This post is the first in a series of three addressing strategic considerations for how to approach your organization’s Big Data infrastructure to create business value.
Information technology is revolutionizing products, and, in turn, revolutionizing how manufacturers use IT. Once composed solely of mechanical and electrical parts, products have become complex systems that combine hardware, sensors, microprocessors, software, and various forms of connectivity. These “smart, connected products” and the data they generate have unleashed a new era of innovation and opportunity.
Smart, connected products, emerging across all manufacturing sectors, enable a new set of capabilities that can be grouped into four categories: monitor, control, optimize, and automate. The ability to monitor—via sensors and other data sources—exposes the product’s condition, external environment, and operation. In effect, smart, connected products now have a digital voice and can exchange data with the manufacturer throughout the longest period of its lifecycle—the “use phase.”
This wealth of new data, which serves as the foundation for all other product capabilities, will bring about a new standard for managing the product and service lifecycle and the customer relationship. Just think about how valuable it would be for manufacturers to stay connected to the products they develop and service every day. Instead of asking customers about product performance, manufacturers would gather design and quality insight from the product directly.
Manufacturers could provide more efficient service by knowing something was about to break instead of waiting for customers to tell them it’s broken. Imagine the business growth manufacturers could drive if they knew how their product was being used, and were then able to deliver relevant and timely value-added services throughout the life of the product. On the furthest extreme, manufacturers might even provide their products entirely as a service, given the visibility and predictability provided by a smart, connected product.
The path to this value is through the aggregation and analysis of the following types of data:
- External Data: Third-party data from customers, partners, suppliers; the broader ecosystem, such as weather, commodity and energy prices; geomapping; and from news and social media sources, all of which informs product capabilities and analysis;
- Enterprise Data: Enterprise systems such as ERP, CRM, and PLM that provide data about customer preferences, sales and service history, and product details like engineering designs, warranty allowances, spare parts and inventory, suppliers, and costs;
- Smart, Connected Product Data: Data from product sensors, which provides insights about the product’s condition such as location, temperature, and component or part failure, and data from the product’s operation, including utilization, usage time and rate, and log files.
Smart, connected products change the Big Data equation for manufacturers. With product usage, condition, and performance data streaming from smart, connected products, data analysis—including enterprise and external data—can access a 360-degree view of the product and provide a richer perspective on customer needs and inform decision-making across the organization.
However, this opportunity is not without costs. Big Data is generally described as data sets whose large volume, wide variety, and high velocity make them impractical to process and analyze with traditional database technologies and related tools in a cost- or time-effective way. In order to extract the real value and promise of Big Data, practitioners must embrace an exploratory and experimental mindset regarding data and analytics.
This unique approach along with the global scale and distribution of smart, connected products changes both the infrastructure and technology choices manufacturers will be required to support.
For organizations with lightly-connected products—with low-bandwidth modem or 2G-cellular connections, or remote devices that are intermittently connected—the challenges can be more profound. While one approach may require transitioning products to a higher bandwidth solution to leverage this new technology infrastructure, another approach may require adapting analytics capabilities directly onto the product. Adding onboard analytics will create a different set of challenges and considerations for modeling and implementing analytics within the limited hardware and software capabilities of the product.
- Defining the Infrastructure for Big Data Analytics
- Big Data Choices and Tradeoffs
- Creating Business Value from Big Data