10.12-13.22-DigitalTech-Prescient-Devices

ANDY WANG: Thank you, Catarina. It's a pleasure to be here. My name is Andy Wang. I'm the founder and CEO of Prescient. What we do is we help customers to deliver insights from their Edge data. Edge data, what we mean are data coming from machines, instruments, robots, automation controllers, and even databases and files. So these are time series type of data and over 2/3 of all companies in the world generate a huge amount of time series today, but most companies don't actually know how to work with time series data properly.

This is because unlike transactional business data, time series data cannot be queried and mined easily like the transactional business data that people use in the modern data stack. So this creates a lot of difficulties for companies to fully uncover all the insights in their time series data and this is a problem we solve. So essentially what we do is that we can grab your time series data directly from your sources or from your databases.

We prepare that data, but the most important step is to create this events data layer. What is an events data layer? It is basically a step to extract all the insights and information from your raw time series data. Once you have this events data layer you can pretty much query and mine and slice and dice the data, like transactional data, in a SQL database.

But obviously, you can choose to store the events data in a SQL database or in a time series database, and now you have all the dimensions, flexibilities to work with that data. And the way to generate this events data layer is through quite a number of different techniques-- data fusion, analytics-- and often you have to integrate the domain knowledge from your company into the events data layer.

And we do this through a software we developed. It's based on open source, but it adds the ability for you to do a lot of the automatic generation and up keeping of the events data layer. It allows you to deploy those data operations to anywhere that your data resides, right? So it's a distributed solution and it sends a compute down to your plants, your warehouses, and allows you to very efficiently maintain that events data layer and that enables you to work with the data modern data stack.

So a simple example use case here is basically working with analytical instrumentation. So these are very complex, very expensive instrumentations, they've been around for decades, and they generate status data. If you look at a single slice of the data, there is actually not much meaning in it.

But by generating that events layer, which, in this case, mostly are basically your tests, right, whether they're successful or whether they failed, what kind of rework, what kind of error, and once you have the events layer, now you can generate a lot of insights out of it, which is efficiency, utilization, user behavior, and you can slice and dice the data to both help your customers and yourself to understand how the instruments work.

So our product is in production and we're working with some great customers and partners. We're looking for additional customers who wanted to uncover the insights out of their time series data, right? We know that a lot of companies have a huge amount of time series data, they're wondering what to do with it, and I think this is a very effective way to be able to get pretty much all the insights out of the data. So if you are interested to find out more, please come over to our booth. Thank you very much.

[APPLAUSE]

CATARINA MADEIRA: Thank you, Andy, and thank you again to all the entrepreneurs that presented today. I'd like to show the slide with the startups at the lunch exhibit, please. So you'll be able to learn more about all these nine companies during the lunch exhibit, but you'll find six additional companies there. They are Service Mob, Claira, IQ3, Einblick, Aura, and Skylla Technologies. And I believe Graham has some more housekeeping to share with us.