Data science work entails a large number of tasks, spanning across the data analytics spectrum, but with an emphasis on predictive analytics. Also, it involves a lot of investigation of the data at hand (Exploratory Data Analysis), the use of advanced math (e.g. Graph Analytics and Optimization), as well as some understanding of the domain to facilitate communication with the stakeholders of the data science project. It also includes querying databases, combining data from various sources (sometimes in real-time), and putting all the findings together in a narrative that's jargon-free and easy to follow. Oftentimes, this is not possible to do with a single individual, which is why data science teams are commonplace, particularly in larger organizations.
Data science consultancy is performing these tasks (or at least some of them) on a project basis, without being part of the organization. In this case, the data scientist is a guest star of sorts, working with analysts, data architects, BI professionals, or whoever manages products like this in that organization. She needs to ask questions to understand the problem at hand, what is required of her, the bigger picture of the project, and the data involved. This is something that can take several months and usually starts with a proof-of-concept project, particularly if the organization is new to data science. Naturally, because of the overheads of consultancy, a data scientist like that will be paid more, while it's not uncommon for the organization to cover logistical costs and other expenses.
It would make more sense for the consultant data scientist to be at the organization permanently, cutting down the costs, right? Well, although theoretically, that's true, not every organization out there has the budget for an in-house data scientist. Oftentimes, the managers involved are not convinced regarding the value-add of data science, which is why they are more willing to work with consultants, even if that means paying more in the short-term. Besides, a data science consultant is bound to be better value for money since they focus on quality and good customer service. Many data science consultants have vastly more experience and broader domain knowledge too, making them a valuable asset, particularly if you need something done swiftly. Also, note that certain organizations have a strict policy when it comes to recruiting, so it's much easier for someone to hire a data science consultant than to go through the whole process of getting a full-time employee on board, especially if they aren't sure about their budget in the years to come.
Since this is a very broad topic and it’s hard to do it justice in a single article, I decided to focus on the highlights of it. If you wish to learn more about the data science work in practice, along with other business-related matters relevant to the role of data scientist, I invite you to check out the Data Scientist Bedside Manner book I co-authored earlier this year. In it, we cover this topic from various angles along with some practical advice as to how you can make the bridging of the technical and the non-technical world smoother and effective. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.