Data Analytics: Design the Need, Find the Solution

Written by Gideon Dean, Vice President of Partnerships, Michael Brizendine and Raju Pillai at Onyx CenterSource & Kaycee Rodgers, Data Analyst at Hotel Engine
Edited by Laurie Pumper, Communication Director, HEDNA

Between data infrastructure, visualization tools, reporting cadence, and more, reporting and analytics is an expansive subject with a wide range of variables to consider. For hotel companies looking to explore analytics, it can be tough to know where to start. We call the work required to find the best solution to meet a data request ”designing the need.”

Connie Marianacci and Gideon Dean of The HEDNA Hotel Analytics Working Group sat down for a virtual interview with Michael Brizendine and Raju Pillai from Onyx. Through the conversation, we identified the following steps as the most effective method to find solutions to data needs:

• Make data a core part of your business strategy. Start with a strong analytics foundation, complete with best practices for the company.
• Develop a comprehensive understanding of the question we want to answer.
• Collaborate with key stakeholders when building your design plan.
• Prioritize requests with cross-functional use cases whenever possible.

In this blog, we break down the value of working each step based on the experiences of our experts. Data teams who work toward defining the need in this way are sure to see success, and we want to help get you there.

Reporting vs. Analytics

Before we dive in, let’s explore the difference between hotel reports and hotel analytics. While the two may not seem vastly different, consider the amount of auto-generated PMS and CRS reports available at the hotel level. These reports provide the facts on what’s been recorded within the hotel system. Examples include rooms sold, rooms available, generated revenue, loyalty bookings, and guest information. This level of data is typically used by the operational staff and management companies to monitor day-to-day business along with hotel financials.

Our experts believe the shift to hotel analytics occurs when we start blending internal data with external data. This is often achieved by using pattern-based algorithms, adding the context of the world around us to our own hotels’ business trends. Merging internal and external data also allows us to benchmark performance against our competitors. This level of data is most often sought out by revenue management and corporate level teams. Hospitality professionals are accustomed to accessing hotel analytics through third-party data aggregators. A downside of these tools is the minimal ability to drill further into the data and learn more details.

As business evolves and we look to data to drive business decisions, in-depth and flexible analytics are essential. Companies that invest in building their own analytics are at a great advantage. These tools allow us to look beyond operational data and tailor analytics to specific business needs.

Step 1: Setting the Foundation for Effective Analytics

Raju believes that companies today should think of analytics as an integral part of their core business strategy. Choosing a platform that can scale as business evolves is an essential first step. Additionally, the company should align on the data skills needed to build a well-rounded team. Reaching a balance of technical and operational skills across teams creates opportunities for effective collaboration. With a strong foundation in place, stakeholders and data teams can approach new requests with confidence. Here are some tips to help you get there:

• A glossary of key business metrics and definitions is essential for companies working with analytics. Agree upon calculations of key metrics, naming conventions, and use cases for each metric across all tools. Make the glossary shareable to increase adoption of these definitions across the organization. This helps reduce discrepancies when pulling the same metrics out of different BI tools — a common issue for companies using analytics.
• If there are gaps in industry experience across the company, familiarize associates with data-related industry norms and limitations. This knowledge helps us identify practical approaches to solving analytics needs.
• Always have a pulse on data security, building governance and controls into the foundation of the data platform.

Step 2: Finding the “Why” Behind the Need

With the essential foundational items in place, the next step in designing the need is to generate some healthy discussion. Start by reducing the request down to the core business question. Ensure everyone involved has a solid understanding of what we are seeking to solve. Raju believes companies should focus on “extracting insights that are going to help you improve your business versus just reporting on how your business is doing.” Some tips to get you there:

• New analytical requests should always be aligned with key objectives. This fosters a clearer path to understand the “why” behind a given request.
• In Michael’s 25 years of experience, he has found people tend to jump to a solution without understanding the true reasoning or challenges behind the business need. Do not shy away from the inclination to ask “why” several times. Identifying the full reasoning for each element of the request is critical to success.
• Keep the operational stakeholders focused on providing the business requirements and success criteria. The technical team should be responsible for creating the most flexible and scalable solution to meet those defined needs.

Step 3: Building Your Design Plan

Once we understand the question at hand, we move to the actual design stage. The analytics format should be tailored to the specific use case. While planning our analytics design, we may uncover more context and requirements for the use case. For this reason, Michael recommends keeping key stakeholders involved in the process. Working with stakeholders helps fill in knowledge gaps between technical and operational concepts. Here are some more tips to guide the way:

• Leverage flexible and open technology. This will allow for your data team to modify data models as business requirements shift.
• Proof of concepts are valuable, as they allow us to validate feasibility, reduce risks, and optimize on cost. It is important to keep the proof of concept work minimal and time-boxed to stay on track.
• Collaboration also fosters strong cross-departmental rapport. This is extremely valuable if there is a need for buy-in on projects across the company.

Step 4: Prioritization

While servicing new analytics requests, we may face the challenge of balancing prioritization levels across departments. Analytics seekers should include a sense of the urgency for their data needs with the request. This helps the data team define a timeline for the project and set reasonable expectations. Requests with solutions that can be used by several departments to further progress toward goals should come first. Our experts agree that these types of requests are also the ones that most often cross the finish line. Additional insight on ways to approach these scenarios:

• • Encourage analytics seekers to consider who else could benefit from the analytics requested. Communicate all additional use cases to those who decide on prioritization. For example, a requested OTA analysis could be used by all hotels within your brand to solve a broader issue. This one data request could have a high impact across the company, and thus should be prioritized.

Conclusion

Using analytics to drive business decisions is a wise approach for any hotel company. In this blog, we outlined the four steps our experts provided to approach business questions effectively. Building and maintaining a strong analytics foundation is sure to set us up for success. Continued collaboration throughout the design process drives well-rounded solutions. We are confident the tips provided here can help hotel businesses achieve data-driven successes.

HEDNA Hotel Analytics Working Group
The Hotel Analytics Working Group raises awareness of the opportunities data analysis brings to optimize cost and conversion and thereby empower hoteliers to collect, store, analyze and action their data to make intelligent decisions about their distribution strategies. The group is currently Co-chaired by Matthew Goulden at OTA Insight, Connie Marianacci at Accor and Anisha Yadav at Revinate. Click here to find out more and how to join as a HEDNA member.