Demystifying Big Data: How to Gather and Utilize Info to Positively Impact Business

By Lisa Terry, Contributing Editor | October 08, 2012

Big Data is a hot IT topic, prompting some companies to urgently convene a task force to study it — and many more feeling uncomfortable that they haven’t.
But like cloud before it, the meaning of big data, as well as the urgency to create a strategy to deal with it, is unclear. “Big data is really a marketing term being thrown about by vendors to make it sound like they’re offering something new, which mostly they aren’t, other than incrementally,” says Douglas Rice, executive VP & CEO of Hotel Technology Next Generation (www.htng.org). “If you want to talk about big data, you need to get specific about what new data is being stored and what features/functionality it’s going to provide.”

First, identify the problem opportunity
IT associations’ attempts to define big data often mention four characteristics: volume, variety, velocity, and value. The idea is that companies are rapidly amassing huge quantities of both structured data, things like transactions, folios and basic customer data, as well as unstructured data, such as social media messages, guest comment cards and video. Unfortunately, few are equipped with the right technology, or even a plan, to boil down the ocean of data into insights that help them run their businesses better.

In hospitality, that means missed opportunities to enhance the guest experience. Hospitality companies have lots of separate systems where they collect data about their guests — what they’re looking at on the company’s website, line item purchase detail, dietary preferences, how they set their room temperature, how they’re using mobile apps  — but no place to bring it all together for a 360-degree view of the guest. Leveraging all of that in real time to better serve the guest on the spot is an even more daunting task.

Hospitality executives can take comfort in knowing that no one has solved this problem yet. These are early days in big data, and not every data problem is a monumental one. “Many organizations that think they have big data problems often just have data management problems,” explains Victoria Moore, research analyst for Info-Tech Research Group (www.infotech.com). Most experts advise not putting the cart before the horse: the first step is to figure out what you want to do with data that you can’t do now, then seek out expertise to determine if big data technologies are part of the solution, and start small.

Lee Holman, lead retail analyst at IHL Group (www.ihlservices.com), says only some verticals within hospitality are likely to need big data solutions. National table service restaurant chains, gaming, cruise lines, theme parks and lodging may find value using big data technologies to analyze marketing campaigns and customer experience/sentiment, especially for social media, Holman says. Gaming will have additional applications in fraud detection; cruise lines in immigration/passport control; and theme parks in churn analysis. Big data may also find a role in real estate selection. Holman sees less application in the quick-service restaurant segment right now, particularly at store level.

“An early adopter of big data solutions will have an edge on improving the price optimization system, winning over more customers, and it will give them a chance to nurture brand loyalty in those customers,” Moore states. “Once the guest is actually in the hotel, analytics can again come into play in helping to customize and enhance the guest’s stay by viewing past preferences.”

That’s the way Kees Hospitality (www.keesvacations.com) is using big data. The company manages private rental properties in major destination areas worldwide, and competes with global hotel brands. To manage rates, Kees worked with revenue management provider LeisureLink (www.leisurelink.com) as it developed its Marketspan solution, a cloud-based revenue management, merchandising and electronic distribution application.

LeisureLink says Marketspan is based on big data technologies. Instead of historical views, Marketspan applies its algorithms to a steady stream of real-time pricing data from a variety of sources including OTAs, wholesale packagers, websites and the client’s PMS. “It levels the playing field and allows my team to compete against major branded properties, without paying a franchiser,” says Jeremy Grogg, CEO of Kees, who credits the application with boosting revenue by 12% in the past year. Other potential uses include cross-channel analytics, hotspotting and customer engagement response analysis.

Conquering the Technology
Most hospitality software is built around transactional or operational databases, often SQL- or relational-based. This doesn’t work well for rapid analysis of large volumes of a combination of structured and unstructured data. “Vendors are moving in this direction and have started to recognize it as a need for the industry, but efforts have only just begun,”  Moore explains.

A handful of tier-one hospitality players have begun working with major IT companies to scope and test big data solutions. A common recommendation is a NoSQL database, used in conjunction with a NoSQL Business Intelligence solution. NoSQL rejects the relational model (i.e., tables) of database management. Such systems are useful when working with huge quantities of data that don’t require a relational structure. NoSQL is available in several different models, such as key-value, column, graph, or document-based formatting. A key component of success: having the right analytical talent capable of using these tools, an objective that is further complicated by the rapidly evolving industry opinions and best practices for the use of SQL versus NoSQL.  

Teradata (www.teradata.com) and SAP (www.sap.com) say operators should expect to have a second big data warehouse alongside their operational database tuned for big data. Teradata calls it a discovery platform, and says it uses map reduce technology to add structure (map) and quickly look through large volumes of data for insights (reduce). Eventually this second database will be integrated with hospitality line-of-business applications. SAP’s big data software stack uses a column database with in-memory technology to allow analysis of massive amounts of data in real time, along with BI tools tuned for
this purpose.

Analysis by McKinsey Global Institute (www.mckinsey.com) found the accommodations and food services industries face significant challenges in capturing value from big data. The barriers don’t come in availability of data, or even in the industry’s receptiveness to big data. The challenge for hospitality is a resource one, coming in both the talent and IT intensity required to conquer big data, the company reports.

Cognizant (www.cognizant.com) says hospitality companies will want to seek out IT partners to help them assess their needs and align big data initiatives with specific business goals. Important criteria to ask about are technology, intelligence, pricing, talent and data privacy.

As Kees Hospitality has discovered, the prospect of acquiring big data solutions in the cloud means this may be one IT trend in which smaller users are not left behind as tier one operators build up a substantial early lead.


Big Data in Hospitality

4 Hot Markets
Areas where the industry is ripe for data analysis

1. National table service restaurant chains, particularly in minging social media for consumer feedback.

2. Lodging,
with particular value coming in the ability to analyze the customer experience before, during and after their stay.

3. Gaming, not only to track guest activity while on property, but with added applications in fraud detection.

4. Cruise lines, tracking the full onboard experience, plus immigration and passport control.


3 Best Practices
Considering Big Data? Tech consulting firm Cognizant offers this advice:

1. Align big data initiatives with specific business goals.

2.Correlate, wherever possible, big data with structured data.

3. Create an IT strategy
for big data and plan to reshape the existing IT
operating model to accommodate the future.


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