According to McKinsey’s Global Industry Digitization Index, the U.S. and Europe have captured only a small percentage of digitization potential at 18% and 17% respectively.
That means there’s still time for leaders from traditional industries to modernize their aged data warehouses in becoming more data-driven. However, they face a serious threat of extinction from data-hungry disruptors born in the 21st century that base their decision-making on cloud-optimized analytical platforms with machine learning capabilities at scale.
The Birth of the Disruptive Data Unicorns
In just the past 10 to 20 years, completely new industries and market segments were forged, such as adtech, fintech, online gaming, e-commerce, social media, and IoT. This is just the beginning. The success of the respective market leaders in these industries share one critical trait – their quick ability to derive actionable insight from mind-blowing volumes of data used for competitive differentiation.
Online gaming leader Zynga collects and analyzes user data from its games to deliver personalized gaming experiences. Simultaneously, the insights realized from this data reduces customer churn and attracts more and more deep-pocketed advertisers. Analytics is core to Zynga, so much so that Ken Rudin, its former VP of Analytics, referred to the online gaming pioneer as “an analytics company masquerading as a games company.”
Perhaps one of the most disruptive companies of this century is rideshare service Uber. Founded less than 10 years ago, Uber has already delivered more than five billion trips at the touch of a screen, and now even offers food delivery service options. To continue delivering competitive rates, the company employs thousands of data analysts and scientists that sift through petabytes of data and use geospatial analytics to map supply and demand across the world.
So how are leaders in more traditional industries like banking, telecommunications, and retail fending off these upstarts? One way is through mergers & acquisitions (M&A).
If You Can’t Beat ‘Em, Acquire ‘Em
Monsanto, now part of Bayer, entered the agricultural industry in the 1940s, introducing insecticides and eventually seed and fertilizer for farmers. The agricultural giant understood even then the importance of smart agriculture (or agritech) for future growth.
In agritech, Monsanto saw massive opportunity in selling data and services to the farmers who purchase its products. So, in 2013, Monsanto acquired Climate Corporation for nearly a billion dollars to optimize their data analytics capabilities. Initially an underwriter of weather insurance for farmers, Climate Corporation acclimated into the modern data environment and built the Climate FieldView data analytics platform, which collects, stores and visualizes field data from sensors in the soil, weather data, and more. Monsanto’s investment in Climate Corporation and its analytics platform enabled Monsanto to help farmers become more data-driven, ultimately maximizing yield and profitability on every acre of their farms.
Is M&A the only hope for traditional companies in established industries to anticipate and capitalize on the future using data? Of course not. There are plenty of examples of industry leaders who zeroed in on established analytics use cases with a practical approach to integrate data into their organization. However, focus is key.
Focus on Analytical Use Cases that Impact the Business
Ameripride, recently acquired by Aramark, has been in business for well over 100 years and is recognized as a leader in uniform, linen and facility services. After adopting a high performance analytics platform integrated with a popular data visualization tool, the company pursued a pragmatic approach to data analytics, attempting to answer common questions that would help improve the business bottom line. For example, why do customers stop using their services? How did they become customers? How are product categories trending?
With many use cases to choose from, including analyzing vehicle telemetry data from its fleet of vehicles for route optimization, the team at Ameripride chose to focus on direct business impact, starting with rolling out its data analytics platform to direct sales. As a result, the company quickly identified ill-informed pricing on contract renewals with customers. Armed with this insight, Ameripride made adjustments and improved contract renewals by nearly 20 percent. Since integrating data-driven insights into the company, Ameripride has been recognized with multiple awards for its analytics program and associated execution.
It’s no mystery that data opens many new doors for both the disruptive data unicorns of the 21st century and traditional companies seeking to optimize business benefits. Due to the value of sensor data, the Internet of Things (IOT) and Industrial Internet of Things (IIOT) offer great promise to organizations seeking to establish entirely new markets or enable greater, more beneficial differentiation. Only time will tell if established market leaders can adapt to the modern data environment using purpose-built, cloud-optimized data analytics platforms, equipped with machine learning, or be rendered irrelevant by the more agile data-driven upstarts.
About the Author