In the business and engineering worlds, spreadsheets are our friends. They help us forecast, simulate, understand finite data sets, and perform quick mathematical exercises.
But when it comes to the IoT world of sensors and data, Excel is not our friend. Deploy some sensors, gather a bunch of data, then graph it. Not so difficult right? Well it all depends on how many sensors you have, how often they collect data, and how you want to visualize and compare the results. What starts as an academic exercise on a few hundred rows, can quickly snowball into a gigantic mess of thousands of rows/columns of time stamped asynchronous data series, never ending mouse hovering at the bottom of the screen attempting to expand your selection, and the unavoidable occasional spinning icon of death which leaves you cursing while wondering how much you time you have lost. Worse, have you ever tried graphing, windowing, and analyzing large data sets? As powerful as pivot tables are, it’s never enough. There has to be a better way, right?
The fusion of analytics and data has never been more valuable, especially in IoT, but for many enterprises is unreachable until both become easily accessible by all. Think of the progression in other domains; sales teams have moved from spreadsheets to CRM systems, engineers to Python based environments, and manufacturing teams who tracked everything manually with custom Excel macro’s, to ERP systems. Sure, there are your Power BI’s, Tableau’s, Spitfire’s etc., but they typically leave the users wanting more, without becoming overloaded with unnecessary complexity.
There exists an inherent gap in the marketplace for tools that can bridge the gap between generic analytics platforms, and purpose-built application specific solutions that are designed for ongoing use. This is where the analytics side of IoT can add a lot of value by:
- Easily managing large amounts of data
- Aggregating diverse sets of data
- Organization and visualization of complex metrics
- Distillation from past outcomes driving prediction of future occurrences
“Big data” isn’t always meaningful data, experienced IoT solution providers know this, and will work with enterprises to understand and present data in meaningful ways. For example, in industrial environments there are several key variables that are meaningful for IoT applications like temperature, pressure, and vibration. At 1-minute sampling intervals, this translates into almost 13,000 data points every 24 hours (9 unique variables). Now extrapolate this over several months to gauge trends, and you have added an enormous layer of data management into the equation that only few experts know how to properly handle. If we wished to compare those 9 variables (easily), it would be virtually impossible with Excel.
In the example above I can synchronize by time, zoom in, zoom out, and see trends automatically updated. As another example below, I can quickly perform comparisons on several sets of data to view similarities and differences. In this example I am viewing pressure data from three similar assets, clearly one is not behaving well (which led to multiple alarm warnings). Sure, I could trend the data manually in Excel and come to the same conclusion, but using built for purpose IoT analytics allows me to focus my time on more productive activities.
With over 8 million and counting data points under management, Preddio knows what works and what doesn’t when handling the everyday needs of data beyond spreadsheets. From aggregation, to consumption, to presentation, enterprise customers turn to Preddio to transform the complexities of IoT into simple to use analytics for the masses. Contact us today to learn more about how easy managing your data through Preddio can be.