A famed cruise ship corporation seeking a dramatic competitive edge after years of PR setbacks. A wildly popular entertainment streaming service in search of an innate connection to individual viewers. A major agricultural distributor facing a cadre of frustrated shrimp farmers.
These are just a few examples of businesses that in recent years have provided solid evidence that data analytics, combined with careful strategy, can make the seemingly impossible happen.
These scenarios diverge radically from organizations that have accumulated tons of data—often at great expense—only to relegate it to long unopened digital files. What does it mean? What is it telling them? How can it be used?
Carnival Corporation created a wearable digital concierge system to cater to every guest’s whims. Netflix discovered that working “thick data”—research based on ethnography and anthropology—into the analytics mix could pave the way for a profitable and game-changing pop-culture shift. And major agricultural distributor Cargill found a distinctly data-driven way to ensure the health of a vital commodity—its shrimp.
What are the elements to finding success with data analytics? The answers are emerging as reams of data pour in.
Synthesizing at Sea: Carnival Corporation’s Comeback
From the late 1990s to the early 2010s, Carnival’s fleet of ships suffered a series of public setbacks. But none of them topped what became known internationally as the “poop cruise” of 2013.
An engine fire left the Carnival ship Triumph stuck at sea for nearly a week, with thousands of passengers wading through sewage-flooded hallways, on a ship with no air conditioning or running water, surviving on strict rations of food and bottled water. The ship was towed into Mobile, Alabama. Customers disembarked, but media coverage had already spread rampantly.
This was just over a month after 30 passengers died off the coast of Italy when the line’s Costa Concordia ship struck a rock. Several on-board fires and passenger suicides had made the news in recent years.
When corporation CEO Arnold Donald came on board later in 2013, a turnaround began. The corporation desperately needed to reverse its dismal reputation. But competition, primarily from Norwegian Cruise Lines and Royal Caribbean, was also a growing challenge.
The latter 2010s have become “the golden age of cruising,” Donald says. The Cruise Line International Association reported last year that an estimated 30 million travelers would cruise in 2019, up from just 10 million in 2009.
This surge in business coincided with the rapid advance of technology, including streaming data analytics paired with the Internet of Things, or IoT.
While Royal Caribbean built new ships that would break records in terms of size, Carnival and its fleet would center on one key aspect—customer experience on a scale that would have been unimaginable a decade ago.
The personalized Medallion platform, implemented by tech firm Accenture on several of Carnival’s Princess lines, presents a whole new level of competition in an industry that demands never-ending re-branding, such as bow-to-stern refurbishments and renaming of ships, upgraded staterooms, and on-board recreation. The Medallion, a quarter-sized device guests wear on lanyards or clip to belts, is the latest and most dazzling.
Before embarking, each guest receives a Medallion embedded with a unique digital identity, including individual preferences about food and drinks, entertainment, health requirements, and a host of other data. The devices link to 7,000 onboard sensors throughout the ship. Paired with the IoT, streaming analytics—real-time tracking of data generated by guests—the system caters to guests’ whims in unexpected ways.
Passengers embark seamlessly. As they approach their staterooms, lights come on and air conditioners adjust to their preferred temperature. They can order anything—food, drinks, personal amenities—through the integrated app, from anywhere on the ship; a staffer, greeting them by name, arrives with the order within minutes.
Through the platform’s tracking service, guests can locate their children or group members throughout the 19-deck vessel, aided by phone GPS, Fifty-five-inch touchscreens in every public area let guests play interactive games, ask questions, observe what’s happening, and what’s coming up, on all levels of the ship.
The data collected in real time can be archived to predict the performance of amenities throughout the cruise and adjusted to present an even more immersive experience, helping to transform Carnival’s fleets into a major contender on the seas.
The Human Element: Netflix
After grappling with how to perfect its famous algorithm, which predicts viewers’ likelihood of watching a show or film within a tenth of a percentage point, execs decided the company needed a fresh perspective on what it brings to customers.
That’s when the service brought in cultural anthropologist Grant McCracken, who implemented a new concept at the company: “thick data.”
Thick data is based on the work of McCracken and others who gather their data through close study of cultural behavior. They seek out what consumers want, often in detail, then contrast and compare that to the findings of big data.
