Nanine Camélia June 3, 2021 Spreadsheet
About four months after my husband asked for a divorce I made an appointment to meet a divorce attorney recommended by my therapist. She was 60 years old, short, smart and focused. I thought she was great. She charged $350.00 per hour. It was good that she was focused. I was at the time furious at my husband for his recent behaviors and I told her that I wanted to file for divorce immediately. Could she explain the process to me?
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.
Templates are spreadsheets that are pre-formatted with text, colors, and/or formulas. They can be used to save you time and effort. Sometimes you are using a workbook as a template and don‘t even realize it. By this I mean you have a spreadsheet that you use daily, weekly or monthly. You may have to modify a few cells but the remainder of the spreadsheet is exactly the way you want it. It may be tedious to modify the cells but it is easier than recreating it from scratch. Guess what, you have the basis of a template!
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.
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.
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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