Celestia Maddy June 11, 2021 Spreadsheet
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.
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.
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.
This will show your Angel Investor that you indeed are a rational thinker and concerned about the money as well as the truth. If the Angel Investor cannot trust you your chances of being funded are nil. An angel investor is betting on the jockey not only the horse. As an entrepreneur you must be honest with yourself as well as your financial partner. They want to make sure you believe in what you are doing and that you also have risked your own capital, time and energies into the new business. Angel investors want you to succeed and often they also like to give their input and if you end up taking their money for your startup, the need to realize that their input needs to be taken seriously.
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.
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