Building a Robust Analytics & AI Platform in IoT

As IoT ecosystems expand, effective analytics and AI capabilities are key to realizing the full value of connected devices and data. While many organizations are eager to dive straight into machine learning, setting up a foundational analytics platform first is essential to ensure data is accurate, accessible, and actionable. This “crawl-walk-run” approach supports immediate insights and scales with future AI demands, preparing organizations to leverage increasingly advanced analytics as their IoT deployments mature.

Let’s explore how organizations can begin their journey with a strong analytics and visualization platform and then evolve it into a robust AI platform, allowing them to unlock the potential of machine learning and intelligent automation.

The Analytics & Visualization Platform: Starting Simple to Build Real Value

Before embarking on the complex journey of AI, an analytics and visualization platform is a valuable first step. This platform serves as a centralized system for collecting, organizing, and visualizing data, creating a structure for efficient data analysis. At this stage, organizations focus on descriptive and diagnostic analytics—the initial levels in an IoT analytics journey—to answer foundational questions: What happened? Why did it happen?

Setting up a descriptive and diagnostic analytics platform allows businesses to:

• Centralize Data: With data from connected devices consolidated in one platform, teams can identify trends and anomalies at a glance. This setup provides transparency across operations, simplifying decision-making at all levels.
• Visualize Trends: Diagnostic tools and visualizations allow companies to quickly analyze data patterns, root causes, and potential issues, laying the groundwork for predictive analytics down the road.
• Scale Easily with Infrastructure-as-Code (IaC): By using IaC principles, organizations can deploy and replicate their analytics platform in minutes, ensuring the system is easily scaled or re-architected when needed.

For organizations just starting in IoT, an analytics platform generates immediate value without requiring a full AI infrastructure. Descriptive and diagnostic insights offer actionable feedback, allowing companies to identify operational improvements before investing in predictive or prescriptive models.

Transitioning to an AI Platform: Enabling Machine Learning & Real-Time Decision Making

Once a solid analytics foundation is in place and data is continuously streaming, the natural progression is to develop an AI platform. This shift enables organizations to add machine learning models to their IoT stack, building intelligence that anticipates and automates responses based on historical data and trends.

Moving from analytics to AI involves establishing an infrastructure to support machine learning operations (MLOps). Key components of a robust AI platform include:

• Data Collection and Curation: For machine learning models to improve, they need continuous, high-quality data. An AI platform must support the collection and processing of training data, allowing models to learn and improve over time.
• Model Training and Experimentation: With the AI platform in place, teams can experiment with different model architectures and datasets, ensuring they find the best algorithms for their unique requirements. As needs evolve, the platform allows for ongoing testing and iteration.
• Model Monitoring and Management: To maintain performance, models must be continuously monitored. The AI platform tracks model accuracy, flags when updates are needed, and automates retraining processes to keep models relevant and effective.

An AI platform designed for MLOps allows organizations to make data-backed decisions at scale, identifying patterns and opportunities that may not be visible at the individual data-point level.

Achieving Long-Term Value with a Scalable, Adaptable Platform

As IoT platforms evolve, they must remain agile to meet new demands. By starting with an analytics and visualization platform, businesses can quickly gain visibility into their operations and validate the value of data-driven decision-making. When they’re ready to move into predictive analytics, the existing structure can scale, and an AI platform can be layered on top.

At Very, our approach to building analytics and AI platforms empowers organizations to take their IoT data further, helping them leverage everything from diagnostics to prescriptive analytics. Whether a company is just beginning its analytics journey or ready to integrate machine learning, a robust and adaptable platform ensures they’re equipped to meet the challenges and seize the opportunities of an IoT-driven future.

Read the full whitepaper to explore these topics and more, and learn how to future-proof your operations with industrial digitization.