Management for Hospitality
7. What kind of technology would you recommend for a typical hotel and casino to support their data, data visualization, and
analytical efforts?
(continued )
Advanced generic tools like Tableau are ideal because they already arrive as an Excel killer with a wide range of features and can theoretically be implemented by people with low knowledge of analytics tools. But this is also where they are the hardest to implement since using a generic tool means you have to do all the heavy lifting yourself. For example, choosing which stats to show, what’s important and what’s not, whether the information should be shown as a pie chart, a bar chart, or in a column may all seem trivial when selecting a solution software but becomes hard work when you actually set them up.
When considering a generic analytics solution for your hotel, make sure you thoroughly investigate whether the tool can be customized to every potential metric you may want to measure. The more you know before starting, the less likely you’ll be to get stuck later.
On the other hand, hotel-specific analytics solutions (such as SnapShot Analytics, Juyo Analytics, and LodgIQ) have already taken care of the hard part. They’re designed to present relevant information in the best possible way. Hotel-specific analytics solutions can save time because they are designed to handle the difficulties of aggregating hotel data.
These analytics providers also know the industry, so the visualizations are designed to represent data in the most useful way for hotel management.
What’s more, the better hospitality-specific analytics solutions also have ongoing partnerships with PMS providers and other data sources, vastly accelerating the implementation process.
A final note on generic versus hotel-specific analytics solutions should be made on the topic of predictive analytics and prescriptive analytics.
Hotel-specific solutions often already have either predictive analytics or prescriptive analytics built in. This creates an opportunity for hotels to develop massive (and highly actionable) business intelligence.
Hotel-specific solutions are usually the better option for small to medium-size hotels and groups. Generic tools are customizable, but they’re often challenging to customize or integrate with data sources like PMSs, a job normally only available to very large hotel chains that have the resources to launch such a project. Hospitality-specific data analytics tools are faster and cheaper to set up and are vastly more powerful and responsive.
(continued )
conclusion
Data visualization tools are putting more power in the hands of the business user, while reducing the burden on IT to create ad hoc reports and maintain dashboards. Advances in technology, including com- plex data management, are facilitating more access to more data than ever before. New capabilities like mapping and animation, as well as a self-service approach, are empowering the business to explore rela- tionships and answer questions without requiring support from IT or analytics.
Remember, the data is what it is. You are the one who puts con- text around it. When you get good at providing that context, you will transform what people think. This is, obviously, a big responsibility.
Darrell Huff said, “If you torture the data long enough, it will confess to anything.” This is scarily true. It’s up to you to make sure you are maintaining the neutrality of the data. Let it tell you what it is; don’t force it. Not that you can’t take some license to show it in a light that supports your story, but be careful, and be respectful.
The concept that a broader set of individuals across the organi- zation can have access to data and analytics that were formerly the domain of IT or data scientists is known as “democratization of data.”
With the clear shortage of data science talent, the idea that a broader set of resources can perform some of these functions is quite attractive to organizations today.
Recently, the term “citizen data scientist” has become a popular way to describe the persona that is enabled by the access to data analytics that I describe in this chapter. The citizen data scientist is not necessarily formally trained in business intelligence or statistics and might not be in a role purely involving data analytics. They do understand the business and are familiar with the data. Now they are able to perform data manipulation and basic analyses through wizard- driven, highly visual interfaces. Most experts agree that enabling citi- zen data scientists will not replace the need for educated and trained data scientists, but it will greatly increase the efficiency of data preparation and speed up routine analyses. The right tools are what make this possible. There is an interesting blog post on this topic, titled “How the Citizen Data Scientist Will Democratize Big Data,” at
www.forbes.com.3 Think about whether enabling citizen data scientists will help you to overcome resource challenges in your organization.
Much of what I described in this chapter is about looking back on historical data rather than predicting what may happen in the future.
In the next chapter, I will describe the difference between this historical view and predictive analytics and introduce you to the types of advanced analytics that will help your organization move from reactive to proac- tive decision making.
ADDitionAl resources
■ 7 Considerations for Visualization Deployment, www.sas.com/en_us/whitepapers/
iia-data-visualization-7-considerations-for-deployment-106892.html.
■ Visualization Best Practices (All Analytics), www.allanalytics.com/archives .asp?section_id=3365.
■ Great reference site for visual inspiration: Perceptual Edge, www.perceptualedge .com.
■ Stephen Few, “Save the Pies for Dessert,” Perceptual Edge, August 2007, www .perceptualedge.com/articles/visual_business_intelligence/save_the_pies_for_
dessert.pdf.
notes
1. Portions of this chapter taken from Kelly McGuire, Telling a Story with Data, Hotel Business Review, www.hotelexecutive.com/business_review/3723/telling-a-story- with-data.
2. From HSMAI Insights Video Series “How Will Revenue Management Look Different in 2020?” www.hsmai.org/knowledge/multimedia.cfm?ItemNumber=22136.
3. Bernard Marr, “How the Citizen Data Scientist Will Democratize Big Data,” Forbes, April 1, 2016, www.forbes.com/sites/bernardmarr/2016/04/01/howthe-citizen-data- scientist-will-democratize-big-data/#257f316e4557.
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