Introducing the New Predikto Maintain!

At Predikto, we take your IoT data and turn it into predictions. We convert unplanned failures into preventative maintenance events. We are very good at data transformation and analysis. We tear it up at modeling and machine learning. We have a cadre of super-smart data scientists, engineers, and subject matter experts.

But we are too smart for our own good. We created an app that is 1) beige (really?) and 2) doesn’t go the final mile to turn predictions into actions. And you can’t use your data to make a decision if you can’t understand the predictive output. Even if that prediction is mathematically sound.

For all the people out there who have ever felt this way about math books:

Predikto heard you, then, they hired me. And bam! Now we have a new app that normal humans can use.

Redoing your app from the ground up is scary. Why are you making such a radical shift? What was wrong with the old app? I am sure we’ve all read those business cases about how Domino’s took a big risk by admitting their pizza stunk and then totally redoing it. But, it worked out for them. Now I can order pizza from an app while driving. Which is totally a good idea.

This time, it wasn’t about old vs. new or good vs. bad. Our old app was quite nice. I just have a personal vendetta about beige (as do many bitter parents  and what is this perfume?), and against people who don’t think normal humans can understand science. As a writing professor once told me, “If your reader doesn’t understand your message, they aren’t stupid, you wrote it wrong.”

Without further ado – here is the new Predikto Maintain. It’s still got our algorithm-mastering, machine-learning, big-brain, MAX™ making amazing predictions. But now, it’s more understandable to mere mortals.

I know, It’s blue. Because we can’t be going too crazy here. Blue it is.

The heart of Predikto is our predictive models. But our end users think about them in three different ways. So now, we offer up three different methods to examine what our Predikto MAX models are doing, and more importantly, what they are telling you to do.

  • Let’s start with the system engineers who actually have to go do things during the day (like fix locomotives or quay cranes or aircraft engines). We let you know when our MAX algorithms predict a critical component will fail. And now, on the same screen, you can see what has happened to this asset, and point-of-failure, in the past – and how long you have to fix it. You can drill down into the events from that asset, reading more about work orders and sensor data relevant to the situation. You can quickly select what to do about this prediction (go fix this, close this) and move on with your day.
  • Moving on to the data scientists, who need to monitor their company’s data, and their overall system health. We’ve developed a Data Science screen where your data folks can geek out to their heart’s content. We’ve got a method to monitor the success of the ETL, a way to check in on model performance, methods to perform data integrity checks, QA checks for data feeds, and much, much more…
  • Finally, for the decision-makers, we show you Predikto’s Value Add. Now you can see exactly how many unplanned maintenance events have been prevented, and how many man-hours (and more importantly, how much money) our system has saved. And you’ll notice how quickly this all adds up to customer success.

All and all, a great, blue, package. And this is just the tip of the iceberg. Soon, we’ll have even more information about our predictions, our models, and your system health. Maps! Drill downs! Sliders! The display options are endless. I promise this: no 1990 my-space pages, and certainly, no beige.

I predict the future is bright. And actionable.

Author: Amy Sharma

Amy Sharma, PhD, is the Dir of Product for Predikto. She sits at the intersection of customer needs and technology possibilities to develop the vision, strategy, and action plans to guide technical products from conception to completion. Dr. Sharma has worked in many aspects of the engineering field: spearheading the development of a High Performance Computing / Big Data Analytics vertical at Georgia Tech Research Institute (GTRI), managing a $1M annual Independent Research and Development (IRAD) program at GTRI, working as an Assistant Professor in Medical Physics at the University of Western Australia, working as an Assistant Program Manager for the National Science Foundation, obtaining a PhD in Biomedical Engineering at Duke University, and designing hardware logic for advanced server microprocessors at IBM. In her work, Dr. Sharma enjoys problem solving, project management, developing big-picture R&D strategies, and communicating scientific and technical ideas to non-scientists. Outside of work, Dr. Sharma enjoys volunteering with various STEM outreach organizations and smoking meat.