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Big data and the need for a data strategy

02/01/2019| 4:52:58 PM| 中文

Amazon’s recommendation engine is estimated to generate more than 1/3 of its purchases by using AI to identify, rank, and serve up recommendations. But what would happen if Amazon decided to start selling hotel rooms using this knowledge?

Big data. We’ve all heard and talked about it. Big data is a major component of every industry and hospitality is not an exception. However, having access to data is one thing, but to maximize big data, it needs to be actionable and clearly defined in a detailed data strategy.

Let's use a metaphor of a child playing in a room and when you arrive, it’s a complete mess with toys, books, and clothing everywhere. You immediately start thinking of how to clean up the mess into an organized state. This is how many of today’s data points look if they’re not structured properly and well managed. Without organization of any form, big data is one gigantic room with data points scattered around and thrown everywhere.

While big data has been around for quite some time now, the hospitality industry is still at a low level of maturity in comparison to other industries when handling data. The combination of silos between systems and technologies, large amounts of unstructured data, and legacy systems, hinders the hospitality industry from advancing and taking advantage of this valuable resource and deploying big data management. In order to resolve these barriers, companies must endure high integration costs, as well as allocate additional resources, money and time which could be spent on other important matters.

For a better grasp of the data status in the hospitality industry, it is possible to look at the industry through four ways: the industry average, mid-sized/larger groups, smart groups, and external industry influences.

Data Silos - Industry Average

The industry average is made up of mostly groups and independent hotels with legacy data and technology-based thinking. These hotels tend to use premise-based technology, have invested less in technology solutions and generally have a lower value assigned to data. They have the tools to only conduct basic data reporting. With little integration in legacy technologies, data silos are naturally created. Hospitality organizations deal with many more systems besides PMS, including CRS, GDSs, OTAs, POS, and more. Hotels have data stockpiled in operational systems, secondary platforms like online travel agency partners, and tertiary platforms for functions like SaaS systems or messaging. Attempting to pull all of these different types of data into an actionable format that hoteliers can use is difficult, especially when some third parties produce data and are not inclined to easily share data. Furthermore, system integration costs remain high and the industry suppliers still do not cooperate easily. 

Data Lakes - Mid-sized/Larger Groups

Returning to the child metaphor, progress in cleaning up a room and managing un/structured data can be looked at as a data lake management problem. While improvements have been made in data management, just like the child’s toys have been put into the closet, still there are many steps to be taken to convert that data into information or knowledge. Hotels that fall under this category tend to be mid-sized to larger hotel groups who understand that data has value. They have invested more than the industry average in technology, including cloud solutions, and have a data strategy that is evolving. Essentially, data lakes copy data from one place to another, but the data is left in its source format. While the data becomes centralized, it is still difficult to index the data and find what you are looking for when the data is left in unstructured forms. Data lakes act as a temporary storage solution to the data management problem. They also begin to address challenges of integration and data silos, but they don’t completely fix the issue. Hotels under the data lakes scenario are able to produce basic reporting as well as report aggregation and data warehousing.

Wouldn’t you want to maximize your hotel data in a strategic, organized way that allows the use of data analysis to make better business decisions? Returning to the metaphor, wouldn’t you want your child to have a clean and organized room and have fun with friends and family? Enter the data hub.

Data Hubs - Smart Groups

Data hubs are one solution for the smart groups of the hospitality industry who place a high value on data, have utilized to help properly collect, harmonize and prepare data for deeper analysis and mining. These hotels and groups are rare to the industry and have realized this high value of data is the key to understanding the customer, the business and how to get ahead of their competitors. Smart groups see technology investments as a business differentiator and have developed a mature data strategy. With their investments and full understanding of the value of data, smart groups are able to perform data aggregation, predictive analytics and deep mining, which extends their knowledge and capabilities well beyond the average in the industry.

No matter the size of your company, whether you’re a multi-brand chain or independent property, data hubs allow for businesses to optimize processes, increase their technology ROI and generate keen customer insights through effective data management.

By incorporating a data hub into your data strategy, your hotel begins to open up to even more capabilities for next level data management, such as AI operations, machine learning, and predictive analytics.

Gatekeepers - Tech Titans

Tech titans like Apple, Amazon, Google, and Facebook, have paved the way for the tech industry and continue to do so today. However, they have increasingly begun to pay more attention to the hospitality industry and see data as part of the product. With advanced technology solutions, the dominant players are the big consumer tech companies, or gatekeepers, as we call them, control devices and what customers receive for searches. The control that the gatekeepers possess will extend to the types of content they require for ad ranking and placement and how hotels are presented to customers. They are the gatekeepers to access the data of your own guests and they already have a deep understanding of them. They will own your customer unless you have your own data strategy. As Arne Sorenson, CEO of Marriott International, so rightly said, “We are in an absolute war for who owns the customer.”

Tech titans are not only dominating control to information, but also see data as their product. These companies do not want to own or manage hotel, but instead they want a piece of the value chain in filling hotels - many services that are traditionally delivered by brands will shift to vendors offering convenient consolidation to consumers, and they are already using advanced technologies to mine the data they already have.

There are countless examples of how the tech titans are becoming more involved in the hospitality industry. Amazon has launched Alexa for Hospitality for hotel rooms, and booking a room through Alexa won't be long to follow. Google Travel is also involved in the industry by combining mapping, search, and hospitality data. Alibaba’s online travel platform Fliggy and their Alipay payment platform are driving the need for implementing their payment type outside of China. You can even book a room through Facebook’s Instagram app now, not to mention the number of Instagram ads and social media posts about hotels.

The tech titans don't stop there. All functions that are device-driven give an advantage to the device vendors who own the consumer’s actions when they own their device. 

Data Strategy

Imagine a world where you have the ability to predict your guest’s future purchases, wants and needs. Predictive analysis is achieved when full advantage of data is taken and applied through a combination of algorithms and machine learning to make predictions of which future outcomes are most likely. Many technology companies are already adept to predicting the next product a consumer wants to buy and then serve it up as a recommendation. For example, Amazon’s recommendation engine is estimated to generate more than one-third of its consumer purchases by using artificial intelligence to identify, rank, and serve up the most appropriate product recommendations. But what would happen if Amazon decided to start selling hotel rooms using this knowledge? In order to get to this ultimate goal of predictive analysis, it is imperative to implement a strong data strategy.

In hospitality, companies are beginning to link predictive analytics with geolocation data to deliver effective recommendations on-property and in real-time through mobile applications. For instance, a hotel company is piloting a program to drive ancillary revenues through the use of next-product-to-buy algorithms. The predictive analytics revolution in hospitality has only just begun as more and more new players are entering the playing field and accelerating innovation with a data strategy.

It’s not as simple as purchasing new technology to accomplish predictive analysis. You can’t tell a child to clean up their room one time and expect a perfectly tidy room from then on - maintaining a data strategy is a hard work. By identifying the issue and navigating through all stages from data silos to harmonized structured data, hotels can begin to achieve the same predictive analysis that other technology giants have. 

Having reached the strategic asset level, information can drive knowledge which enables data driven decision making, new informational dimensions and opens new opportunities for mining the rich data and information sets.

While the value of data has evolved tremendously over the past 20 years, few companies have adjusted their approaches to capturing, sharing, and managing corporate data assets. Their behavior reflects an outdated, underlying belief that data is simply an application by product. Organizations need to create tailored data strategies that match today’s realities.

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TAGS: big data | data strategy
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