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Want More and Better Data? Build Trust

Date: Sep 21, 2020 @ 05:00 AM
Filed Under: Industry Trends

We kicked off the “The Optics of Data” series talking about how to get an organization ready to leverage the digital ecosystem and be better prepared for change. We explored how discovery culture, speed of learning and building agility are the keys to being “digitally prepared” and beating the competition to the digital high ground.

You may have noticed that this strategy propagates on one very important assumption: The organization already has access to the data it needs. No doubt your organization has data-driven operations, but are the quality and capabilities of those data streams enough to enable the discovery, learning and agility necessary to capture market share? Based on my experience, the answer is “No.” Organizations begin their digital journey with the data they have available. This makes sense because they understand that data, its source and its meaning.

But the problem is the data you have now was not gathered to answer the questions you will have tomorrow. Successful digital transformation requires not only organizational preparation, it also requires access to the right data at scale. You will need to do three things to get the right data flowing – embrace the insight real-time data provides, understand your customers’ needs for data and, most important, establish trust for access and scale.

In Three NOs, Dr. Timothy Chou recently described the problem industrial companies are having with digitization. He summarizes the state of industrial digitization with the non-existence of three critical components: relevant data streams, an understanding of what questions to ask and a supply of analytics applications that solve important problems. Chou’s analysis of the situation and his outline for moving forward are based in the fundamentals of design thinking – understand the end customer (“workers, not developers”) and build solutions that deliver value to them by solving their important problems.

Given the strong connection between industrial operations and equipment finance, i.e. the equipment, it is not surprising Chou’s “Three NOs” reflect the challenges of digitization in equipment finance. We discussed how to prepare an organization to engage challenges with digital tools. Now, we must follow Chou’s advice and build both the tools and the skills necessary to use them.

Timothy Chou’s Three “NOs” of Industrial digitization reflect similar challenges faced by Equipment Finance.

Equipment Finance Advisor Chart Industrial Compared to Equipment Finance

Today, equipment finance relies heavily on historical data – mostly past financial performance in the form of credit data. In comparison, our industrial operations cousins use historical data to create digital models for both operations and equipment but use real-time data to improve productivity. Today they are leaning into the IoT and AI to extend their digital skills with real-time data to better manage the unknows of the future. If equipment finance is going to similarly transform, it must acquire and leverage real-time operational data.

Equipment manufacturers and the IoT solution providers have made real-time data streams and applications available. But as Chou points out, those data streams are not yet proliferating, (i.e. not selling rapidly), because the IoT industry has focused on the technology, (i.e. developers and infrastructure), rather than users and the applications they need. The challenge for a lessor who wants to transform and engage real-time operational data is similar. For example, I have spoken to lessors who are buying and deploying IoT tracking devices. They are doing so primarily to serve their equipment recovery needs. But they have not considered that their customers may have similar data needs. As a result, their experience with IoT data does not scale and is expensive.

Usage-based Insurance Shows the Way

One of the best examples of engaging customers to get data to improve your own operations is the retail usage-based insurance (UBI) industry. Insurance, like any risk management, runs on data – customer use and incident experience data to project loss. Insurers always need more data on drivers, and they saw an exciting new source in the digitization of automobiles. But how could they get access to that data?

The automotive OEMs created the possibility of data streams from every driver and vehicle, but they struggled to get consumers to pay for the cellular plans required to stream the data to the cloud where they could leverage it. For example, in the first 10 years of GM’s OnStar availability, less than 1 percent of drivers subscribed. Automotive manufacturers focused on safety and reliability to motivate consumers. The problem is every driver thinks he/she is a safe driver and they expect their car to not break down. As a result, their risk perception did not justify the expense of the service – particularly when every driver also has a cell phone.

