Data is everywhere in today’s connected environments—and it adds up fast. The issue isn't collecting data anymore. It’s knowing what’s relevant and what to do with it. Artificial intelligence (AI) and machine learning (ML)-driven tools are helping to solve this problem, but only when applied with purpose, oversight, and an understanding of their benefits and limitations.
That’s where MachineQ’s new smart summary feature, designed for our client-facing application, MQInsights, comes in. Intended for operations and facilities teams, it uses ML to process millions of data points and summarizes them into clear, concise, and actionable insights, enabling teams to move quickly and make informed decisions.
Too Many Alerts, Not Enough Clarity
IoT adoption has opened incredible visibility into operations, especially in industries like life sciences. Lab ops teams can now track assets, monitor conditions, and spot issues in real-time. And since labs constantly collect data, finding ways to cut through the noise becomes vital.
That’s because traditional threshold-based alerts, which help teams focus on critical problems, often cause “alert fatigue,” which leads to teams potentially ignoring notifications. On the flip side, since labs collect so much data, it would be nearly impossible to generate alerts that cover every possible scenario. So, how are teams expected to keep up with alerts, analyze IoT sensor data, and synthesize insights while keeping up with day-to-day tasks?
A Smarter Way to Surface Data that Matters
MachineQ’s smart summary feature addresses this challenge head-on by applying AI and ML to review and synthesize large swathes of data produced by its IoT sensors across four key operational areas:
- Asset location
- Asset utilization
- Alerts and general status updates
- Sensor readings from monitoring devices
Essentially, the smart summary feature serves as a virtual data analyst integrated into our MQinsights application. It won’t replace your team’s expertise, but it will run quietly in the background, constantly scan for what’s relevant and deliver a summary of what's worth your time—all with the click of a button.
That’s why it comes as no surprise that AI adoption in life sciences is gaining ground. A recent survey found that nearly 60% of labs plan to implement AI and ML tools within the next two years.
Turning Data Insights into Business Impact
At MachineQ, we’ve always believed data is the backbone of smart operations. But its value depends on how it's used. The smart summary feature helps close the gap between raw data and real action—what we see as the “last mile” of IoT.
By delivering intelligence that’s usable, we can help teams stay agile, efficient, and focused on what really matters.
