The study exposed extreme polarization in the intensity of capture and use of data. For example, while some companies like GE were forging ahead with heightened attention to quality at 400 of their factories – GE refers to these factories as “brilliant factories” – with one battery plant capturing 10,000 variables, some as frequently as every 250 milliseconds, only twothirds of the study respondents said that they were capturing sensor generated data (see Figure 2: Manifold Data Sources). The study observes that despite decades of quality improvement programs after World War II, tens of thousands of factories in North America and Europe are light years removed from advanced, cutting edge digital processes to ensure product quality.
Bigger Drivers of Machine Data on their way
Examine Figure 2 in the light of the Internet of Things – where billions of devices will be connected with products having to work quite differently – and you can see the pre-eminent role of data and analytics in quality management. We are transitioning into a world where intelligent machines will be used a lot more to manage life, and humans will work in tandem with them distributing work based on strengths.
Telematics in vehicles, smart grids, connected wearable medical devices etc. are already creating an overwhelming amount of data. We now need a model to manage big data – or, put another way – we need models that can translate big data into meaningful and useful insights.
In effect, there are two main challenges to be overcome:
1. To have a strategy to extract signals from the data, and then separate noise from the valuable signals that contain insights.
The questions to ask here are:
- Should all the data be brought to a central repository or do we apply smart filters at the point of capture, at the very edge of the network, to keep data transmission costs to an absolute minimum and to avoid over sensing?
- Have we defined the key metrics up-front so that we know what data elements to capture and aggregate/transform to the right level of granularity?
- Do we have a reliable data discovery platform that can analyse data in motion? Can it issue alerts about real-time threats to an individual device, as well as provide early detection of emerging patterns of problems across a segment of the installed base?
2. To arrive at a data structure that can overcome the challenge.
The questions to ask here are:
- Does the ontology and taxonomy of the data allow us to create quick linkages?
- Can the structure leverage all data formats – both structured and unstructured?
- Is the data architecture future-proof? Can it assimilate newer formats in the future as device usage changes?
The Road Ahead
Today, manufacturing is no longer de-coupled from its customer. There is just one degree of separation between the two. Soon enough the manufacturing industry will be leveraging the Internet of Things to collect product and usage data directly and regularly rather than depend on dealers and surveyors to collect and send in the data after a machine or device has broken down. This will give them visibility to product and customer issues in near real time and enable them to fix such problems remotely in a semi-automated fashion, even before customers come to know about their malfunctioning machines.
A powerful example of this is Vehicle Telematics. In-vehicle systems are capable of exporting vehicle performance, location, drive conditions and usage values to a central server, analyse the data and alert the owner about driving behaviour improvements, suggest fuel optimization strategies, service needs, when to expect failure, where to find the nearest dealer, and how to avoid the disruption. By ensuring that the right spares and skills are available at the right time, data can also help the dealer in reducing service time.
Vehicle Telematics is not only helping automobile manufacturers and dealers in getting closer to their customers but is also creating whole new products and services ranging from the Autonomous (driver-less) vehicles such as the Google Car to new services such as Uber and Lyft that offer low cost rides to people who do not own cars. It is further helping communities with creative solutions to address traffic congestion and pollution thereby helping move more cars per hour without having to incur large amounts of capital in widening roads or building new ones.
The focus of business is shifting. From being a one-time sales transaction with customers, businesses now want to have a lasting customer relationship. Manufacturing organizations that bring this focus to their data strategy are already headed for earning more impactful customer experiences that translate into profitable long-term growth. This is indeed validated by the EIU survey results which indicate that the most data-adept companies are also the most profitable.