Big Data is defined as high-volume, high-velocity and/or high-variety information assets that enable enhanced insight, decision making, and process automation. In other words, big data is the massive influx of information that can be acquired by a variety of systems. The amount of data stored by industries is now doubling at a staggering rate of once every 1.2 years. Big Data in and of itself has little value until it is analyzed and put to work. It can be thought of as the raw ingredients that are fed into business intelligence and business analytics. Before diving into the deep waters of business data, intelligence and analytics, a couple of definitions are helpful.
Business Intelligence (BI) and Business Analytics
These terms are closely related and are sometimes used interchangeably. They are also defined differently in different industries and there is often some overlap. With that said, here goes:
Business Intelligence (BI) is a means of aggregating data via reporting and using it to make shorter term business decisions. Supply chain management is a good example of BI being put to use in close to real time. Business intelligence is about making decisions on events that have recently occurred or are occurring in near real time. BI can also benefit from data derived from analytics.
Business Analytics tends to be more future focused and relies more on predictive analysis based on data that has accumulated. It is helpful for longer term decisions and spotting and predicting future trends. As an example, data derived from consumer behavior can be used by business analysts to predict future buying preferences.
This is a simplified view of a complex subject, but for our purposes, it should suffice. As different tools and data models continue to emerge, it is likely that these definitions will change and probably merge into a single “umbrella” term.
Sources of Big Data
Large volumes of data can flow into an OEM from a myriad of sources. Below are a few examples. We have omitted vehicle sales as most OEM’s have heavily (too heavily?) focused on vehicle sale data acquisition. Instead, let’s look at some other places where big data and its analysis can have big impacts on profits.
1) Supply Chain
Visibility into the supply chain is crucial to any automakers success. Supply chain management is all about supply and demand. Overproducing parts and components leads to bloated inventories and increased costs. Underproduction leads to decreased customer satisfaction (poor parts availability) and loss of revenue in the crucial after-sales market. The key to proper supply chain management is end to end visibility via BI. The data flowing from the production side (supply) has improved significantly of late. Much of that can be attributed to the massive supply chain interruptions caused by the 2011 Japanese earthquake and tsunami. OEMs learned how vulnerable their supply chains were and took steps to strengthen them.
However, the consumptive (demand side) is heavily dependent on timely data flowing upstream from retail locations and is a continuing “blind spot” due to fractured retail integrations.
How important is this? The cost to OEMs of supply chain inefficiencies can decrease shareholder value by as much as 1 billion dollars annually (pg. 15).
2) Consumer Behavior
The largest growth in Big Data is occurring at the consumer level. Digitally monitoring and analyzing consumer behavior had its big start in online advertising. With the explosive growth of ecommerce and social media, online advertisers and retailers quickly realized that they were sitting on a treasure trove of customer behavior and buying habits and then used this to vastly improve the advertising and buying experience. Big Data and analytics spread quickly into retail sales and now reaches into virtually all aspects of consumer interactions. Highly targeted digital ads that seem to know exactly what to offer you and when to offer it is a perfect example of big data at work. So is ordering movies online and having relevant suggestions made for you.
For the OEM’s, that have millions of prospective car buyers interacting with their retail locations every month, capturing and leveraging the data from these interactions is the next frontier. Right now much of the data collection and analytics is focused on successful sales. However, knowing why a prospective customer chose not to purchase is often more valuable than knowing why they did purchase. Applying analytics to this data could be used to improve marketing, product selection and future product development.
This would include (but not be limited to) parts used in warranty and customer repair, OTC sales, and accessories. This segment contributes heavily to a OEMs profitability. After-sales drive a disproportionate amount of profit relative to revenue. In Germany, for example, the after-sales business generates more than half of German OEM profits while accounting for only 23% of revenues (including passenger cars, SUVs and light commercial vehicles). Other car makers also generate as much as 50% of their profits from after-sales.
After-sales has become a fiercely competitive market due to new regulations favoring non-OEM repair shops and the growth of “boutique” brand repair companies. After-sales is intimately related to both supply chain management (supply) and consumer behavior (demand).
For BI and analytics to provide meaningful answers, large volumes of complete, quality data are required. Since much of this flows upward from the retail locations, sound data collection must begin there. This requires that all software systems that collect data from the consumer must work together to provide a seamless and accurate stream of data back to the OEM. As dealers add more sophisticated CRM, marketing, and customer retention programs, the quality and quantity and granularity of available data is expanding. This does not take into account the burgeoning field of connected vehicles.
Retail integration has long been seen by many OEMs as a “necessary evil” fraught with high IT staff burdens, escalating costs and protracted project timelines. New technologies are making these issues a thing of the past.
Some forward looking OEMs are now seeing retail integration as a golden opportunity. With a single objective, better data via integrated systems, they can:
- Streamline their business processes
- Anticipate the wants and desires of the prospective car buyer
With a little luck, you may one day be able to walk into your local dealership without an appointment and find the car of your dreams, parked out front, ready to go, and pre-programmed to your favorite music. All thanks to Big Data.