Spending a vast amount of TV time with Netflix users in the US and Canada, McCracken gathered ethnographic data on how viewing patterns change: Were viewers watching alone or with a group? Were they eating? How did they react to plot twists and spoilers? Did they make “fidelity pacts” with friends and loved ones, vowing to avoid watching a show until everyone else in the group had?
The data science team at Netflix scaled McCracken’s findings with its existing quantitative data. When an entire season of a show is released on Netflix on one day, throngs of people will remain glued to their televisions all night, if that’s what it takes.
The clever combination of big data and thick data helped Netflix create binge-watching, which made the company not just profitable, but also a cultural phenomenon.
The evolution has moved at warp speed.
In the late 2000s, ethnographer Tricia Wang, who runs the digital consulting firm Sudden Compass, was a China-based researcher at Nokia who spent her off time with low-income young people who, despite their lack of funds, were passionate about saving up for the newly released iPhone.
But when Wang excitedly told execs about this new craze, they were unfazed. The numbers told them they’d be fine. Smart phones were a trend, not a revolution. The company changed hands during the next decade, with negligible market presence. As late as 2017, Nokia entered the smartphone market.
“There are many reasons for Nokia’s downfall,” Wang wrote in a piece for Medium, “but one of the biggest reasons that I witnessed in person was that the company over-relied on numbers. They put a higher value on quantitative data, they didn’t know how to handle data that wasn’t easily measurable, and that didn’t show up in existing reports.”
Save the Shrimp: Cargill
The Minneapolis-based agriculture production and distribution company Cargill, the largest privately held company in the US, emphasizes sustainability in the methods and products it manages. But when mortality rates in its shrimp-farming niche began to rise, farmers needed to know why, and how to reverse the course.
Early mortality syndrome (EMS) is a bacteria-borne disease that has caused significant production losses across the world. But data showed that shrimp farms on the west coast of South America seemed to be immune to EMS.
To develop a new system, a team of Cargill engineers and executives traveled to Ecuador, where they observed firsthand how shrimp farmers successfully improved farm management and captured pond data to detect health and water quality issues, boosting their shrimp harvest’s immunity to EMS outbreaks.
The company’s animal nutrition enterprise began work on iQuatic, a mobile-data tracking app that can predict biomass in shrimp ponds using temperature, pH, nutrition, and other factors.
Along with those measures, iQuatic has an automated feeding system using acoustic technology to determine natural shrimp feeding patterns.
Tiffany Snyder, CIO of Cargill’s Animal Nutrition Enterprise, told CIO.com that bringing the app to light required more than a team of data analysts.
“We made the farmer part of our team,” Snyder said.
Using the app, the shrimp farmers were able to save data to the cloud and access a live operations dashboard that visualizes the performance level of their ponds. This information has become critical in helping the farmers increase their yields. Previously, Snyder said, the farmers had relied on their own observations, which were at best murky.
Bringing It All Together
A merging of minds, diverse skillsets, and data is on the rise, with more companies knocking down the walls that previously separated IT experts who gather and handle the data from the employees it could ultimately benefit.
“It’s very important to include not only people with analytical skills, but also those with business and relationship skills who can help frame the question in the first place and then communicate the results effectively at the end of the analysis,” says Tom Davenport, a senior advisor at Deloitte Analytics and author of “Competing on Analytics: The New Science of Winning,” in an interview with insiderpro.com.
In 2016, Jaguar Land Rover began offering classes in analytics after discovering pockets of self-service analytics activity—in which employees perform queries and generate reports on their own—were already happening in several departments. The free training, aimed at empowering staff with new data capabilities, was a hit. The first course, set for 60 seats, was filled. By the next year, the Harvard Business Review reported, between 1,800 and 2,500 employees were able to create their own analytics every day.
Nike has created software tools for data analytics geared to business users who might otherwise face technical barriers. Sainsbury’s, a British supermarket chain, acquired a 60-member team of data specialists—the Humanalysts—to spread the word throughout the company about the value and opportunity that analytics presents. HBR reported the oddly named team did its job. Even the most hardened skeptics became enthusiastic about what data can do.
As Wang puts it, companies need to involve a mix of what she calls data-nerds and people-nerds.
“Those things are interchangeable, because data is people. People are data,” Wang tells the online platform dscout. “Data and people are inextricably linked. To decouple them is a massive mistake.”
