IoT
Saving time series data
Time series data is a sequence of data points collected over time. It's crucial in the IoT industry because it helps in understanding patterns and trends, predicting future outcomes, and improving decision-making. Efficient storage of time series data can greatly enhance retrieval and query speed, enabling faster insights. Our platform enables us to apply strategies, such as advanced compression algorithms and optimised database structures, for time series data to save storage space and enhance performance.
Data aggregation
Aggregating data involves combining it in a way that makes analysis more efficient. This involves summarising raw data into broader metrics (such as daily averages), or grouping data by categories or dimensions. This enables platform to save processing power, reduce data storage needs, and make it easier to get high-level insights from the data.
Data Ingestion
Data ingestion refers to the process of gathering and importing data from disparate sources, and moving it to a place where it can be stored and analysed. In the context of the industrial sector, these sources could be various types of IoT devices and sensors, legacy systems, and even manual input or logs.
With our platfrom, data is ingested in real-time (also known as streaming ingestion), which means as soon as the data is produced it's ingested. It can also be ingested in batches (batch ingestion), where data is collected over a period of time and then ingested, when there is no need for real-time data.
This data can include information like machine operating times, performance metrics, environmental conditions, material specifications, product quality data, and much more. The specific type of data collected depends on the goals of the data analysis and the nature of the industrial operations.
Data Consolidation
Data consolidation is the process of combining data from different sources into a single, unified view, often within a central data storage system like
a data warehouse. In industrial environments, this can be a challenging task due to the variety and volume of data.
Data can come from legacy systems, IoT devices, ERPs (Enterprise Resource Planning systems), MES (Manufacturing Execution Systems), and SCADA (Supervisory Control and Data Acquisition) systems. Each of these systems might store data in different formats or structures, making consolidation a complex process. However, it's a crucial step, as it allows for a comprehensive view of the operation.
A key part of data consolidation is ensuring data integrity and maintaining the accuracy and consistency of data. Measures like validation checks
and de-duplication may be used to ensure the quality of the consolidated data.
Data Transformation
Data transformation refers to the process of converting data from one format or structure into another, to allow for better data analysis and insights. This might involve cleaning data (removing errors, inconsistencies or irrelevant information), standardising formats, and enriching data by adding additional information or context.
In the industrial sector, this involves converting raw real-time data into meaningful metrics. For example, a temperature sensor might output data in raw form that is then transformed to categorise it into 'normal', 'warning', and 'critical' bands. Similarly, vibration data from a machine might be transformed into metrics about the machine's health, such as 'expected', 'maintenance required', or 'failure imminent'.
The transformed data is then loaded into platform, where it is used to generate reports, feed machine learning models, trigger alerts or support decision making and more.
Our Platform is designed to handle the scale and complexity of industrial data, automating much of the ingestion, consolidation and transformation process and making it easier for companies to become data-driven.
Data Sharing
Our Platform has a range of data sharing options, connectors, and features that enable data to flow seamlessly to and from the platform:
● Broad interoperability: Ability to interface and share data with a wide range of other systems, from SCADA to ERP systems, and from cloud-based applications to on-premises databases.
● Open APIs: Offering open APIs to allow third-party applications to fetch and interact with the data and services of your platform. This fosters a flexible ecosystem where your platform can be used in conjunction with other systems.
● Data exporting in various formats: Capability to export data in multiple formats like CSV, JSON, Excel, XML etc. This allows the data to be utilised by different types of software systems with different requirements.
● Plug-and-play integrations: Providing ready-to-use out of the box integrations with commonly used software tools and platforms in the industrial sector. This means customers don’t have to develop custom integration code.
● Customisable integration framework: Flexibility to build custom integrations as per the unique needs of different industries or clients.
● Real-time data sharing: Capability to share data in real-time with other systems, enabling instantaneous reactions and decision-making based on the latest information.
● Secure data sharing: Robust encryption and cybersecurity measures to ensure data is securely shared, maintaining data integrity and confidentiality.
● Two-way data sharing: Not just sending data to other systems, but also the ability to receive and process data from other systems.
● Semantic data interoperability: Ability to share not just raw data, but also metadata and context information, helping other systems to better understand and utilise the data.
● Cross-platform data synchronisation: Ensure that data updates are reflected across all connected systems, maintaining consistency.
● Scalability: The system's data sharing capabilities can scale up or down according to the needs of the industry or the client, whether it's a small business or a large enterprise.
All-in-One Data, AI + IoT Platform
With our platform, you get all your platform software needs addressed with one, simple to configure platform
● Flexible data ingestion and integrity monitoring: The platform supports many different data formats and device protocols, and allows you to easily group and manipulate it, before deploying logic to monitor data integrity.
● Drag-and-drop workflow builder: Easily create customised business logic for your specific solution requirements.
● Machine learning and predictive analytics: Create machine learning models using a drag-and-drop interface or import any Python-based model, train and test them with your data, then easily deploy them into your IoT workflow.
● Automation and control: Push data to any third-party system, including control systems for manual and automated control.
● Real-time dashboards, visualizations and insights: Configure customised dashboards that make sense of complex data and provide actionable insights and advanced analytics.
● Rules, alerts, events and triggers: Create rules for event monitoring, notifications, or push/pull to any third-party system via Modbus or our API builder, for example.
● Enterprise-grade security and back-ups: Leveraging the latest cloud-based capabilities, ensure your data is safe and ‘always available’.
● Device monitoring, management and maintenance: Monitor the health of any device and deploy OTA firmware or configuration updates.
● Automated reporting: Create custom dashboards and reports that can be emailed to your inbox as and when desired.
● White labelling: Brand your so that your users / customers experience a customised, consistent experience.