Editorial
Data science is a big investment — hiring a data scientist could easily set you back six figures annually. But promises of increased profits have companies clamoring for deep analytics talent, regardless of cost.
The problem is, not all of those companies should be hiring data scientists in the first place.
If you don’t have the right foundation already in place, bringing a data scientist on board could be a costly mistake. Data science is about leveraging a company’s data to optimize operations or profitability. Therefore all of the processes that come before this stage — such as data warehousing and data engineering — should be fully operational before the data scientist recruiting process begins.
So how do you gauge if the time is right to take the plunge into data science? There’s no one right answer, but below are a few indicators that you’ll get a good return on your investment.
Indicator 1: You’re collecting a significant amount of data, and are ready to scale
A data scientist should never be your first hire. But when your business growth has yielded complex problems without obvious solutions, a data scientist can help you scale your operations, refine your product and ultimately increase your profit margins.
Hire one any sooner, and he or she might be underutilized — meaning that you’re essentially wasting your money. You wouldn’t hire Van Gogh to paint your kitchen, would you?
Let’s use an online marketplace as an example. In its early stages, an online business will concentrate its efforts on getting users or products onto its platform. Only once that business has passed the proof-of-concept phase and gathered a significant amount users and inventory does it make sense to hire a data scientist.
At that point, a data scientist can help the business perform more advanced tasks, like building powerful recommendation engines that put relevant products in front of the right customers — a feature that wouldn’t contribute a significant lift to the business’s bottom line unless it already had a sizable product suite or customer base.
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Indicator 2: You already have a business intelligence team in place
Business intelligence (BI) lays down the platform upon which data science operates.
While some significant overlap exists between the two fields, data science goes beyond BI — in addition to programming and data management knowledge, data scientists are often required to perform statistical interpretation as well as build, test and deploy predictive models. In other words, if what you’re looking for is access to data or regular reports, then what you really need is a BI solution, not a data scientist.
However, even if you’d like to be forecasting business outcomes and performing analytics at a higher level, you still need BI. Your BI team should lead the charge in getting your business’s data into a data warehouse and answering empirical questions like, “What are my quarterly sales?”
Once you have that data, you can bring in a data scientist to forecast future sales — a task that requires extensive predictive modeling experience and a significant collection of historical sales data.
Indicator 3: Your software engineers are telling you that you need a data scientist
It’s not uncommon for software engineers to make choices about the way software or an app will operate, such as the type of advertising that will be displayed, the placement of inventory, or the design of interactive features like buttons and menus. However, in many cases, a software engineer will make those decisions based on his or her best guesses, which can result in a lost opportunity for optimization.
For instance, someone in an engineering role might choose to place advertising in your app based on which ads have had the highest bids in recent days.
A data scientist, on the other hand, would ask questions about these ads before making a selection: What is the relevance of the ad to the content on the page? What is the effectiveness of that ad on individual users? How has that ad performed previously?
While this level of optimization might not matter to your business at first, there could come a day when it will have major impact. At that point, your software engineers will be more than happy to turn these tasks over to a data scientist.
Ultimately, it’s up to you and your decision makers to determine when it makes sense to hire a data scientist. But considering the investment required, you should have your data collection and management processes well handled before you post that job listing. If you skip those steps, you might never fully realize the value of data science.
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