Big Data Is A Game Changer

I spoke recently at the Gaming Technology Conference in Las Vegas, and instead of sharing my slides with the many folks who asked for them, I thought I’d just share my slides and messages with everyone via a blog.

Big Data Changes Your Analysis Processes

As I’ve said before, I’m continually amazed at how many folks are still struggling to understand the differences between business intelligence (BI) and advanced analytics. To coin a phrase from Chris Surdak[1], who was the very insightful co-presenter on my panel, many organizations think that “Big BI” is the same as advanced analytics, and are satisfied to keep moving along their current analytics path.

First, you need to understand that “Big BI” is not “Big Data.” Let’s look at Figure 1 to see how they’re different.

Figure 1: Differences between BI and data science

Probably the most important thing that differentiates BI and data science is that BI is focused on retrospective and descriptive analysis:  understanding and reporting on what happened in the past. However, data science helps you understand why things happened in the past and leverage that information to predict what is likely to happen in the future. I talked about the differences in some detail in my Business Analytics: Moving from Descriptive to Predictive Analytics blog.

To quote Chris:

“One other element of the descriptive vs. predictive discussion worth noting is the value of context. Descriptive analytics are very focused upon questions of ‘what’ rather than of ‘why.’ Answers to ‘what’ are readily found in structured data, and that’s what most organizations are comfortable with. 

“However, richness and context are found mostly in unstructured data. This context helps to answer questions of ‘why’ which leads directly to predictive analysis. If I understand ‘why,’ then I have real power. Hence, a huge step in moving from descriptive analytics and towards predictive is to start analyzing unstructured data in conjunction with structured and to start asking ‘why’ things happened.”

Changing the Game and the User Experience with Big Data

Organizations like casinos need to constantly look for ways to exploit new sources of customer and gaming data, coupled with advanced data management and deep analytics, to uncover new metrics that are better predictors of gaming and casino performance, and member behaviors.

Don’t be satisfied that your current metrics—the same ones that every other casino is using—are the best you can do. Find those metrics that position you to exploit the art of gaining an “unfair” advantage. (This reference is for those of you that have read the book “Moneyball: The Art of Winning an Unfair Game”, which I will discuss in more detail in a follow-up blog).

Casino owners have to be salivating over all the new ways that they can capture and display additional data and insight into their casino visitors. Technologies like Google Glass and heads-up displays (see Figure 2) can provide member insight to front-line employees at the point of member engagement. That can lead to a more personalized, differentiated casino experience. Come on, everyone wants to be treated like they are special, and having someone on the floor of the casino know my favorite drink and have that drink in hand when I walk over to my favorite machine or table would be very impressive.

Figure 2: Heads-up displays can power customer intimacy

But we don’t necessarily need these futurist technologies to arm our front-line employees today with the insight and recommendations that they need to improve business performance and member satisfaction. Vast new sources of data on our customers—their behaviors, tendencies, propensities, interests and associations—can be mined to uncover new insight that can be used to drive in-casino, in-flight performance.

Casinos could leverage the move to the “third platform” to deliver more actionable insight and recommendations to front-line decision makers at the point of customer engagement. This would not only help the casinos improve the customer experience, but help them spot potential fraudulent or illegal activities, or even identify casino guests who having higher than normal losses (see Figure 3).

Figure 3: Actionable casino dashboard

As an example, the mobile app shown above could help the casino front-line employees with the following types of information and recommendations:

• Highlight potential casino areas where machine or table payouts are outside normal ranges and match to known illegal activities. For example a “James Cagney” (a gang of players having unusual success as a group, though not as individuals) happening in Area (1) and a “Chuck Norris” (lone wolf having extraordinary success) happening in Area (2).
• Identify a player who is having lots of success legally and may be someone that the casino offers a comp room in order to get them to stay an extra day and give the casino a chance to win some of that money back. For example, a “Where’s Waldo” in Area (3).
• Highlight a high-value player who is having an unusual amount of bad luck that might require an intervention to prevent further, more permanently damaging losses. For example, a “Rodney Dangerfield” in Area (4).

The same actionable dashboard could deliver recommendations about:  machines that might be in need of repair, replacing a dealer who is having an especially bad day, or shutting down a part of the casino due to lack of traffic. All of these recommendations focus on both improving the customer experience while working to increase casino performance and profitability.

Big Data Transforms Business

Your Big Data journey starts by understanding your organization’s key business initiatives. Remember, organizations don’t need a Big Data strategy; they need a business strategy that incorporates Big Data. Consequently, you need a guided process that allows you to apply the 4 big data value drivers (access to all your operational data, access to internal and external unstructured data, real-time data access and analysis, and integrated predictive analytics or machine learning to uncover insights buried in your data) against your key business initiative in order to move from Monitoring to Optimization to Metamorphosis (see Figure 4).

Figure 4: Big Data business model maturity index

You’ll know what you are on the right path when the organization starts to develop an insatiable appetite for more and more data, such as:

• Recording the age, make, and model of the casino members’ cars during valet service to see if they have upgraded (or downgraded) their cars.
• Asking one more question during hotel check-in to capture what brings them to the casino and finding out if this is any sort of special occasion such as a birthday, anniversary, or wedding party.
• Asking them at checkout if they need a ride to the airport and what airline they are flying.

There are so many opportunities to engage with members to capture just one more thing about them and their interests, passions, affiliations and associations. From these little nuggets of data (“non-big data”?) come the information, that when combined with other sources of customer behavioral data and business performance data, can lead to a more engaging customer experience and to more profitable operations.