The Internet of Data, Not Things

The Internet of Things (IoT) has the potential to revolutionize many industries, but its full potential has yet to be realized. One reason is that the focus has been on the sensors rather than the data. IoT devices collect data from various sources, including machinery, customer behavior, and environmental conditions. This data can be used to improve efficiency, optimize processes, and make better decisions. However, collecting and processing this data can take time and effort. Businesses need to invest in specialized platforms and processes to make the most of IoT data. These platforms can collect, process, and analyze data in real-time, providing businesses with the insights they need to make better decisions.


Tangible benefits for real-time business objectives

Integrating and combining sensor data from multiple streams and sources multiplies that benefit through:

  • Optimized operations, system monitoring, and predictive maintenance. Sensors can help identify patterns, find anomalies, and suggest real-time changes that save money, prevent failure, and keep customers happy.
  • Personalized experiences and products. Customers who become more sophisticated and connected expect real-time, personalized products and marketing messages. Sensors offer a view into actual customer behavior by observing what people do in the real world, not relying on what people say they want.
  • Real-time decision-making. From getting a loan to a dinner reservation, users and businesses want to get and make decisions immediately based on the best available and freshest data. Real-time decisions that incorporate sensor data are more accurate and lower risk.

Extracting the full value of sensor data

  • Data collection. Sensors gather data from their environments generating raw data streams based on various parameters such as system health, temperature, pressure, or location. These raw streams contain raw data, which can be messy, including some duplicates, and overwrites, and each collection stream will have its own description of the data in the stream that computers and people need to analyze and move that data.
  • Data transmission. Significantly, IoT sensors rely on wireless protocols such as Bluetooth, Wi-Fi, or cellular networks to send data to a central system hosted by a company, to cloud data platforms like Snowflake or Databricks, or cloud platforms like AWS, Azure, or GCP. Wireless protocols for data transfer are more reliable and secure than ever but still suffer from drops and transmission gaps that can cause delays and inaccuracy as data is sent to its destination for processing.
  • Data ingestion. Central systems onboard the raw stream and prepare it for downstream consumption by people and other machines. Often overlooked, this process is another unique source of delay and possible inaccuracy as raw data moves through gateways, into central systems, and through baseline data quality checks.
  • Data processing and analysis.  Streaming sensor data finally flows to analysts who can extract insights in real-time using modern analytics platforms for streaming. Often, at this stage, the process can include integrating third-party or batch data. That can be challenging as each data source has different descriptions and schemas, making apples-to-apples analysis and integration hard.
  • Data Storage and visualization. Processed data is stored and available for data consumers across the enterprise. Dashboards and reporting tools deliver insights and allow users to monitor trends, fix issues and expand capabilities. These tools constantly change as users discover new data needs and the underlying data changes.

Pillars of IoT Analytics Platforms

IoT data has special requirements to ensure the data and resulting analytics are timely, trusted, and consistent.

  • Speed and performance –  Sensor data needs to be fast and trusted. Processing and delivering sensor data is unlike any other data analytics activity and has particular latency concerns. Distributed cloud computing, highly distributed data collection over wireless networks, and multiple hops inside the enterprise can delay data arrival and erode trust.
  • Interoperability –  The power of sensor data comes from combining streams and adding context. When all the streams and third-party contextual data have different data definitions and schemas, humans often have to hand-tune and connect the correct data for the complete analysis. Making it easy to add, combine and review many data sources is essential to using sensor data.
  • Scale –  Sensor processing platforms must address data scale at a level no other data function can imagine. The velocity and volume of data are often discussed, and nowhere is it more visible than in the arena of IoT. In addition to specialized hardware and cloud computing, sensor data networks require highly specialized architectural and software solutions to support real-time outcomes.
  • Flexibility – The often forgotten but essential player in the workflow from data collection to insight is the human and the user experience. Sensor data that is quickly moved, combined, and delivered will not have an impact without giving humans the dashboards and analytic tools they want and need to extract value.

Datorios has a Solution for Real-Time Sensor Data Processing

The shift to real-time data sources is across all domains and will grow even faster with the availability of Generative AI. That growing demand is amplified by the economic need for higher productivity and expense reduction. Datorios, a leading provider of real-time sensor data processing solutions, has announced the launch of its new solution for sensor events, transactions and IOT data.

The Datorios serverless-like solution is built on a cloud-native architecture designed to scale elastically with demand. Businesses can easily add or remove capacity without worrying about infrastructure management.

The solution also includes many features that make building and deploying real-time sensor data processing applications easy. Buyers and implementers should consider these features as they evaluate Datorios:

  • Combination of declarative code and visual layers both for the pipeline specifications and the data itself.
  • Built-in streaming engine that can process data from millions of devices in real-time.
  • Unique interactions between the pipeline logic and actual real-time events for the acceleration of development and debugging.
  • Comprehensive set of analytics and visualization tools that can be used to gain insights from sensor data.

The Datorios serverless solution helps businesses process massive volumes of sensor data in real time. The solution includes a comprehensive set of features that make it ideal for a wide range of applications, with sensor data as the key to the first use cases.

Business Benefits of the Datorios Serverless-like Solution

  • Time to market – By changing the way code interacts with data in real-time, Datorios can significantly accelerate the development cycle for data and change the typical delivery time from weeks to hours.
  • Free up the experts, save time on DevOps – Customers report and detailed case studies document up to 70% less time spent on integration and testing code reviews and documentation. That is a plus for data experts and modelers who want to spend most of their time on module development.
  • Save money with elastic scale – The solution is designed to scale elastically with demand, so businesses can easily add or remove capacity as needed. This can help businesses save money on infrastructure costs.
  • Performance delivers the speed customers expect – The solution is designed to process massive volumes of data in real-time. This can help businesses gain insights from data faster.
  • Trust and security – The solution is designed to be secure, so businesses can be confident that their data is safe.
  • Cost reduction through saved people time and architectural design – Datorios architecture is designed to reduce costs through pre-processing of high data capacity functions, moving data quality resolution closer to the source of any issues and compute optimization in a specialized event-based architecture.

There are numerous challenges encountered in processing sensor data. From grappling with the sheer volume and variety of data to ensuring its accuracy and reliability, these hurdles demand innovative solutions. These solutions must address the complexities of real-time data streaming, the necessity of robust data storage and management systems, and the intricacies of integrating diverse sensor networks.

As industries increasingly rely on sensor-generated data, it is imperative to devise comprehensive strategies and leverage cutting-edge technologies to surmount these challenges. The Datorios solution is one to consider for the scalability, ease of use, performance, and security sensor data demands. By doing so, companies can unleash the full potential of sensor data and pave the way for transformative advancements in various fields.

Full disclosure: Datorios is a sponsor of Software Engineering Daily.



Jocelyn Houle

Tech founder, investor and product manager focused on data, AI and infrastructure. Jocelyn focuses on data and AI for financial institutions. Follow her on LinkedIn.

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