Ruby Hanna June 4, 2021 Spreadsheet
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!”
Whilst Excel cannot clean or structure all of your data for you it does come with some useful functionality for manipulating and analysing clean and structured data sets. This in-built functionality includes pivot tables, sorting and filtering. Filtering alone is a powerful tool and can help to quickly isolate data based on specified criteria. But what happens if your data is clean but not very structured (a common problem). For instance what if you, a client or your team is using colours, fonts or some kind of formatting to classify data in an Excel spreadsheet. In short, you wont be able to filter the data, because Excel‘s in-built filtering logic requires rules based on numbers, dates and text only. It will not perform filtering based on formats. In addition Excel filtering only applies down rows. It will not perform filtering across columns.
In a well-designed spreadsheet, any output can be calculated from the raw data. However, that‘s not always enough. Sometimes the output is fixed and the raw data is variable. Let‘s say you run an investment company and want to offer your clients a fixed return. An Excel expert could create a very complex model to calculate the likely return on investments over a fixed period. You could then calculate the internal rate of return being offered to clients. The problem is that you‘re not interested in the return offered to clients; that is, after all, fixed. Instead you‘re concerned with how much money you expect to draw from the investment fund, whilst still offering your investors a satisfactory return. If you have $1 and owe investors a quarter, you can calculate your profits using a simple formula.
”Rippeto‘s Rendezvous” was only a block away, and attracted patrons from all levels of the social spectrum: Primarily fringe types, college students, and the occasional Young Urban Professional. It was near the University, and close to Civic Arena and Three Rivers Stadium. On clear nights, you could look out Rippeto‘s windows and see the Monongahela River afire in the distance. Two things are striking about Rippeto‘s when one weasels through the wall of humanity standing outside, and plasters oneself against an identical living wall inside: The smoke and the smell.
Lester loved his numeric universe, but this was not how he had envisioned his life unfolding; flying hither and thither from his hometown of Hershey to wherever his firm wished to send him. Just because he was 38, single, and still living with his folks didn‘t mean his employer should take advantage of him which, in fact, his company did on a regular basis. After all, Lester had other important interests, too. The ”Four Bs” he called them: Botany, bowling, bugs and Buddy Holly. Myriad plants crowded his tiny room in his parent‘s house, forcibly sucking carbon dioxide out of anyone who entered. Bowling trophies – ranging in size from tiny silver cups to massive bronze edifices shaped like the Empire State Building – claimed space not dominated by flower pots, planter boxes, and hanging baskets.