Bertil Angtorp, senior business analyst, and Mikael Krizmanic, senior engine diagnostic engineer, Volvo Car Corporation

Bertil Angtorp, left, is senior business analyst at Volvo Car Corporation, and Mikael Krizmanic is senior engine diagnostic engineer.


Case Study

Data in the driver’s seat

Volvo Car Corporation realizes core values through data-driven decision making.

Volvo Car Corporation’s core values have always shaped the form and function of its automobiles. Founders Assar Gabrielsson and Gustaf Larson set that expectation in 1927. “Cars are driven by people,” they often said. “Therefore, the guiding principle behind everything we make at Volvo is—and must remain—safety.”

The company has kept its founders’ promise, leading the automotive industry in safety engineering for nearly 80 years. Anti-lock braking systems, collapsible steering columns, front and rear crumple zones, energy-absorbing bumpers, side-impact protection systems, three-point seatbelts—all appeared first in Volvo cars. Over time, the brand identity has also encompassed quality, innovative design and environmental sustainability, each value making its mark.

Executive summary

The company: Headquartered in Gothenburg, Sweden, Volvo Car Corporation is the global automotive leader in safety and innovation. In 2009, it sold more than 334,800 automobiles.

The challenge: The company wanted to better understand mechanical performance of its vehicles under actual driving conditions. A legacy system, however, made it impossible for analysts to integrate diagnostic readout data with design and warranty information held in the existing data marts.

The solution: The automaker consolidated product design, warranty and diagnostic readout data into a data warehouse from Teradata, slashing response times from hours—and, in one case, weeks—to minutes and extending access to 300 users across four departments.

The results: Product design, manufacturing, quality assurance and warranty administration divisions share a single view of product data. Broad access to high-performance analytical processing is driving a company-wide shift to fact-based decision making.

Another defining Volvo characteristic is its systematic use of operating data from vehicles to improve the quality and performance of not only those in the field but also those in production and design. The automaker has long compiled warranty information as a design resource, and since the advent of onboard computers, it has collected vehicle diagnostic data as a window into performance under actual conditions. But by 2006, the volume and variety of that data was becoming unmanageable.

Building on a better platform We had a warranty data warehouse in one database,” recalls Bertil Angtorp, senior business analyst at Volvo, “and we were starting to build a new, separate data warehouse for the diagnostic readout information. Then we realized that what we really needed was to integrate those data sets. “We knew there was business value to be gained if we could eliminate manual integration, and if we could easily match warranty claims against diagnostic data from actual service records. We tried to bring the diagnostic data onto the existing warranty platform, but the performance was unsatisfactory. So we began building a business case for a platform upgrade.”

Performance was Volvo’s primary selection criterion, and it found what it needed in a Teradata platform.

Figure: The Volvo Car Data Warehouse—Phase 1

Click to enlarge

“The analyses we do are very CPU-demanding, so that’s our number-one issue,” Angtorp says. In September 2006, Volvo began migrating its data to a new 2-node Teradata Active Enterprise Data Warehouse that went live in July 2007. (See figure.)

The data warehouse integrates information from four primary sources: a system for managing vehicle and hardware specifications, one for managing on-board software specifications, the system that collects vehicle diagnostic data from service centers worldwide, and the warranty administration system. Data access and analysis are enabled through a variety of standard reports and ad hoc analytics, implemented using SAP BusinessObjects and developed in-house using Microsoft’s Windows .NET environment.

Consolidating multiple data marts on a single platform produced impressive results. The new environment immediately increased the raw data available to Volvo analysts from 364GB to 1.7TB and dramatically improved query response times:

  • A daily fleet mileage calculation that had taken two hours in the previous environment now ran in five minutes.
  • A comprehensive report of diagnostic failure codes by model per year was reduced from two weeks to 15 minutes.

Where performance constraints had restricted access to a handful of users, the new platform extended access to approximately 300 in product design, manufacturing, quality assurance and warranty administration.

"The Volvo Data Warehousing Program, started in 2006, supports our goals by providing a common view of all quality and warranty related information across Volvo Cars. By ensuring that all the detailed data is available to be analyzed, the use of a data warehouse within Volvo Cars supports a fact-based decisions environment."

Volvo was also the first company to implement an Early Warning Engine leveraging Teradata’s in-database analytics—which delivers failure predictions for every diagnostic trouble code, component, system and vehicle variation in the market. This enables prioritization and focus of issue resolution based on automated modeling of failure rates and reliability. Setting priorities on future failures—together with their fast identification on safety and performance-critical vehicle features—enables the automaker to act fast on emerging issues with a holistic data-driven approach.

