If you simply collecting a lot of data, you do not have a big data use case. However you do have a big data use case, if you need to process and analyze the collected data in order to generate greater business value. A common example cited is that if you are storing millions of records from your customers personal info in a database that is not a big data analytics use case. However if you are collecting web logs from millions of hits to your site from online transactions, then that probably is.
For a long time, automotive industry was in the former category. Today that seems to be changing and much media attention has been focused on an area where big data and automobiles intersect: Vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication to improve safety and information. This is collectively referred to as machine to machine or M2M communication. Some studies seem to indicate that by 2022 “there will be 1.8 billion automotive M2M connections. This will comprise 700 million Connected Cars and 1.1 billion aftermarket devices for services such as navigation, usage-based insurance, stolen vehicle recovery (SVR) and infotainment“. The improvements in safety alone this will generate will be significant: from collision warnings to blind spot checks to left turn assisting. While this is all great, much of the infrastructure, policy regulation and standardization required to make these things a reality does not exist today. That is one of the big challenges.
The number of sensors constantly collecting data in a car has continually increased from a handful in the 1980s to more than 1000 today. These sensors are capable of gathering typical mechanical and electrical conditions data during the operation of a vehicle. But nothing serious was done with this data and thus the data was typically not stored. However automotive companies are now realizing that there is actual value in this data stream and are beginning to take notice. This can positively impact everything from maintenance and warranty costs to recall to consumer insurance costs. The emergence of big data technologies which enable cheap storage and affordable processing allow automotive companies to generate significant value from data they would normally ignore.
If we look at the three key data types that are driving adoption of big data analytics technologies such as Hadoop, they are (in order of decreasing usage)
However this is very soon going to be inverted. For example, GE pointed out that industrial data is growing at twice the rate of other types of data. For example, GE generates about 5 terabytes of data a day in its labs. To address this, GE is building big data software called the Historian that uses Hadoop to manage time-series data to help industrial customers track their rising industrial data. They further estimate that predictive maintenance and diagnostic applications alone can generate about $130 Billion in value. Coupled with impacting supply chain efficiency, this can be as high as $160B.
The point of this argument is that machine data is the low hanging fruit of big data analytics. The applications in manufacturing and automotive areas themselves will soon dwarf today’s dominant use cases. The key for manufacturers is to get on this value train early and not get left behind.
Originally posted on Tue, Sep 09, 2014 @ 08:35 AM