Hundreds of analytics professionals descended on Disney’s Contemporary Resort for the 2016 Disney Data & Analytics Conference on August 31 – September 1. There were people from all aspects of research & analytics, and the conference featured a mix of topics, from attribution modeling and forecasting methodologies to broader business and management strategies.
The culture at Disney is clearly about the customer experience, which made it the ideal environment to sharpen both our internal and external customer focus. Here are some of our takeaways from this year’s DDAC.
People don’t want to be managed, they want to be led
The first keynote speaker, Robert Herjavec, presented ideas from two angles. As the leader of one of North America’s fastest growing technology companies, he understands the value of data and the people that make his business successful. “You can’t be successful in data unless you love it,” he said. Also, as managers, you have to find ways to inspire and motivate people.
Courtesy of http://www.disneydataconference.com/home.html
There’s no such thing as a better mousetrap unless you tell someone
You probably also know Herjavec from ABC’s Shark Tank, where he and the other sharks invest their own money in entrepreneurs. When discussing what makes a good Shark Tank pitch, he emphasized that if you can’t sell yourself, you will suffer. If no one knows how good you are at what you do, the world will not beat a path to your door. One of Herjavec’s most profitable ventures, Tipsy Elves, is testament to the practice of investing in people, not just ideas. The lawyer and dentist who gave up their careers to make ugly Christmas sweaters are on track to do $17 million this year!
Data-driven enterprises are constantly evolving
Teradata Labs’ Oliver Ratzesberger discussed the idea of a Sentient Enterprise, bringing a company together to act as a single organism and making data-driven decisions in real time. This is difficult to achieve, and doing it to scale is even more challenging. Ratzesberger said up to 75% of an organization’s time will be spent dealing with data instead of gaining new understanding. This is an incredible amount of time wasted on data discrepancies.
Consumers crave real-time interaction, relying on real-time data for travel, finance, news, and even their Disney Parks and Resorts experience. The My Disney Experience app, Magic Band, and website are just a few of the ways Disney is leveraging data and analytics to improve the customer experience. To do this as a Sentient Enterprise, Teradata identified five stages of organizational maturity, similar to our own digital analytics growth model. The movement to agility can take companies years; this goes all the way to the CEO and board at successful companies.
1. Data Agility
The agile data platform moves central data warehouse structures to a balanced, decentralized framework built for agility, such as sandboxes where organizations are experimenting with new ways to manage data. Here is where a minimum viable product becomes something that can be applied to data.
2. Behavioral Analytics
Organizations at this stage shift to an understanding that the data platform value comes from behaviors rather than transactions. This stage is all about behavioral thinking. Disney represents a best practice in this area. Visitors can book reservations, buy passes, update their profiles, and pre-book attractions in the Disney app before they even arrive. Then when they enter the park, the immersion and personalized experience, along with real-time information, offers guide and delight. A delighted guest then shares the magic with friends and family and books her next trip.
3. Collaboration Ideation
Companies need internal collaboration platforms, like a LinkedIn for analytics, that can tell people what kind of data is out there and allow them to interact with the data. Concepts of crowdsourcing apply here, where sharing and liking ideas help them to scale. Social interactions connect the data within the enterprise.
4. Analytics Application Platform
Insights are worthless unless they can be acted upon, so this stage empowers users by turning the analytics ecosystem into a self-service application. This helps even more people within an organization use, understand, and act on data.
5. Autonomous Decisioning
With 90% of decision-makers’ time being spent digging through mountains of data, they have little time left to make decisions and act on them. This final stage is where companies apply algorithms and predictive technologies to guide decisions without searching through data.
Activate the psychology of inspiration
IBM’s Watson subject matter expert, Rob High, helped explain the benefits of cognitive computing. Cognitive computing is helping to extend the expertise and capabilities of humans to help make what we do more creative and insightful. We are moving out of the programmable and tabulating systems eras. Cognitive-based systems build knowledge and learn, understand natural language, and reason and interact more naturally with human beings than traditional programmable systems. Pioneering organizations across industries are already leveraging Watson’s capabilities to realize significant business value and help solve some of society’s greatest challenges. This ranges from micro segmentation in medical treatment that captures clinical expertise to Under Armour evaluating users’ fitness information to artificial intelligence in Hilton’s concierge robot, which uses body gestures to resolve ambiguity. Ever been told to tone check an email? IBM’s tone analyzer understands and fine tunes your message using psycholinguistic analysis. Emotional analysis helps build empathetic systems; we can even use machine learning models and feature engineering techniques to predict emotional labels. The company’s 53-point analysis of personality can be run by something you write, better so than personality tests.
Don’t be sad Keanu
Jeffrey Ma presented ideas that can be applied to business and to life. If you’ve seen the movie 21, you know Ma is the card-counting phenom that inspired the movie about the MIT card-counting team and the author of The House Advantage: Playing the Odds to Win Big in Business. There are biases that cause us to make bad decisions, such as omission bias, and the fallacy of the gut decision can cause you to lose sight of the right decision vs. the right outcome. This is a lesson I should definitely learn to apply in life; the decision is actually independent of the outcome, and we should learn to separate the decision from the outcome.
Courtesy of http://www.disneydataconference.com/home.html
Other life lessons blackjack taught Ma include trusting your team, another simple but important lesson for managers at all levels. If you don’t let them do their jobs, then you can’t scale. Intrinsic motivation is also important. You can be motivated by knowing you’re getting better at something. Competition can be aligned with incentives to make everyone on the team perform better. Communication and transparency are key to creating a collaborative environment.
Embrace failure. As someone who generally likes to avoid conflict and risk, Ma’s presentation resonated quite a bit. He urged us not to fall for group-think; don’t agree with the group if the data tells you otherwise, even if your position is unpopular. You have to take risks to grow. Loss aversion is impacted more by a loss than a win, so you must understand the downside and upside and be able to quantify the risk.
By trying new things, overcoming cognitive bias, and inspiring collaboration, you too can create a data-driven culture. Were you at the DDAC this year? Share your takeaways with us on LinkedIn, Facebook, and Twitter.