It so happens I have a deep background on this topic. Over almost a decade, I deployed enterprise-level customer data consolidation for many of the world's leading luxury brands. In this capacity, developing the methodology and guiding execution of this specific desired outcome scores of times. The process always begins with the standardization of data and the rollout of those standards, followed by the cleanup of information sets once the standards are in place. Only after that point is true consolidation a reality.

Once upon a time in a hospitality career that began long ago, we used to get written warnings when data capture was not accurate. Ahh the good old days!

The problem with skipping this foundational layer is that you might achieve "aligned data" at the top, but if it doesn't exist throughout the entire organization, everyone is literally speaking a different language. The entire point of the exercise is to create a commonality of business and data approach that staff (and systems leveraging AI) understand universally and can turn into meaningful product or service encounters for the guest. Without this alignment, the effort inevitably falls apart. I know because I have watched clients insist on taking shortcuts, only to revisit the project later and do things properly.

Written Warnings

Our industry has a long history of not correctly architecting data. This legacy has led to a lack of application toward the maintenance and quality of data. The result is that many businesses are left behind, unable to achieve a "single source of truth," which is a necessary objective for any serious data strategy. For a long time, data has been considered the new oil, but we haven't given it the respect it deserves.

Even when data is distributed across various systems, we must have a deep respect for it. The idea that information availability is a new phenomenon is a fallacy. It's always been there, but the industry often fails to see it until it's too late. The challenge isn't the data's existence but our discipline in capturing and maintaining it. Once upon a time in a hospitality career that began long ago, we used to get written warnings when data capture was not accurate. Ahh the good old days!

Deep standardization of data sets is not just a good idea; it's absolutely essential. Without it, the value you can derive from your data will be severely limited. This applies directly to an organization's ability to answer critical business questions and position itself for a future with more advanced analytics and AI.

  • Is this approach essential? If failure is an option, no. Does anyone like failure?
  • Don't worry about whether you will be 'perfect' or not. You won't. It's an iterative process that requires continued oversight and improvement.

Technology can help with some of the heavy lifting and cleanup, but it is not a magic bullet. I have found that without meaningful, across-brand standards, consolidation limits value. There are no shortcuts to achieving data quality. The path to realizing the full value of your data begins with a commitment to fundamental principles of data capture, maintenance, and standardization. If you can answer yes, or mostly yes, to having this discipline in place, you are well-positioned to move to the next stage of data consolidation. Relatively quickly!

In a "now vs waiting" scenario, the "now" is undertaking this journey as one moves through each element of the business that will be connected to the single source. In hospitality, there are usually multiple data feeds, and this approach applies to all of them. This allows a start to be made, and information is augmented, business function by business function, to the master data set.

The Essential Data Connections for AI

I would argue that all data connections are essential if a well-rounded view of the customer is the objective. AI's value is derived from a holistic understanding of the customer across macro and micro information points. From this point, the creativity can begin.

However, if an organization is not in a position to achieve a holistic approach from the beginning, then the product bringing the most value to the business is a logical starting point. In hospitality, in most cases this will be the PMS.

Assuming a master CRM layer provides the single source of collated information, the essential interfaces are:

  • Systems supporting a line of business that includes customer information and transaction data: This data answers the high-level questions of who a customer is, what they buy, and which of our products resonate with them. This is specific to the business disposition of the hotel and provides a transactional and behavioral foundation for AI to build upon.
  • Feedback systems: These systems are essential to understand what customers think about our products and service. They provide the qualitative layer that allows AI to analyze sentiment, identify service gaps, and pinpoint areas for improvement, directly informing a better product or service.
  • Service management and delivery systems: This data answers the question of how customers like to be served. AI can analyze these interactions to optimize service delivery, predict service needs, and personalize service encounters.
  • Digital transaction systems: This data helps to understand what customers look at and their digital behavior. AI can leverage this to predict customer intent, personalize the digital experience, and drive more meaningful product and service encounters.

When these interfaces are unified and structured, they provide the necessary inputs for AI to move beyond simple automation and begin to deliver on its true promise of personalized service and strategic insight.