Hotel Profit Discovery

The 5 Cs of Building a Data-Driven Hotel Company

It is hard to argue with the claim that Analytics is the hottest buzzword in the hotel business today. More specifically, the impact of Big Data is now a global topic of conversation in industry, government, and education. In the hospitality industry, every function from RM to Marketing to F&B is becoming more data driven. Unfortunately, behind every technology bandwagon there are a plethora of unproven software startups cashing in on the unceasing pressure placed on managers to find a way to consistently outperform the competition or, at a minimum, beat last year’s results. Allow me to save you thousands or even hundreds of thousands of dollars. Analytics is not about software, it’s about people. If you are serious about beginning the transformation to a data-driven hotel company, where decisions are based on fact rather than faith, you should first be concentrating more on brains than bytes. Here are the five C’s of leading your very own analytics revolution.

Culture of Data

All successful analytics driven organizations have had to undergo a transformation from a culture where the HiPPO (Highest Paid Person’s Opinion) guides all decisions, to one where everyone in the organization defers to data insights to illuminate the best course of action. This cultural shift will require all employees to be engaged and committed to a data centered management philosophy. Unfortunately, changing a company’s culture does not happen overnight. For many, cultural change becomes an indefinite process which requires long-term commitment and ongoing leadership. You are unlikely to get much done if you take a bottom-up approach. Companies that don’t have upper management support to make cultural change a priority typically find that their biggest analytics initiatives fall flat.

Collaboration

When you think analytics, you may picture a lone statistician in a room with a giant server, crunching through formulas that spit out perfect answers to complex questions. Similar to how the Jonah Hill character in the movie Moneyball sat in his little office analyzing player performance. Ironically, the most successful analytics driven companies work exactly the opposite way. They employ a collaborative effort among analytical, technology, creative, and operations people to create a fruitful data driven environment. Let’s look at how working together is critical for the three basic steps of the analytics process.

Resources and time have to be redirected to enable better collaboration. In some companies, this may be accomplished through cross-functional teams that work on analytics problems together. It is very likely that, while the quality of their decisions may be better, these teams will be slower at arriving at conclusions. At first, this latency in decision making will have a real cost, but as everyone learns to share their knowledge and becomes more data focused, the payoffs will start to come faster. In time, all departments should be seamlessly collaborating on integrated data to establish one version of the truth.

Communication

Collaboration depends on easy communication. Today, the email has taken the place of the memo as the most formal form of business communication. More and more, high functioning business teams are connecting and sharing via private social platforms which look more like Facebook than Outlook. This real-time communication is essential for the success of a data-driven environment as a lot of business decisions are usually short-term and repetitive rather than long-term and project based. In a real time analytics environment you need to share the information quickly and get immediate feedback. In short, social platforms are a better fit for the type of collaboration required in an analytics world. Deploying resources like SocialCast or Jive can boost your success with analytics.

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Critical Thinking

The biggest obstacle to creating an analytics driven company is the severe lack of Critical Thinking and mathematics skills in the workforce. This is especially true in the service industries. In the next decade, companies will have to spend substantially on re-training their employees in quantitative problem solving if they are to see any returns from their analytics investments. We are currently in a dead zone where companies are implementing data warehouses and business intelligence platforms that are just sitting unused because managers are not equipped with the knowledge of how to use data to research a business problem. Even the smallest investment in mathematical training (online or live) should have a big impact on the quality of decisions in your properties.

Cultivation

According to Gartner Research, by 2018 only one-third of the required analytics talent will be available for hire. You can try to bridge this talent gap with software, and as mentioned above, there are many vendors waiting to take your money. However, expecting the “latest and greatest” analytics software to make up for a lack of analytics talent is like expecting the best set of Wusthof knives to instantly turn a valet into a gourmet chef. There is just no way around it – you need to begin cultivating your own analytics talent. This will mean additional training for those employees with high analytical potential. HR will have to begin the process of assessing the analytical skills of all employees in order to find talent in departments where number crunching may not be critical. You might also have to offer paid internships and sponsorships to university students. Investing now in developing analytics talent will help you mitigate the severe shortage that is coming.

The most forward thinking hotel companies are transforming their organizations into analytics driven enterprises. They are actively investing in the tools and talent that will allow them to dominate the market for years to come. To catch up to them, learn from their mistakes, and invest in the people side of analytics before you invest in the software side. This strategy will allow you to leapfrog many common obstacles in deploying analytics and will save you innumerable resources in the end.

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