Enter UBI and “safe driving discounts.” The insurers learned from the OEM’s struggles and turned their attention to their customers. They converted the same “safety confidence” into a way to get drivers to want to connect their driving experience AND share their data with the insurers. Progressive SnapShots and Allstate Drivewise UBI devices enable almost 70 percent of customers to reduce their insurance costs by showing the insurer, in real-time, that their risk expense is lower. Drivers feel empowered and are rewarded for good behavior. Of course, in return the insurers now have massive data streams on driver behavior, richly enhanced/segmented by meta-data, to which they can ask an almost unlimited number of new questions that traditional accident data could not support. And the frosting on this cake is that “watched drivers” experience 20-30 percent fewer accidents than before they used UBI.UBI insurers had the same problem as the equipment finance industry in the table above – they needed more and better data to affect their transformation. They took three key steps to get the data they wanted.

Real-time Data Is the Right Data

First, embrace the power of real-time data. “Past performance does not guarantee future returns” seems to be a mantra often ignored by risk managers. UBI engaged the fact that real-time data analytics can predict what is about to happen. Real-time operational data enables one to better understand the use of equipment and its relationship to financial performance. The driving metaphor is “we manage risk by looking ahead through the windshield, not in the rearview mirror.” The beauty of today’s digital ecosystem is that we can look ahead with new data streams if we just consider the problems we wish we could solve.

Empathy for Customers

Second, if you want more and better data you have to create demand for that data with your customers. Learn and embrace the fundamentals of design thinking to understand the needs of your customers. Lessees are operators and have needs for better applications of operational data. The equipment OEMs and IoT solutions providers know this and are creating connected products and applications that solve operational problems with real-time data. Show those solutions to your lessees and they will pay for the data streams you desire in the same way customers ask for and plug in the SnapShots and Drivewise sensors. Empathy will align your data needs with those of your customers.

Trust Provides Access and Scale

Finally, and perhaps most important, is quality data at scale requires trust. Privacy and data ownership are always a concern for customers in any industry. Progressive and Allstate work continuously to establish trust so that their customers continue to find value sharing their driving data. Apple is leveraging its brand-trust to become one of the largest health data purveyors now collecting data from over 50 million Apple Watch “patients” every day. Every lessee will consider whether they trust you, their lessor, as a partner to have access to their operational data. GPS-based location data is obviously valuable to the equipment lessors for recovery purposes. But even if the lessee is willing to pay for GPS data, they may not share it if they believe it will be used counter to their business objectives. Follow the first phase of design thinking, use empathy to understand the needs of your customers, and then create trust helping them meet those needs. Then they will reward you with access to the data you desire.

Equipment finance providers looking to transform in today’s digital world are fortunate. Unlike our industrial cousins, the digital technology and data streams we need already exist. We don’t have to figure out how to add cellular modems to our products or invent new sensors to get the data we need to improve productivity. But we do have to embrace the real-time data customer operations generate and use that data to better understand their needs.

Our ace in the hole is the foundation of trust we have created with customers over time. Most of us don’t have individual brand trust of Apple, but as an industry we enjoy trust in the form customers sharing their valued identity information and engaging us to financially support the pursuit of their dreams. Digital transformation requires that we reward this trust by leveraging operational data to better understand and join them in that pursuit.

Readers are invited to read Part 1 of The Optics of Data series:

“Be Prepared” in a Digital World – How to Look Like You Already Knew This Was Coming!


Scott Nelson
President and Chief Technology Officer | Tamarack Technology
Scott Nelson is the President & Chief Technology Officer of Tamarack Technology. He is an expert in technology strategy and development including AI and automation as well as an industry expert in equipment finance. Nelson leads the company’s efforts to expand its impact on the industry through innovation using new technologies and digital transformation strategies. In his dual role at Tamarack, Nelson is responsible for the company’s vision and strategic planning as well as business operations across professional services and Tamarack’s suite of AI products. He has more than 30 years of strategic technology development, deployment, and design thinking experience working with both entrepreneurs and Fortune 500 companies.
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