For the third year in a row, Forbes[1] reported “Data Scientist” as the best job in America. The job pays well (median base salary of $110,000), and qualified data scientists are in high demand. Currently, Amazon is actively seeking to fill its 436 data scientist vacancies and Google is trying to hire another 135 data scientists.[2] Data science and data scientists are hot… and for good reason! The role of data scientists is to help businesses increase efficiency and profits by harnessing data. According to Amazon, “Data is the lifeblood of Amazon… Big data analytics is the magic wand for Amazon.”
Hotels would like magic wands as well, and every hotel in the country could likely benefit from the services of a well-qualified data scientist. But the majority of hoteliers do not have the generous budgets of Amazon or Google. And they will not likely employ an exclusive on-site data scientist. Rather, in most cases, the hotel’s GM must serve as their property’s own big data analyst.
“Big data,” the identified power behind Amazon’s magic wand, is one of the most popular new managerial buzz words. But it can be hard to determine how revolutionary this change is because “big data” is only loosely defined. Many professionals use it with very different ideas in mind. Big data can simply refer to data that is large in size (usually over 1 Terabyte), or it can mean data large enough that it has to be stored across multiple locations. The past decade has seen the definition of big data change and grow to describe increasingly complex phenomena. For all practical purposes, however, big data means unprecedented amounts of data.
GMs know that their hotels already generate more data than previously imaginable, and the amount of available data gathered is increasing exponentially. In most cases, hotels generate far more data than they actually use. GMs only rarely need to capture entirely new data, so the much more crucial challenge is how GMs can best manage the data they already have. Keeping that in mind, the methods GMs use regarding their data becomes critically important. GMs could learn a lot from data scientists about how to approach data analysis methodically and efficiently.
To address even the most complicated analytical concerns for their businesses, data scientists are trained to assess an organization’s data set and ask the following crucial questions:
