RESEARCH & RESOURCES

How NCR Corporation Improved Quality and Time to Market for Advanced Analytics

By Audrey Ng, Senior Director, Strategic Alliances, Hortonworks
and Cesar Rojas, Product Marketing Director, Teradata


As NCR says, its customers “turn the gears of the global economy.” They include thousands of banks and retailers that depend upon tens of thousands of NCR self-service kiosks, point-of-sale (POS) terminals, and ATMs and barcode scanners, among other equipment.

“Our customers—such as banks and retailers—can’t have downtime, so it’s our job to actively support that requirement,” said Brian Valeyko, NCR’s director of enterprise data and business intelligence.

That downtime is increasingly rare, thanks largely to how NCR uses big data. The company has the capacity to gather and store real-time performance data not just from a sampling of devices, but from every one of millions of deployed devices. NCR can store data securely in a Hortonworks Data Platform–powered “data lake.” From that data, NCR can create rich predictive models that prevent downtime, using analytics capabilities built on a Teradata Unified Data Architecture (UDA) platform.

Expanded Collection of Data and the Role of Hadoop

“We’re using Hadoop as the landing zone for our machine data,” said Valeyko, “and as we acquire new devices into the portfolio of equipment that we support, we’re going to continue to expand the amount of machine-learning information that’s coming off those. I think Hadoop has a big impact on our ability to scale; the only way to effectively store that volume of data is in a commodity Hadoop environment.”

Before Hadoop, NCR could collect only snapshot data, which limited the accuracy of its predictive models. For example, the company might collect snapshots from a sampling of ATMs (perhaps once-daily performance data). With an enterprise-grade data lake built on Hadoop, NCR can monitor every ATM it has manufactured and build predictive models based on data from 100 percent of those ATMs.

But how does the company make sense of it all? NCR uses the Teradata Enterprise Data Warehouse for its day-to-day analysis and the Teradata Aster Discovery Platform deep analytics capabilities to move that data around effectively in the architecture. With Teradata Aster, NCR can query a truly global data set. All of NCR’s ATMs, POS terminals, and self-checkout devices collect and transmit “heartbeat data” about the condition of the device, whether components are operating properly, and generally how the network is performing.

“When we marry that data in Teradata Aster with help-desk data from service technician call logs, we can enhance the predictive capabilities of those systems,” Valeyko said. “That’s how we use Teradata Aster, for discovery of algorithms that we use in our predictive operational systems.”

NCR can sample data for analytics and insight, and it can also use an entire set of data, which includes historic data and new data types, thanks to Hadoop.

Expanded Data Processing and Analysis Capabilities

“Hadoop is a way for us to efficiently and effectively store those large amounts of data, even data we’re not sure yet how we’ll use,” Valeyko said.

Collecting and querying a vast amount of data creates new capabilities. “Right now we’re using Hadoop to store our telephony data—that’s a new usage we’ve found,” he said, “and we’re using that data to analyze our use of telepresence devices worldwide. When we’re doing communications between offices, we’re trying to figure out the most efficient path and determine where to put telephony equipment in our network.”

That’s a purely operational use of data, one that NCR customers won’t see. Valeyko’s group fosters “organizational altruism,” sharing information across multiple lines of business.

“An analyst responsible for financial products might never have looked at retail service calls,” he said. “Using big data, we’re able to expand the scope of a query and root cause analysis to see how a collocated printer might be shared between a point-of-sale device, an ATM, and a self-checkout device,” with implications for retail as well as financial services. This is the benefit of being able to leverage all the data in one place, for analytics across lines of business, and sharing analytical insights across the business as well.

The data is easy to find and move. “As we find pieces of that lake that we want to analyze, we can quickly move the data using Unity Data Mover [another Teradata product] into Teradata Aster for analysis and then use that to build an algorithm, for example, and put that into another operational system,” Valeyko said.

Accelerated Time to Market

NCR continually feeds machine data back into its processes, which enables continuous engineering and improvement.

“Before Teradata Aster, it took six months to create, test, and release an algorithm that enabled predictive replacement of a component,” Valeyko said. “Now we have an effective algorithm in production in less than three weeks.”

As Valeyko described it, the global scale of its data set allows NCR to “pick off the pieces of that large stream for further analysis within Aster.

“We can quickly update the business rules in those operational systems to ensure that we have the right device, the right component, even the right technician on the right truck, to get to the customer as quickly as possible,” he said.

NCR can perform enhanced scheduling of support for those devices, matching components and technicians to specific needs. Finally, leveraging all the data, NCR can identify a much higher percentage of actual faults than it had in the past and reengineer its processes to predict those faults and preempt them.

The result, said Valeyko, is that “our customers know that their customers have a great experience with our devices.”

Data curation and analytics have a direct, almost immediate impact upon NCR device performance and customer experience. But that all begins with the ability to collect, store, and mine that data cost-effectively.

Lessons

IT can provide tremendous benefit across an organization leveraging storage and processing in the Hortonworks data platform for Apache Hadoop, heavy analytics from Teradata Aster, and a Teradata Enterprise Data Warehouse. As NCR discovered, some of those benefits and uses are obvious—such as predictive modeling—but others are not so obvious, such as analysis of its telepresence devices and their use.

However, said Valeyko, “an organization may need to be sold on the enterprisewide need. It can be difficult to figure out how to fund a discovery platform because it supports the entire organization.”

NCR funded its initial investment in its discovery platform with sponsorship from its financial and services organizations, but now enjoys enterprisewide support. At first NCR’s data scientists were the most enthusiastic, but as business users began to see the utility of the Teradata UDA—for example, in process reengineering, freight optimization, and saving money on telephony—they became interested as well. NCR formed a data governance council with executive-level representatives from across its lines of business, including engineering and operations. Customers, too, expect the deep discovery capabilities that UDA enables through the integration of the enterprise data warehouse with Hadoop.

“What it comes down to is being able to look at your customers individually and collectively so that you can effectively give them the best service you can,” Valeyko said.

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