Adalene Shaïna June 5, 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.
These common complaints with Microsoft Excel filtering are heard time and time again by engineers, accountants, management consultants, bankers and finance professionals who work with data in Excel spreadsheets on a daily basis. Many spreadsheet users including financial modellers (who seem to be leading the charge) are turning towards Excel Add-ins and software tools that plug into Microsoft Excel to help them improve the in-built filtering logic of Microsoft Excel and thus analyse certain data sets quickly and easily.
Here‘s a very simple budget set up. Keep a simple income spreadsheet. List all the sources by name in column A. List how much each brings in in column B. And then, any notes you have for the income (like if it is temporary) in column C. You don‘t need to get very detailed with the income, because it only needs to be accounted for so that we can budget for it‘s use. And, the incomes use is in our expenses spreadsheet. This spreadsheet will be much more complex than the income one. You‘ll need a field for income that you carry over from the income sheet. You‘ll also need a field for a total expenses budgeted for. A third field will give us the budget surplus. We get that by subtracting the budgeted amounts from the income amount. A final field will subtract the actual amount spent from the income, and will serve to tell us where we stand in our budget. If you like, you can add another field that subtracts the actual amount spent from the amount budgeted.
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.
First – History/Budget – what kind of a history do you have from your last convention? Did you fill out forms that showed all the results of your meeting? You started with a contract that specified sleeping rooms and scheduled functions, but did you update those numbers at the conclusion of your convention? This is important! You really do need to know what happened last year including your exact sleeping room pick-up, registration numbers with total income generated, specific meeting expenses and the number of attendees that attended each function. Without these numbers you are just guessing.
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.
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