Internet of Things (IoT) is the digital backbone of physical industry, connecting products, machines, devices, and people and becoming the bedrock for game-changing innovations. The secular trends of Autonomy, Connectivity, Electrification,and Sharing (ACES) are creating massive volumes of IoT data, growing at exponential rates. Gartner, IDC and leading analysts all agree, IoT data in manufacturing is growing at an incredible rate.In Automotive, software defined vehicles can generate up to 30 TB data per vehicle per day. In Aviation, next-generation connected aircrafts generate 30X more data than legacy platforms. In Industrial Manufacturing, analysts estimate 200-500% growth in data volumes in the next 5 years.
Operationalizing these datasets will enable companies to maximize industrial productivity by monitoring the health of their assets and processes in real-time, drive stickier customer experiences by gaining predictability into key events that matter, and unlock SaaS-like (or service-driven) business models that are linked to customer use and value (eg., power by the hour) The economic upside of getting this right runs into hundreds of billions of dollars every year.
Yet, today’s approaches to creating value from these high-value signals remain shrouded in mysterious acronyms and unnecessary complexity. IoT data is siloed and duplicated to other data stores, resulting in higher operating costs. There is a complex decision tree of how to best process data at varying speeds and sizes, often requiring teams to build a new tech stack by stitching together many services tailored to individual use case.These teams face lengthy development cycles and data being locked to multiple proprietary solutions that require specialized skills that are hard to learn and find. Managing security and governance of datasets is a bigger burden, requiring extra tooling and integrations. These factors make it more challenging to bring analytics and AI to the right place and at the right time – contrary to the business objective of such investments.
The result? Slower progress in demonstrating business impact and an unsustainable trajectory for data-driven innovation across the enterprise. The root cause for this: the current approach does not answer the most important question: now that you have connectivity to all this data, what’s the most effective strategy to democratize and monetize this valuable data at scale?
With the Databricks Lakehouse, companies can:
Power all use cases with a simple, flexible architecture
Databricks Lakehouse gives companies the ability to ingest and process IoT data near real-time or in scheduled batches, powering the broadest set of use cases and make efficient optimizations for cost and speed, based on business SLAs.
Efficiently scale unit cost of data processing over time, as data volumes grow
IoT datasets are massive, noisy and complex. The Databricks Lakehouse enables a wide range of time series data processing tasks and offers a faster path to executing the most common data engineering tasks in the most computationally efficient manner.
Reduce dependence on specialized tooling and skill sets
IoT data needs to be combined and contextualized with other data sources in order to power more actionable insights. Databricks enables companies to drive consistency in how these data products are curated and eliminate the cost of “extra”. Data teams are able to use the tools and languages that they know best and right for the job.
Power real-time applications and low latency insights at the edge
Mission-critical applications require instant calculations and predictions. This requires the ability to process data as it arrives and make insights available in near real-time dashboards, alerts and notifications. For decisions made at the edge where always-on cloud connectivity is not an absolute certainty, Databricks enables companies to consistently train AI models in the cloud and then serve those models to edge infrastructure and devices for inference.
The benefit? Lower costs, faster innovation and establishing a trajectory of data-driven innovation that fosters new competitive advantages. This is why industry leaders John Deere, Rivian, Collins Aerospace, Honeywell, Mercedes Benz, GE Healthcare, and Halliburton choose Databricks to monetize IoT data.
Enhancing IoT Capabilities with New Partners in the Databricks Ecosystem
Databricks is thrilled to announce strategic partnerships to deliver specialized expertise and unparalleled value to the industry. These partnerships allow companies to simplify access to complex datasets, generate actionable insights and accelerate the time to value with the Lakehouse platform.
Seeq, a global leader in advanced analytics for the process manufacturing industries, delivers self-service, enterprise SaaS solutions to accelerate critical insights and action from historically unused data. Oil and gas, pharmaceutical, specialty chemical, utility, renewable energy, and numerous other vertical industries rely on Seeq to optimize business and production outcomes, including yield, margins, quality, and sustainability.
- Sight Machine
Sight Machine enables real-time data-driven operations for manufacturers to achieve breakthrough performance by continuously improving profitability, productivity, and sustainability. Purpose-built for manufacturing, Sight Machine’s Manufacturing Data Platform is an open, cloud based solution that creates a common data foundation and utilizes AI/ML to power real-time visibility and decision making. Through a data-first approach, Sight Machine captures, contextualizes and analyzes data from the entire factory to deliver a systemwide view of the end-to-end manufacturing process, empowering all stakeholders — from executives to plant managers and operators — to drive and scale productivity improvements across the enterprise, supply chain and value chain.
Kobai delivers unparalleled semantic capabilities to unify operational and enterprise data and empowers all users to make better decisions and drive operational excellence. Their semantic graph technology is accessible to business users without writing a single line of code. The Saturn graph is embedded directly within the lakehouse, providing on-demand scale and performance. Their Studio and Tower products offer a next-generation unified platform for building Connected Digital Threads integrating, modeling and visualizing data and AI at an enterprise scale. Kobai helps semantically organize your data and prepare it to make your Generative AI solutions more validated, accurate and explainable for every response.
Companies across the Fortune 500 leverage Plotly’s powerful interactive analytics and visualization tools to build and scale production-grade data apps quickly and easily. Their flagship product, Dash Enterprise, is used by Databricks customers worldwide. In the manufacturing, transportation, and logistics sectors, the two solutions are used together for use cases like predictive maintenance, performance optimization, and system monitoring to enable sophisticated at-scale industrial IoT streaming analytics workflows.
In addition to these new partners, Databricks has a vast array of technology partners to support your end-to-end architecture for IoT Data.
You might be still early in your journey with IoT, but the destination is clear – Databricks Lakehouse. The lakehouse platform allows teams to simplify the data architecture, efficiently scale unit cost of data processing, and power all use cases, truly allowing customers to accelerate data-driven innovation and capture the immense value potential in IoT data.
Learn more about Databricks Lakehouse for Manufacturing and solution accelerators explicitly designed for manufacturing customers, including: