Domain Knowledge in Data Science: An Overview
Acquiring data-skills (coding and statistics) and combining them with domain-specific knowledge is increasingly more important as the field matures.
“The world’s most valuable resource is no longer oil, but data”, claimed The Economist a few years ago. Indeed, companies continue to collect increasing amounts of data to serve business needs. And just like oil, raw data isn’t as valuable as “refined” data. Consequently, companies require an increasing number of data-savvy professionals. Among those data professionals, the most well known are the “Data Scientists”. But new technical roles have emerged as well, such as Data Analysts or Data Engineers.
Arguably, domain-expertise in areas like business, finance, or science and engineering, combined with solid data-skills (coding and statistics), are far preferable than purely data-handling knowledge, no matter how sophisticate.
This is specially so as the field matures, as more automatic (no-code) tools become available, making the theoretical side of Data Science an area of interest for fewer and fewer people over time: only to those who work in the development of those automatic tools.
On the other hand, if you develop domain expertise, you can:
- Understand the big picture of what the data is about,
- Understand the business goal of working with this particular data,
- Ask the right questions in order to determine problem to solve,
- Communicate effectively with non-technical peers in your industry,
- Determine a relevant criterion to measure the success of a model.
These matters are undoubtedly crucial when working for an organization.
Also, we should consider the level of maturity of the field of Data Science. While today it is a still a relatively new area, there are many ongoing efforts to improve the infrastructure and automate software development tasks.
In a few years, much of the technical heavy-lifting might well disappear. With domain knowledge, on the other hand, you will be able to target the right business problem to solve with the available data-science techniques, whatever those might be. Demand for such skill will never go away.
How to build domain knowledge?
Developing domain knowledge is probably one of the keys of a long-term career strategy. But how can this be accomplished?
At first, it’s important to acknowledge that this will take time. For this reason, trying to work in the same industry for a few years can be a good idea.
This doesn’t mean that you can’t switch jobs to another company, of course. However, when switching jobs, staying around the same application area for a while is one of the aspects to consider: whether you are working on applications in the insurance industry, in e-commerce, or in real estate, just try to stick around these areas for a while.
Another way of deepening the domain knowledge in a given industry could be working in different areas of the same company, too.
Finally, any industry has its own specific literature: try to find some good introductory books and review papers. Too much specialization is usually not what you are looking for at this time.
Last but not least, simply trying to find some time to just talk with people in this industry, or attending industry conferences can help as well.