As artificial intelligence (AI) comes of age and data continues to disrupt traditional industry boundaries, the need for real-time analytics is escalating as organizations fight to keep their competitive edge. The benefits of real-time analytics are significant. Manufacturers must inspect thousands of products per minute for defects. Utilities need to eliminate unplanned downtime to keep the lights on and protect workers. And governments need to warn citizens of natural disasters, like flooding events, providing real time updates to save lives and protect property.
Each of these use cases requires a complex network of IoT sensors, edge computing, and machine learning models that can adapt and improve by ingesting and analyzing a diverse set of high-volume, high-velocity data. To inspire greater trust and confidence in every decision, companies need to partner with organizations that are innovating with proven AI and streaming analytics in the cloud and on the edge. By doing this, it will be easier for them to harness hidden insights in diverse, high volume, high velocity IoT data, and capitalize on those insights in the cloud for secure, fast, and reliable decision making. To take advantage of all the benefits that real-time streaming analytics has to offer, it’s important to tailor your streaming environment to your organization’s specific needs.
Here are four steps you can take to better understand the value of IoT in parallel to your organization’s business objectives and then strategize, plan, and manage your streaming analytics environment.
Step 1: Understand the value of IoT
While you may already know that IoT and streaming analytics are the right technologies to enable your business’ real time analytics strategy, it is important to understand how it works and how you can benefit. You can think of streaming analytics for IoT in three distinct parts: sense, understand and act.
- Sense: Sensors by design are distributed, numerous, and collect data at high fidelity in various formats. The majority of data collected by sensors has a short useful life and requires immediate action. Streaming analytics is well-suited to this distributed sensor environment to collect data for analysis.
- Understand: A significant number of IoT use cases requires quick decision-making in real time or nearreal time. To achieve this, we need to apply analytics to data in motion. This can be done by deploying AI models that detect anomalies and patterns as events occur.
- Act: As with any analytics-based decision support, it is critical to act on the insight generated. Once a pattern is detected this must trigger an action to reach a desired outcome. This could be to alert key individuals or change the state of a device, possibly eliminating the need for any human intervention.
The value in IoT is driven by the reduced latency to trigger the desired outcome. Maybe that’s improving production quality in the manufacturing process, recommending a new product to a customer as they shop online, or eliminating equipment failures in a utility plant. Whatever it is, time is of the essence and IoT can help get you there.
Step 2: Strategize
Keeping the “sense, understand, act” framework in mind, the next step is to outline what you hope to achieve. To get the most out of your streaming analytics with SAS and Microsoft, keep your objectives in mind so you can stay focused on the business outcome instead of trying to act on every possible data point.
Some important questions to ask yourself are:
- What is the primary and secondary outcomes you are hoping to achieve? Increase productivity?
Augment safety? Improve customer satisfaction?
- What patterns or events of interest do you want to observe?
- If your machines and sensors show anomalous behavior what actions need to be taken? Is there an existing business process that reflects this?
- What data is important to be stored as historical data and what data can expire?
- What kind of infrastructure exists from the point where data is generated (edge) to cloud? Is edge processing an option for time-critical use cases or does processing needs to be centralized in cloud?
- What are your analytics and application development platforms? Do you have access to high performance streaming analytics and cloud infrastructure to support this strategy?
Once you’ve identified your outcomes, define which metrics and KPIs you can measure to show impact. Make sure to have some baseline metrics to start from that you can improve upon.
Step 3: Plan and adopt
Now it’s time to take your strategy and plan the adoption of streaming ana lytics across your business.
Adoption will look different if you already have an IoT platform in place or if you are working to create a net-new solution. If you are going to be updating or iterating upon an existing solution, you will want to make sure you have access to key historical data to measure improvement and use institutional knowledge to maximize performance. If you are working with a net-new solution, you will want to give yourself some additional time to start small and then scale your operations up over time so you can tackle any unforeseen challenges.
In both cases it is important to have key processes aligned to the following considerations:
- Data variety, volume, and accuracy: Focus here on the “sense” part of the “sense, understand, act” framework. Accessing good data is the foundation to the success of your streaming projects. Make sure you have the right data needed to achieve your desired business outcome. Streaming analytics helps you understand the signals in IoT data, so you can make better decisions. But if you can’t access the right data, or your data is not clean, your project will not be successful. Know how much data you will be processing and where. Data can be noisy, so it is important to understand which data will give you the most insight.
- Reliability: Ensure events are only processed once so you’re not observing the same events multiple times. When equipment fails or defects occur on the production line, ensure there are processes in place to auto-start to maximize uptime for operations.
- Scalability: Data science resources are scarce, so choose a low-code, no-code solution that can address your need to scale. When volume increases, how are you going to scale up and out? Consider a cloud that simplifies scale with its PaaS offerings, including the ability to auto-scale in the cloud.
- Operations: Understand how you plan to deploy your streaming analytics models, govern them and decide which processes can be automated to save time.
- Choose the right partners and tools: This is critical to the success of any initiative. Look for a best-inclass solution for bringing streaming analytics on the most advanced platform for integrated cloud and edge analytics.
Now that you have created your plan, it is time to adopt. Remember to start small and add layers of capability over time.
Step 4: Manage
To get the most value from IoT and streaming analytics, organizations must implement processes for continuous iteration, development, and improvement. That means having the flexibility to choose the most powerful models for your needs. It also means simplifying DevOps processes for deploying and monitoring your streaming analytics to maximize uptime for your business systems.
Maximizing value from your IoT and streaming analytics systems is a continuous, agile process. That is why it is critical to choose the most performant platform for your infrastructure and analytics needs. Learn more at sas.com/Microsoft
About SAS in IoT
SAS empowers organizations to create and sustain business value from diverse IoT data and initiatives, whether that data is at the edge, in the cloud, or anywhere in between. Our robust, scalable, and open edge-to-cloud analytics platform delivers deep expertise in advanced analytics – including AI, machine learning, deep learning, and streaming analytics – to help customers reduce risk and boost business performance. Learn more about our industry and technology solutions at www.sas.com/iotsolution