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. Knowing how to recognize and utilize big data to your company’s advantage unlocks great amounts of power. 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.
NB: This is an article from Snapshot
Take children for example – we all have been a child and many of us have raised one. 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 – it’s a big data problem and it’s time to clean up.
Big Data Problem
There are many reasons for data problems to occur, but one of the biggest ones is simply from low maturity and low value of treating the data. 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.
Data Value
Other industries have understood the value of data for quite some time now, but the hospitality industry is just getting started. Could we be too late? Understanding the value of data and creating a data strategy takes not only time, but also effort and investment. 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, the “tech titans”.
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. When it comes to the level of data sophistication among industry average hotels, they have the tools to only conduct basic data reporting. Given that these hotels are predominately still using on-premise legacy technologies with little integration, data silos are naturally created due to the tendency for systems to retain their data and causes difficulty in sending data from A to B with little hope of any type of data analysis. It’s a well-known fact that 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, integration costs between operational systems, secondary platforms, and tertiary platforms still 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, it’s great you know where your data is, all you’ve done is moved it into the closet or child’s toy box and there are still 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, as mentioned in our article explaining hospitality data platforms, 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 un/structured forms. While not impossible to find and manage, you need to use a variety of tools and systems to work with the data you need. 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 to use of data analysis to make better business decisions? Returning to the metaphor, wouldn’t you want your child to have always have a clean and organized room that they are happy about, you are happy about, and allows your child to have free time for fun with friends and family? Enter the data hub.
Data Hubs – Smart Groups
Data hubs are one solution that 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 multibrand 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.
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 and 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. Loyalty cards can be tracked by app vendors like iTravel, which can also offer incentives to use Apple Pay or Google Wallet and risk devaluation of the brand loyalty. Hotel brands that are among the industry average or semi-aware can resist the changes, but it will be futile and at a cost. Consumers are self-driving this trend and the tech titans know that. It’s rare time that hospitality players realize the value of data and how it is driving the industry.
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 three stages from data silos to harmonized un/structured data, hotels can begin to achieve the same predictive analysis that other technology giants have. However, designing a data strategy is easier said than done. To begin with, a data management program needs to be put in place where the development, execution, and supervision of all plans and programs that will deliver, control, and enhance the value of data and information are managed.
However, that’s not the final result smart organizations are aiming to achieve. Data are discrete elements. Data evolves into information from linking these accessible data elements which leads to data exploration. Once the data and information are managed and available for exploration, you can then build data strategy by design as an extension of the basic data management program. During this deliberate design phase, the focus of an organization is not on the quantity of data, but on the quality. This drives an organizational maturity by treating information as an invaluable and strategic company asset. Having reached the strategic asset level, information can drive knowledge which enables data driven decision making, multiple sources of truth, new informational dimensions and opens new opportunities for mining the rich data and information sets.
Beyond the knowledge level, the next step in data is wisdom, whereby organizational maturity reaches a point of applied data knowledge. Data wisdom is highly qualitative with active resources being constantly applied and is central to the core decision making process. Data wisdom is discoverable, minable and becomes available for applied predictive analytics and machine learning. We are no longer talking about simple reporting aggregation and revenue optimization, but about treating data as a science. Such data maturity, when used as a competitive differentiator, can ultimately enable an organization to become an industry leader.
While the value of data has evolved tremendously over the past 20 years – and business users recognize it – 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. To build such a comprehensive data strategy, they need to account for current business and technology commitments, while also addressing new goals and objectives.
Which side of the revolution do you want to be on? Will you still be the child in your messy room or have an organized mess? Or are you ready to enter the new frontier of predictive analysis and AI operations with central repository of harmonized data?
