It’s been a decade since “connected” objects—commonly referred to as “the internet of things” (IoT)— reached broad audiences. Connected toothbrushes, sensors embedded in sneakers, and smart watches have started to change consumer behavior through a data-driven, gamified approach. Technology has rapidly evolved to handle large data volumes at high velocities and big data analytics. AI has become more democratized. 5G adoption supports streaming and micro batches. And manufacturers start to monetize connected products. 

The topline impact

The ubiquitousness of connected devices and sensors represents a massive opportunity for manufacturers to generate new revenue streams, capture market share, and fuel growth. Sensor data analytics in the cloud allow manufacturers to differentiate, foster loyalty, and offer value for customers along the B2B and B2C chain. 

• Automotive vehicles conveniently navigate to the next parking spot, gas station, or charging point. They support energy-conscious driving while providing entertainment and safety features. In-car apps augment the driving experience and increase brand loyalty, impacting future revenue—even if the apps are free. Other data services are paid add-ons and result in margins that far outperform those from core vehicle production. Related to car configuration and usage, OEMs may flash software over the air or plan spare parts delivery just in time for an upcoming  inspection at a repair shop. Thus, leading players are keen to address issues like data privacy, sovereignty, and security, instead of leaving the field to tech companies.
• Particularly for mobility services providers, anonymized telematics data is indispensable for effective fleet management. It helps balance regional peak demand with incentives or location-based pricing. It enables planning for seasonal upgrades, like snow gear, and negotiating advanced usage-based insurance contracts. Detailed vehicle lifecycle information results in accurate residual values for better remarketing when selling off used cars. In a competitive mobility market, connected vehicles have become a crucial lever.
• In the truck segment, data-driven services are enabling a successful transition to a new business model. With e-mobility,  owning commercial vehicles is less attractive. Soon, leasing and as-a-service models will prevail—along with autonomous vehicles at various levels. As a result, truck OEMs upsell services to product usage subscriptions. Top of mind are services that enable fleet management, boost utilization rates, facilitate handshakes with other modes of transport, or match arrival times at warehouses with time slots for offloading. Connected trucks provide data for modular service components that can be customized or sold as needed.
• Aftermarket revenues of machinery and equipment result in attractive margins for manufacturers over a product’s life cycle. Widespread planning of preventive maintenance and scheduled downtime become even more precise through machine learning. In addition, leading manufacturers offer data-driven automation solutions to help operators in their day-to-day job, such as calibrating machine settings to the overall line or cluster, thus optimizing end-to-end performance. Operators further receive alerts from evaluating in-line measurements to identify a drift in quality, saving costs related to rework, scrap, and direct or indirect material. Helping customers with their savings pays back handsomely. 
• More recently, some startups or segment-specific initiatives are working with connected product data to provide a group of similar customers with services and/or application logic in return for their data contribution. However, the products feeding into the data pool might not be from the same manufacturer. When competing vendors agree to share their products’ data in a collaborative approach, it is a must to protect IP by anonymizing data to guarantee secure access and data governance.

The bottom line impact

 Additional revenues are one side of the coin, while reducing cost (thus increasing profits) is the other. Product excellence efforts across all segments of manufacturing leverage sensor data for cost savings related to design for manufacturing and end-to-end quality management. Data from field tests bundled with digital testing reduces cycle times for product launches. Outsourced factories and feedback from technicians provide additional, external input from beyond their own four walls, as do install base sensors. Pattern recognition, root cause analysis, and predictions often involve R&D collaboration with suppliers to stop issues from occurring; the shorter the time from detection to correction, the better. 

Use cases manufacturers can enable with connected products in manufacturing industries are wide and nuanced. Total revenue streams are estimated to reach $1.52 trillion by 2030 with a year over year growth rate of almost 25% according to Precedence Research. To keep pace with competition and capture the vast potential of sensor data, manufacturers small and large are looking to modernize their legacy data management infrastructure and processes, as well as scale their data science teams.

That’s where manufacturers and Snowflake team up

Snowflake can save data analysts precious time, who can then reinvest it into more use cases that raise the bar for data-driven innovation. It’s possible to shorten data preparation cycle times for otherwise iterative and tedious steps, including data collection, visualization, and transformation. Snowflake customers benefit from the near-unlimited scale and efficiency of a multi-cluster, shared data architecture that allows manufacturers to scale at the pace their business requires. Because of Snowflake’s consumption-based pricing model, manufacturers only pay for the compute they actually use. 

When data analysts collaborate on connected product data with experts from other departments or outside their organizations, they can grant instant access to the Data Cloud via Snowflake permissions set at a fine granular level. This eliminates data silos or duplicates and replaces costly, risky, and sometimes cumbersome methods. Snowflake’s Clean Room functionality allows data to be shared without exposing personally identifiable information or other sensitive data, enabling companies to comply with various data privacy requirements. 

To learn more about ingesting data into Snowflake, see this previous blog. 

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