How much should algorithms dictate business decisions? That's the provocative question posed by Harvard Business Review (HBR) in a case study about hiring and promoting employees. But it's a question that can apply to a wide range of circumstances in a wide range of industries — including hospitality and events. As data becomes more available and manipulatable by new technologies, it's natural to assume that computers — powered by artificial intelligence and machine learning — know better than people. Natural, but not always advisable. As HBR points out, not all data is created equal. Make sure you consider these possibilities before ignoring your intuition altogether.
Biased DataLike the performance reviews in HBR's case study, data can be biased. Hotels use customer reviews and attendee surveys to inform future decisions, but those are obviously subject to respondents' biases. Data's objectivity can also be compromised by those deciding what information to include or not include. For instance, the people who create and conduct a post-event survey inherently affect the results by deciding which questions to ask and how to word them.
Incomplete DataEvaluating historical trends in room demand, event bookings, seasonality, and more can help hospitality professionals manage their inventory and ensure positive customer experiences. But historical data that does not consider environmental impacts — major conventions coming to a city, unexpected weather patterns, etc. — can lead to flawed assumptions.
Erroneous DataEvery database has errors, and the saying "garbage in, garbage out" is important to keep in mind. Algorithmic decisions and predictions are only as good as the data on which they're based.
ConclusionSophisticated technology can analyze massive amounts of data to provide both historical and forward-looking insights, which help hospitality professionals make better decisions. First, though, there is a need for people to create and drive strategy and think critically about the data they're using to make decisions. When you're confident that the data you're working with are objective, complete, and accurate, they will be an invaluable resource for your decision-making.
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