Business value delivered

Beyond the improvements in query performance and user access, the new data warehouse immediately began returning significant new business value through a variety of process improvements, including enhanced warranty reimbursement accuracy, increased engineering efficiency, and cross-functional quality analysis. A pre-project impact analysis performed using data samples from one quarter revealed a 115% time-adjusted return rate (TARR). At the launch of Phase 1, 12 months of real historical data loaded in the data warehouse showed a TARR of 135%. One year after launch, a new verification was made, which had a TARR that was significantly higher than 135%. The analytical applications producing this new business value owe their effectiveness to their ability to access product design and warranty information together with field performance data created by Volvo cars in normal operation, throughout their service lives. This diagnostic readout data is created by on-board sensors and control processors, and downloaded by technicians during service and repair work. The detailed data is then available in the data warehouse.

Initially, this information consisted largely of the diagnostic trouble codes generated when sensors in the vehicle detect—correctly or incorrectly—a system failure or wear condition requiring service attention. For drivers, these instances result in a “check engine” dashboard light.

But a trouble code is simply binary data that provides limited insight into historical conditions in the systems that created it. So Volvo is increasingly equipping its vehicles with data loggers to capture the many variable measurements available in the outputs of industry-standard outsource components. Volvo has extended its systems to collect nearly 400 discrete measurements, which are read out along with trouble codes during service and loaded into the data warehouse.

By integrating these streams of design, warranty and life cycle operating data on a single analytical platform, the automaker is uniquely positioned to understand the relationships among engineering, performance and customer experience. Specifically, Volvo employees can:

  • Trace mechanical failures to their root causes
  • Understand failure rates over time, including predictive modeling of future trends
  • Correlate mechanical failures with geography-specific conditions and driving patterns
  • Prioritize, target and expedite problem response efforts
  • Identify and resolve design and manufacturing problems within the current production run

Because Volvo has detailed data on all of its cars—and it doesn’t have to be integrated manually—problems can be scoped faster and more accurately. And since functional teams work with the same data, they act as one. The importance of such capabilities to vehicle quality and safety is self-evident. But these proficiencies have also proven essential as the company pursues the core value of environmental sustainability.

Implementation lessons learned at Volvo

It’s all about:

Business value

  • Win and keep management support with a strong business case
  • Business value is always the highest priority
  • IT cost savings are a bonus


  • Find the people with strong statistical and mathematical skills
  • Insight into numbers leads to improvements
  • Involve the business at the pilot stage to create ownership


  • An enterprise data model based on detailed data saves time and supports the data warehouse
  • Save all data—new uses will arise
  • Plan for capacity—demand will grow


DRIVe for a greener future

Beginning in late 2008, Volvo has been rolling environmentally optimized configurations of its products. Dubbed the DRIVe line, these vehicles are designed to maximize fuel efficiency and minimize emissions. Their features include a lower chassis, aerodynamic underbody panels, an engine start-stop system to minimize idling, higher gear ratios, and specially selected wheels and tires.

All models emit less than the 120g/km limit of carbon dioxide that is the Eurozone threshold for special tax incentives. One vehicle, the C30, has tested as low as 99g/km. Volvo designers are using the data warehouse to collect and analyze diagnostic information from DRIVe cars in the field to track actual performance against design objectives.

“We want to make sure that our customers actually receive the fuel efficiency performance that we’ve certified,” says Mikael Krizmanic, senior engine diagnostic engineer. “For instance, we know when, in which situations, the start-stop system should engage and stop the engine. By collecting and analyzing the diagnostic data from DRIVe vehicles, we can verify that vehicles perform as designed or as intended.

“And in any cases where we don’t reach that target, we can look at the variable data to understand why. We also have instrumentation on board to calculate average fuel consumption, so we collect that data at each service interval. And we can see the average fuel efficiency performance across the model year fleet.” The data warehouse is also providing critical design support for future environmental optimizations. For example, charging data from the electrical system is being studied to develop an algorithm for balanced use of engine braking to recharge the battery without overcharging.

Behind the solution:
Volvo Car Corporation

Database: Teradata 12

Platform: 2-node Teradata Active Enterprise Data Warehouse

Data model: Physical—3rd Normal Form

DBAs: 2

Operating system: Unix MP-RAS

Storage: 1.7TB

Teradata utilities: Teradata Tools & Utilities 8.1

Tools/applications: Teradata’s Enterprise Data Warehouse Roadmap and products from IBM, Microsoft, Oracle and SAP BusinessObjects

Data-driven decisions

For all of its impact on vehicle design and problem resolution rates, Volvo’s data warehouse may have had its greatest impact on the company’s decision-making processes.

“Our decision making has become more fact-based,” Angtorp says. “Now, whenever a question arises, people invariably ask, ‘What is the data telling us?’ We test our assumptions against the data before we act.

“Once we’ve verified the existence of a problem, we use the data to determine the scope, to prioritize and scale our response,” he adds. “It helps us make sure that we’re focusing on the things that are most likely to affect the customer experience.”

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