Lucile Elya May 3, 2021 Spreadsheet
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.
Second – Planning your Budget – is this easy or are you going to start over from scratch? If you kept good records and have accurate figures, then you have a great start for you next meeting. It is easy to modify last year‘s information and make changes for this year. That will be necessary for a variety of reasons. You will need it to tell your hotel contact what you want and you will also need it to prepare this year‘s budget. Third – Budgeting Spreadsheet for Meetings – take the easy way out. Use a spreadsheet that will make your job easy. There are excel spreadsheets that can do it for you. Do not waste your time trying to design something that already exists and is proven to save you effort and stress.
Here‘s where the expense sheet gets complex. All of your categories of spending go in column A. Get as detailed as you like, or keep it really simple and just put the basic categories. Generally, the more detail the better. it‘s also helpful to have categories for your categories. A Utilities category for your power, gas, water, etc, categories. Again, detail is good, but be wary of going into too much detail. Column B is where you‘ll put the amount that you‘re budgeting for that category. Column C, how much you‘ve spent on that category to date. If you like (or are a statistics junkie) you can add columns for % of budgeted amount, and % of total budget as well. For our simple budget, we‘ll just leave that out for now.
”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.
Given this data set imagine trying to find out which Fridays you were busy at an appointment at noon while your partner was also busy at an appointment at noon and the descriptions of both of your appointments contained the phrase down town. If you are not familiar with relational databases and SQL it might surprise you to know that the question can be answered by a single simple SQL query. The database and SQL don‘t have it all their own way however. Spreadsheets come in to their own for tasks that benefit from a visual representation. Traditionally databases do not provide a visual way to browse the data in tables without explicitly requesting data.
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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