Genevieve Bianca June 9, 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.
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.
When presenting your business plan to an angel investor you must understand that they will be very interested in your spreadsheets and proformas, but you must also realize that it is typically an entrepreneurial optimistic approach, which causes problems with proformas. Therefore, you should have dueling spreadsheets; that is to say the spreadsheets, which take your best guess and double the time, double the expenses to compete with your optimistic approach. You should be able to present both of these to your Angel Investor; who chances are is a retired business person with a little bit of financial savvy.
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.
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.
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.
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