Big Data

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March 01, 2015
Big Data
Jon Inge -

The momentum behind Big Data doesn’t seem to be slowing down, which encourages more hoteliers to dive into it.

There’s a vital need for analyzable data in three areas: revenue management, CRM and business intelligence (BI). Historically these three areas have worked from very separate databases, but with so much overlap between operational areas these days they should really be considered as three views of the same one. Big Data, indeed.

Big Data comes in two forms. One is the accumulation detail about every aspect of a hotel’s guests and operations stored with the intent of identifying repeat guests, catering to their needs and preferences, and targeting others like them with carefully designed campaigns. The other is the massive flow of transitory data, overwhelming in volume of detail that’s not especially relevant in and of itself but is very much worth analyzing for trends. An example might be the reaction of different market segments to new pricing approaches; you certainly would like to know who’s interested enough to click on a new offer, but it’s just as important to see whether interest overall is growing or declining, and the sooner the better.

We’ve been gathering the first type of data for many years; repeat guests are highly valued and it’s far less expensive to encourage them to stay again than to attract a new guest. However, as the market continues to sub-divide into smaller segments, and as our consumer-driven society continues to lead people to expect increasingly more personal service, the drive to gather as much detail as possible isn’t slowing down.

The problem is, this data isn’t useful unless it’s actionable, which means two things; it must be accurate, and it must allow someone to do something useful with it. Making sure it’s accurate is unending, tough, gritty work. Staff must be trained both to enter it in the first place and to enter it the same way on every shift and at every property in a chain. It must be checked constantly for duplicate profiles, inaccurate entries, changes of address and so on. This never ends, and it’s essential.
Then there’s the question of normalizing it. Unless the whole chain is running on an ERP system, both guest and operational data will be gathered from several different systems, and it may not be defined quite the same way in each. Translating packages and room type codes between reservations and front desk systems, consolidating address data from systems that keep it different field formats, tying F&B order quantities (the infamous “each” unit) to recipe amounts, etc.; there has to be a consistent way of doing all these things.
The interest in analyzing unstructured data such as guests’ feedback on social networks adds more complexity; tools are available to help with this, but it has to be carefully considered in context. Was a major storm inconveniencing guests at every hotel in the city, or was it only your staff that had trouble dealing with guests’ needs and so generated poor reviews? Do guests from different countries use words you consider key to analyzing their sentiments in the same way?

Because of this constant and essential effort, it’s even more important to be sure that the data we accumulate and normalize actually enables some meaningful action. This is where the Catch 22 comes in; you don’t always know when a piece of data is going to prove valuable. There’s an understandable tendency to want to keep everything that “might be useful one day,” but unless you expend the energy to make sure it’s accurate and consistent, it never will be. The best way out of this is to involve all departments that might have an interest in tracking something in defining how they’d use it and what parameters are needed to make it valuable. Otherwise, save yourself the trouble.

Sometimes systems don’t even have a way to capture something marketing would like to evaluate. Most vendors have a way to add a certain number of user-defined fields, but they’re not always on the same screen as the main data elements they complement, and they’re not always easily reportable. This has led to more than one systems upgrade.

Even the analysis aspect needs to be kept up to date. As more data becomes of interest from more areas, accumulates more quickly and is needed in shorter timeframes, old revenue management and BI engines may no longer be adequate. If nothing else, newer, more graphical user interfaces will almost certainly be required to highlight trends and outliers amidst the oceans of “normal” activity.

There are two thoughts on rolling out Big Data. One is that the potential topic is so huge that it would be overwhelming to try to do it all at once, and so start with a small, well-defined operational area with clearly defined benefits.  The other believes that even a small project will have ramifications into other areas – data is seldom isolated and changes in one area will usually impact others – and so, given that the best way to clean up dirty data is to air it out in full daylight, one should dive right in and put the initial results out for everyone to see. Inconsistencies, outliers and plain bad data entries will be identified quickly, and the sooner they’re corrected, the greater the benefits to the complete organization, not just the analysts.

Is Big Data worth all the effort? Absolutely; there’s no slowing the growth in data generation, and the refinements in operational and marketing efficiency arising from its thoughtful analysis are very real. Just make sure you approach the task in a multidepartmental way to identify and collect only what’s truly useful and worth spending the time on to make it accurate and consistent. As with so many things in modern life, just because it’s technically possible doesn’t mean it’s worth doing.

Jon Inge is an independent consultant specializing in technology at the property level. He can be reached at

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