Lacy Melina June 5, 2021 Spreadsheet
About four months after my husband asked for a divorce I made an appointment to meet a divorce attorney recommended by my therapist. She was 60 years old, short, smart and focused. I thought she was great. She charged $350.00 per hour. It was good that she was focused. I was at the time furious at my husband for his recent behaviors and I told her that I wanted to file for divorce immediately. Could she explain the process to me?
His entomological collection occupied any open areas large enough to accept skewered insects. And his Buddy Holly collection consisted of three scritchy albums the talented tunester recorded before dying at 22 when his plane crashed in Iowa. Lester wore black horn-rimmed classes identical to those of the late singer, and considered these a statement to the world that a ”cool” persona existed within his ”bean counter‘s” body. Too, Lester was a college graduate: Penn State, class of ‘78. He maintained a solid ”C” average over four years, and finally earned ”Certified Public Accountant” status on his fifth try. ”Reversing entries are hemorrhoids in the ass of accounting,” he remarked flatly during a first interview with his present employer, who dwelled briefly on his gradepoint average and numerous shots at CPA accreditation. ”They tricked me every time!” In spite of his lackluster academic record, the firm hired Lester and beginning Day One sacrificed him to Bourgeois and 20 other mediocre accounts.
He grossed $2,000 a week for his bosses, and earned slightly less than $500 for himself. Still, the wages kept him in seeds, bowling shoes, stick pins, and a Platinum Buddy Holly Fan Club Membership. Lester‘s favorite word was ”crapola,” and he applied it to the ball bearing factory‘s antiquated data processing system in coats as thick as the olive drab membrane clinging to the smudgy glass before him. ”You piteous piece of crapola!” he‘d hiss at the computer when error messages flashed across its screen or its ancient system locked under the demand of crunching numbers to the tenth decimal point. ”Some day I‘ll throw your sorry ass into one of those melting pots out there!”
Spreadsheets such as Microsoft Excel are well suited to tasks involving the manipulation of small amounts of related data. Working out a budget, producing visual reports, organizing lists and calculations that involve many variables are all tasks well suited to a spreadsheet. There are some data related tasks however that spreadsheets such as Microsoft Excel are not suited for. Tasks involving the processing and combination of large sets of data for example are generally not well suited to spreadsheets. There is another technology with a long history and theoretical background that specializes in these sorts of tasks. That technology is relational databases. The most common way people insert data into and extract data from relational databases is via the language of Structured Query Language.
Structured Query Language, often referred to as SQL, is a grammar of instructions that allows us to tell a relational database to add, modify or delete data. The key benefit, pardon the pun, of SQL is that it allows us to craft instructions relating large sets of data together. In this way SQL is the natural complement to the single cell and formula based interface of spreadsheets like Microsoft Excel. Imagine you had five hundred appointments from your business calendar laid out in a table. Each appointment might have a day, time, location and description. Now imagine you also had five hundred appointments from your partners business calendar, also each having a day, time, location and description.
Microsoft Excel is a phenomenally powerful calculator. You can create spreadsheets with 10,000 lines of data and calculate subtotals instantly. Indeed, if you change your data, any totals will get automatically updated. Arguably that‘s not too impressive. If we have quarterly revenues of $1m, and we secure another $20k, we can update our subtotal without summing revenues from scratch. So it‘s more impressive that Excel can do the same thing with statistical functions. If you‘ve ever plotted a chart on Excel, you may be aware that you can add a best fit line. These best fit lines are calculated using a method known as regression. Basically, you have to calculate the distance of every single point from the line, and minimise the sum. The maths is a little more sophisticated but the key point is that, every time you change the data, you need to perform the analysis all over again.