2014-07-10: Data Processing...

When I was writing my posts this week in an attempt to present some statistical data as information that was meaningful and useful I did, at one point, start to go off on a tangent about data processing. I didn’t get very far before I realized my post was already going to be really long and I probably should cut that in the interest of staying on topic. So I pushed it to the back of my brain as one of those things I wanted to elaborate more on in the future and kept going.

Most of the time, when this happens, I never circle back around and hit it again as I lose the interest or inspiration I had in the first place. But today I have quite literally nothing else to talk about. Nothing. So today, it’s all about data processing.

Several days ago when I was sitting at my computer and scrolling through posts and tallying up how many of this and how long of that. I had a spreadsheet where I was jotting down notes about the weeks and then it was pen and paper with actual tally marks for all the data on days. I think I said before I did not have time to read every post, so I could only go so deep with what I was counting but in the end what it amounts to is collecting data.

The act of using that looking at the data from different perspectives and trying to consume it in a way that is meaningful and useful reminded me of the very definition of data processing. That’s an archaic term now, but it was originally used to describe what computers did (or rather what people did with computers) when computers were first invented. In essesnce - data in, information out. In the 1970’s and 80’s, the industry was all about data processing and the people who wrote the programs that ran on computers were programmers. 

Things have evolved a lot since then. We’ve gone through several generations of programming languages and the industry lingo has changed. Programmers are now developers and they don’t work on programs, they work on code. I work at a software development company and I’m not a developer, but I do have a swanky title that didn’t really exist 15 years ago - Integration Engineer. 

I only know about the beginnings of “data processing” because I have several profs in college that liked to talk about the “good ole days” and to give credit where credit is due, at my first job after that, I worked with a guy who once worked for IBM and he also seemed to take pleasure in recounting stories about his first job where they used punch-cards. PUNCH CARDS! 

We’ve come a long way since then, but somehow the term data-processing has stuck with me enough that when I was doing the analysis of the data I had collected, it was one of the things I was thinking about.

Fun Fact - When I was in college, these profs had instantiated a club that the students could join, the DPSA. This stood for Data Processing Students of America. I’m not sure if they made it up or if it was an actual thing other students across America were also a part of. For lulz, I googled DPSA and here are the top results:

DPSA - Department of Public Service and Administration
DPSA - Data Power Systems Australia.  A data center company in Australia which does not list what the acronym stands for on their company home page. 
DPSA - Disabled People of South Africa

Keepin’ It Real,
Miss SugarCookie

Meanwhile, in data processing (QINSy)

Below are the some important matters and reminders ( That I managed to learn, so far.):

1. Tide Data (observed data/predicted data/simplified ATT method)

  • the method is depends on the client requirement.
  • if using predicted data generate by QINSy Tide Manager, make sure do a cross-check the value with the surveyor (if previously given predicted value from a different tide software). There will be a slight difference between the predicted tide (CD) and (MSL).
  • the difference between them should not > 4 cm.

2. SVP value

  • it is adviced to record the SVP value before each survey job day.
  • previous day job, the value still can be used but may affect data. (Need to study)