In data analytics, date and time are significant in recording and analyzing information. It guides us toward causality and allows for the sorting, synchronizing, and grouping of data, making it easier to identify patterns and trends. Nearly all data, in actuality, is a time series. It also enables us to track the timelines and progress of events and projects.

How We Mess it All Up
What we DON’T DO is write it down consistently. Take today’s date, April 9th, 2023, at 1:16 PM, for example. We record it willy-nilly as:

April 9th, 2023 1:16 PM
09/04/2023 13:16
04-09-2023 1316
2023-04-09 13:16:00.0000
09-04-2023 01:16 PM
04/09/23 13:16:00
(and on and on)

The month-day and day-month format lacks clarity and screws up so many things.

Wait a minute. We need the time zone, too. What about “Daylight Savings Time” in some locations? We need to track that too. Or do we? Some Windows operating systems don’t even have the concept of “Daylight Savings Time”. I found that out working in Malaysia.

Well, many people will use the local time in their computer systems and time stamp things using that time, but what if you have international operations sitting in many time zones sharing a common information system? Or, you are operating in the cloud, which is sitting in a German data center, and you are in Qatar? Which date-time do you use?

You don’t.

How We Straighten it Out
Our “Intellect” commercial/industrial data analytics, modeling, prediction, and optimization systems are getting so large that they are clusters of “Intellect” systems serving multiple locations spanning time zones and date time “cultures” (formats). Using local time would be a nightmare. A common time basis is required.

We solve this by using ISO 8601 and RFC 3339 standards and time policies. All dates accepted from external sources are converted to “UTC” (“Zulu”) time internally within Intellect, processed on a time standard, and then published the way the user wishes. Our example time above becomes 2023-04-09T20:16:00.0000Z

This standard supports all time zones of data origin within and across Intellect systems, clearly understanding the year, month, and day and resolving cultural differences. The format is string sortable, commonly recognizable, and compatible and eliminates any confusion. It assures that all data is recorded when it happens worldwide. No errors, mistakes, or exceptions.

When presenting information, such as tables and trend plots, the user can select one of several choices for data, such as local “culture” (date time formats), local time or UTC, or from any time zone they prefer.

Instead of a mess, they have a well-managed system with the flexibility to look at events worldwide on any basis they wish.

Next Up
Follow us for another installment in our series of articles, including the quagmire called “Units of Measure”. Cubic cubits per fortnight, anyone? 🙂 #data#analytics#dataanalytics#AI#IOT#IIOT