Point of View

Big data a big boon for equipment financiers

But industry leaders must strategize and act fast

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The equipment leasing and financing industry hasn’t yet benefited from big data on a major scale. But this is changing as industry leaders learn lessons from similar industries and come to understand its potential.

The Equipment Leasing and Financing Foundation (ELFF) commissioned Genpact to carry out a forward-looking study on the promise of big data for the industry. Big Data: A Study for the Equipment Finance Industry draws on Genpact’s experience as a global leader both in big data analytics and business process and technology management services.

This article provides additional insight into the research findings and explores the value of analytics as a business process that supports equipment finance operations.

Big data’s role in equipment leasing and finance

The equipment leasing and finance industry emerged from the economic downturn with renewed vigor only to face another challenge from intensified competition. Firms are under pressure to cut costs at the same time that customers are increasingly cautious.

Leading companies are meeting these challenges. They’re doing so with ever-more sophisticated systems for understanding market trends, achieving customer insights, and building operational agility. Yet while they often recognize that they must operate smarter and faster, they find doing so hard to accomplish. The reason: efficient decision-making requires consolidating and analyzing mountains of business information—big data.

The fact is, integrating so much disparate information from multiple sources—in real time—is overwhelming traditional database technologies. Big data tools solve this problem with advanced software running on massively parallel computing systems. These are typically “commodity
clusters” comprised of existing low-cost systems. The challenge for every industry is developing a practical plan to take advantage of the wealth of data now available. 

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Cues from the banks

The nuanced analysis of millions of terabytes of data is only possible because of advances in data management. Today we can discern clear and actionable patterns from what was, until recently, information overload. The banking and financial services industry in particular has been a leader
in the use of big data analytics, especially in customer engagement and risk management.

Banks are using analytics for:

  • Fraud detection: Predictive analytics uncover unusual spending behavior, so banks can quickly contact customers when warning signs appear
  • Risk management: Integrated customer data from new sources broadens the scope of traditional credit ratings 
  • Personalization: Big data analytics compensate for less personal interaction over online channels by creating 360-degree customer views and targeting customized product offerings
  • Predicting customer behavior: Advanced analytics reveal customer needs to foster upselling and crossselling opportunities
  •  Emotional connections: Sentiment analysis can capture customer feedback through social media and other channels so banks can react quickly to resolve complaints and design loyalty programs

The payoff for equipment leasing firms

Big data computing has great potential for the equipment leasing business today. In the longer run, too, analytics will transform the industry. As the ELFF study puts it: “The use of big data by equipment leasing and finance firms may result in a more comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, and employees.”

Applying predictive analytics—the engine of big data—is limited only by a firm’s capacity for innovation. When leaders support decision making with actionable insights drawn from real-time data, equipment leasing and finance companies win: They achieve more responsive deal structuring, more robust risk assessment, and an improved customer experience—all of which produce a healthier bottom line.

Big data analytics helps decision-making in several ways. It creates transparency by analyzing and delivering all relevant information over networks for superior visibility into the business. It allows for real-time data assessment from multiple sources, revealing previously unseen patterns. Advanced analytics can even build models to test hypotheses and simulate business models and strategies. It can point the way to better products and services—and even entirely new ones.

Best practices for getting started

The ELFF study looked at different markets to reveal best practices guiding big data initiatives. The key takeaway from the research: It’s now clear that we can “industrialize” analytics as a business process and embed it at scale within the enterprise. To take advantage of this development, we must build and assess scenarios for its implementation using an end-to-end, enterprise-wide approach. The goal is to create a flexible framework that can accommodate a firm’s evolving objectives over the long run. At the same time, the data-to-insight-to-action process must be scalable and cost effective.

The following best practices will also make a difference to your business: 

  • Engage stakeholders for each business objective, to break silos and get a broad perspective. Consider organizing stakeholders into brand teams who want to understand customers better.
  •  Consolidate existing data elements, then integrate new  information sources, recognizing implementation must adapt to new realities.
  •  Identify every data type and source that can inform business decisions while exploring opportunities for crosslinks and aggregation strategies at the same time.
  • Plan infrastructure investments carefully, by exploring different options. For example, do you want off-the-shelf big data platforms or an in-house infrastructure, such as a commodity cluster.

The C-suite is key to success. Leadership must cut across silos and bring together the right combination of stakeholders, data analysts, and information specialists. That way, the project will stay on track to meet its goals. 

To make the most of big data, strategize

Big data analytics is fast becoming a mainstream management tool and intelligent enterprises are searching for new ways to generate business impact. Since big data technologies reached critical mass around 2011, they have been evolving quickly and gaining traction. The ELFF study in 2014 found that, globally, 64% of companies have already invested in some aspect of big data or say that they plan to invest by 2015.

Leasing and financing firms that want to separate themselves from the pack must be among them. These companies stand to benefit in a big way, if they approach analytics strategically. That means investing in tools that align with existing business objectives and long-term goals. Prioritize challenges and systematically apply proven techniques of big data—starting, of course, with the applications that offer the biggest payoffs. But also focus on an operating model that can effectively embed analytics into the fabric of business operations. Analytics is not a task: It is a process supporting others.