Lucille Rose June 3, 2021 Spreadsheet
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.
As a set of general rules data is most useful when things like text fields hold only names as well as meaningful and validated codes, categories and classifications. Text notes and other free form text should be isolated to a dedicated notes field and thus separated from other numeric data. Numeric fields should hold only numeric values (numbers, dates, %‘s and in the correct quantum or magnitude with no text prefixes, suffixes, spaces, text elements or text notes present. You must also be careful that numeric data is not stored as text and it should be internally consistent in terms of the correct format so that it can be used in calculations or for comparison and queries. Finally, addresses should be separated out into multiple fields such as street address, town /suburb, state / province, postal code and country to allow for geographic analysis and mail outs if required. Fixing up a data set to meet these criteria is called data scrubbing, cleansing or massaging. This data cleansing process can be very time consuming even for an experienced Microsoft Excel user, database engineer, business analyst or computer programmer.
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!”
At times, Lester became so furious his face radiated heat and fogged his glasses. When this happened, he yanked them from his puffy eyes and wiped the lenses with his tie. On this late Friday afternoon, however, Lester felt exhilarated. The printer regurgitated its last run of printouts and as he scanned the rows of figures he penciled in tick marks to indicate matches with numbers found in the corporate ledgers. The task complete for another 180 days, he removed his glasses, rubbed his aching eyeballs, and inhaled deeply to savor the fluttery feeling of excitement flooding his upper chest. Then, Lester logged off the computer, tapped the surge protector power switch with his toe, and shut down the wheezing system.
When Microsoft Excel is used to manipulate, store and analyse data it can become extremely difficult to manage, let alone efficiently work to produce any meaningful insights. This is because with data sets large and small, the data must be meaningful, logical, structured, internally consistent and clean. This holds true regardless of whether the data has been imported into excel from another system or manually entered. In this computing age, most people know that for any data set to be useable it must first be relatively structured and clean. A spreadsheet and its table layout naturally encourages data to be somewhat structured, however ensuring data is clean is also difficult.
Lester‘s temporary office at the Factory was glassed on all sides, and surrounded by the sights, sounds, searing temperatures, and smells of the smelting and pouring areas. Originally, the cubbyhole had been used for storing coal and coke until the plant converted to gas-fired furnaces in the mid-‘50s. Over the next three decades a succession of plant superintendents used the room to boink their secretaries, which necessitated its windows being painted a squalid olive drab. During 10 years of performing this chore every six months, Lester had scraped two panes clear, so now he could gaze into the murky, smoky, smelly pit outside as he waited for the grinding computer and clackety printer to spit out a stream of spreadsheets.
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