Co-authored by Hanna McCall
Your Casino’s Invaluable Renewable Resource
Similar to how a player can choose the value (e.g., bet amount) of each game on a slot machine or at a table game, your casino has a choice over how much value is extracted from its data. Neither the game nor the database has explicit value, but both have an opportunity to provide significant return. Your casino, however, can decide how much value the business can draw from its database, depending on how data-empowered organizational members are. Furthermore, not only is your data an invaluable resource, it’s renewable too.
Strides Towards Data Empowerment with the Data Lake
Many organizations struggle to detect patterns and behaviors of interest because of the challenge of managing the complexity of casino data involving silos of data with many differences, including multiple transactional systems, different API data interfaces, varying types of data, multitudinous outside sources of data, and a vast array of formats of data. These silos make it difficult to empower the decision-makers throughout the enterprise because team members have to ask the data analytics teams to extract and cleanse data they are looking for, possibly waiting hours or days for the analyst to return their request. Meanwhile, the data analyst writes complex one-off ad hoc queries with seemingly infinitely long ways of joining data to overcome the challenge of working with multiple diverse data sources and tables in the database.
Overcoming complex siloed data can be solved by putting it in a data lake to bring relevance to the high level of variety. Data lakes allow operators to be agile because new data can be added any time, and your data analysts can query against the many types of data sources and tables without grappling with eight-way cross system joins or scripts to pull data from proprietary API data sources. The data lake will then eliminate the wait time of weeks or months to acquire a new data set and deploy it after remodeling, reconfiguring, and redesigning the enterprise data store. With data lakes, you don’t need a data model or schema before the team can start asking questions. Instead, operators ask questions on day zero, on demand, and analysts can do ad hoc queries on demand.
The Why Question: Enterprise-Wide Data-Empowered Leadership
Is your organization focused on assessing historical analytics to answer questions like, “How did we perform last quarter?” In today’s dynamic industry, historical performance is an extremely poor indicator of success; quite simply, the market fluctuations are so massive that historical performance numbers examined in the absence of market information are misleading. Consider analysis that shows performance for the business is up 10% since the prior quarter. On its own, it’s a beautiful result; however, if the market is up 15%, the results aren’t so stellar.
The next step is to conduct empirical analysis to answer why your business is up or down. This “why” question should bring in data that relates the activities of the business to the activities from diverse sets of data sources. Suppose your data lake includes inflation figures, competitive market numbers, and overall economic indicators, leading you to build an organization that consumes data from diverse data sources.
Rather than using data to answer questions already given, data literate operators ask the right questions about data. Finding questions in data means doing cognitive analytics, asking questions like, “Why are we seeing this trend?” or “Why is this segment’s purchasing patterns changing?” Consuming and overseeing all diverse data sources and perspectives leads to a data-empowered team making the right action right now, in the right context.
Solid analytical organizations don’t just analyze static data, they monitor the data streaming to detect people, behaviors, and events of interest, enabling operators to create personal guest experiences and make actionable, data-driven decisions in real time. This doesn’t mean all leadership can query the data themselves, but they should be able to get consumable data efficiently to begin to collaborate with teams including multiple diverse talents and perspectives – this data consumption process achieves data empowerment in your broader team.
Agile team analytics processes encourage teams to share data and collaborate, reuse data, and democratize the use of data, inspiring a culture of innovation and experimentation within operations. When data is monitored and data collection and analytics are continuously improved, teams can strategize around data patterns observing how guest behaviors and interactions are going, how well guest sentiment towards the brand is going, and how the business processes are working. That knowledge is fed back into the business through initiatives discovered in data. Collaboration within the knowledge-driven learning and continuous improvement system will bring new innovations and organizational optimization.
Conclusion
Today’s environment requires analysis of diverse real-time data to understand patterns and changes happening now in the context of market movement. Consider the explosion of gaming revenue that has followed since reopening after COVID-19 closures; this reopening bent forecast models and created enormous uncertainty around future performance. Achieving data empowerment across operations to make insights proactively and in real-time is necessary to get ahead of external factors influencing your market. Moreover, these collaborative agile analytics processes are the foundation of businesses seeking innovation and competitive advantages.
