Question

CASE STUDY: DATA WAREHOUSING AT VOLVO Every Volvo car has hundreds of microprocessors and sensors. Their data is used in diagnosis and repair. It is also captured by Volvo, integrated with the company's CRM, dealership and product data stores, and stored for analysis. This enables Volvo to spot design and construction flaws early, enable proactive correction of faults, and see how its cars respond in accidents. Volvo sells about 400,000 cars per year. Each generates 100–150 kB of data per year. That works out to about 50 GB of data per year from one year's cars. Volvo has collected this data since 1999. It now collects about 500 GB of car data per year. That figure increases each year, as older Volvos are scrapped and newer ones, with more electronics, replace them. And cars are far from Volvo's only data source. Volvo stores all this data in a data warehouse from Teradata. It went live in July 2007, with a size of 1.7 TB at the time. A daily mileage calculation that previously took two hours now runs in 5 minutes. A report of diagnostic codes by model and year went from two weeks to 15 minutes. Where the previous system could barely process one query per hour, the new platform completed one a minute. This enabled Volvo to extend usage from a handful of users to over 300. What are some business results of this system? ? Volvo analysts can predict failure rates over time. Each month, they look at how many cars have reached a certain service age and how many of those have experienced specific failures. “This gives us a cumulative hazard function that tells us how many cars in a given population have experienced a particular failure, and how many are at risk,” explains senior engine diagnostic engineer Mikael Krizmanic (Big Data Insight Group, 2012). “It … describes the failure rate over time, and that's what we use for predictive modeling. It helps us understand which faults will produce large warranty impacts if not addressed systematically.” ? Cars in urban China experience different driving conditions and behaviors than in rural Germany: different average vehicle speed, engine load, operating temperature, environmental conditions, time at idle… “Because we have both error codes and operational log data in the warehouse, we can understand the relationships between geography, patterns of use and mechanical failure,” Krizmanic points out (Big Data Insight Group, 2012). “A problem may be a high priority … in one geography but not in another.” “I would say that today we have only scratched the surface. I don't think we understand yet, from a business point of view, this tool's true potential,” says Åke Bengtsson, vice president of quality and customer satisfaction. “I believe that we can better use data to provide early indications. … We must be able to act quickly, to reduce the number of steps to an accurate, proactive response. Every car we produce with a fault costs the company money. And every minute, hour, and day by which we can expedite a solution saves money for the company. The earlier we can resolve an issue the better it is for the customer and the company. So I think our direction is clear” (Big Data Insight Group, 2012). The Teradata system has enthusiastic support from Volvo's IT side. Jonas Rönnkvist, head of enterprise architecture, helped orchestrate its acceptance in the corporate environment. “Every new development project at Volvo Cars now follows a standard governance process, including reviews by my team. If the requirements include data consolidation and integration, and the design doesn't leverage the Volvo Data Warehouse platform, the project will not be able to proceed without CIO approval” (Big Data Insight Group, 2012). Volvo's data warehouse may have had its greatest impact on decision-making processes. “Our decision making has become more fact-based,” says senior business analyst Bertil Angtorp (Big Data Insight Group, 2012). “Now, whenever a question arises, people ask ‘what is the data telling us?’ Once we've verified the existence of a problem we use the data to … to prioritize and scale our response. It helps us … [focus] on the things that are most likely to affect the customer experience.”

Questions

1. Does Volvo have “big data” here? Why or why not?

2. Volvo Cars has gone through several changes of ownership. It was part of AB Volvo until 1999, was owned by Ford Motor Company from then to 2010, and is now owned by Geely Automotive of China. How do you think this might have affected the development, use and acceptance of this data warehouse?

3. Volvo Cars uses dendrograms to analyze clusters of alarm data, finding relationships among different signals. If you're not familiar with dendrograms (most people aren't), research them to understand what they are and what they show. How could an instructor teaching a course in Microsoft Office use dendrograms to analyze problem areas for students?

Answer 1

Part 1

Volvo has large volume of mostly unstructured data which can be analysed to produce information on the issues based on geography and the type of driving. This would help in determining what are the main causes of failure with particular set of conditions. As this large volume of data can be specifically used for analysis and further take decisions based on the analytical output. for eg if or Germany, failure rate is due to high speed, there will be design changes done to overcome that failure.

Part 2

As Volvo has changed ownership over the years, target customers and strategic decisions have also changed accordingly. with changing senario big data being added in the system will have to be analysed differently to enable executives to make a decision. Also with increasing customer base, and more data is required for quality analysis, they might have to change the data warehouse size and speed.

Part 3

Dendograms are tree diagrams which represent heirarchical clustering of similar classes and relationship among the categories. With dendograms distinct relationship between different units can be identified by grouping them into smaller and smaller unit. for example an enginer overheating reasons can be listed down and can be clustered under various branches, like lubrication oil, radiator, driving conditions. based on the data available, root cause at a certain location can be identified and resolved.