia for over 12 years now. I don’t have any industry data science experience, but long ago I worked as a software engineer at two companies. Obviously, my experience is biased on the academic side.
I’ve see an interesting divergence between what I see being written from data scientists in industry and my personal experience doing data science in academia. From the industry side, I see a lot of stuff about tooling/software and processes. This makes sense to me. Often, a company needs/wants to move quickly and doing so requires making decisions on a reasonable time scale. If decisions are made with data, then the process of collecting, organizing, analyzing, and communicating data needs to be well thought-out, systematized, rigorous, and streamlined. If everytime someone at the company had a question the data science team developed some novel custom coded-from-scratch solution, decisions would be made at a glacial pace, which is probably not good for business. In order to handle this type of situation you need solid tools and flexible workflows. You also need agreement within the company about how things are down and the processes that are followed.
Now, I don’t mean to imply that life at a company is easy, that there isn’t politics or bureacracy to deal with. But I see companies as much like individual countries, with a clear (hierarchical) leadership structure and decision-making process (okay, maybe ideal companies). Much like in a country, it might take some time to come to a decision about a policy or problem (e.g. health insurance), with much negotiation and horse-trading, but once consensus is arrived at, often the policy can be implemented across the country at a reasonable timescale. In a company, if a certain workflow or data process can be shown to be beneficial and perhaps improve profitability down the road, then a decision could be made to implement it. Ultimately, everyone within a company is in the same boat and is interested in seeing the company succeed.
When I worked at a company as a software developer, I’d sometimes run into a problem that was confusing or difficult to code. So I’d walk down to the systems engineer’s office (they guy who wrote the specification) and talk to him about it. We’d hash things out for a while and then figure out a way to go forward. Often the technical writers who wrote the documentation would come and ask me what exactly a certain module did and I’d explain it to them. Communication was usually quick and efficient because it usually occurred person-to-person and because we were all on the same team.
Academia is more like Europe, a somewhat loose federation of states that only communicates with each other because they have to. Each principal investigator is a country and s/he has to engage in constant (sometimes contentious) negotiations with other investigators (“countries”). As a data scientist, this can be tricky because unless I collect/generate my own data (which sometimes, I do), I have to negotiate with another investigator to obtain the data. Even if I were collaborating with that investigator from the very beginning of a study, I typically have very little direct control over the data collection process because those people don’t work for me. The result is often, the data come to me in some format over which I had little input, and I just have to deal with it. Sometimes this is a nice CSV file, but often it is not.
In good situations, I can talk with the investigator collecting the data and we can hash out a plan to put the data into a certain format. But even if we can agree on that, often the expertise will not be available on their end to get the data into that format, so I’ll end up having to do it myself anyway. In not-so-good situations, I can make all the arguments I want for an organized data collection and analysis workflow, but if the investigator doesn’t want to do it, can’t afford it, or doesn’t see any incentive, then it’s not going to happen. Ever.
However, even in the good situations, every investigator works in their own personal way. I mean, that’s why people go into academia, because you can “be your own boss” and work on problems that interest you. Most people develop a process for running their group/lab that most suits their personality. If you’re a data scientist, you need to figure out a way to mesh with each and every investigator you collaborate with. In addition, you need to adapt yourself to whatever data process each investigator has developed for their group. So if you’re working with a genomics person, you might need to learn about BAM files. For a neuroimaging collaborator, you’ll need to know about SPM. If one person doesn’t like tidy data, then that’s too bad. You need to deal with it (or don’t work with them). As a result, it’s difficult to develop a useful “system” for data science because any system that works for one collaborator is unlikely to work for another collaborator. In effect, each collaboration often results in a custom coded-from-scratch solution.
This contrast between companies and academia got me thinking about the Theory of the Firm. This is an economic theory that tries to explain why firms/companies develop at all, as opposed to individuals or small groups negotiating over an open market. My understanding is that it all comes down to how well you can write and enforce a contract between two parties. For example, if I need to manufacture iPhones, I can go to a contract manufacturer, given them the designs and the precise specifications/tolerances and they can just produce millions of them. However, if I need to design the iPhone, it’s a bit harder for me to go to another company and just say “Design an awesome new phone!” That kind of contract is difficult to write down, much less enforce. That other company will be operating off of different incentives from me and will likely not produce what I want. It’s probably better if I do the design work in-house. Ultimately, once the transaction costs of having two different companies work together become too high, it makes more sense for a company to do the work in-house.
I think collaborating on data analysis is a high transaction cost activity. Companies have an advantage in this realm to the extent that they can hire lots of data scientists to work in-house. Academics that are well-funded and have large labs can often hire a data analyst to work for them. This is good because it makes a well-trained person available at low transaction cost, but this setup is the exception. PIs with smaller labs barely have enough funding to do their experiments and so either have to analyze the data themselves (for which they may not be appropriately trained) or collaborate with someone willing to do it. Large academic centers often have research cores that provide data analysis services, but this doesn’t change the fact that data analysis that occurs “outside the company” dramatically increases the transaction costs of doing the research. Because data analysis is a highly iterative process, each time you have to go back in forth with an outside entity, the costs go up.
I think it’s possible to see a time when data analysis can effectively be made external. I mean, Apple used to manufacture all its products, but has shifted to contract manufacturing to great success. But I think we will have to develop a much better understanding of the data analysis process before we see the transaction costs start to go down.comments powered by Disqus