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Bernadea Elyana June 3, 2021 Spreadsheet

Spreadsheets such as Microsoft Excel are well suited to tasks involving the manipulation of small amounts of related data. Working out a budget, producing visual reports, organizing lists and calculations that involve many variables are all tasks well suited to a spreadsheet. There are some data related tasks however that spreadsheets such as Microsoft Excel are not suited for. Tasks involving the processing and combination of large sets of data for example are generally not well suited to spreadsheets. There is another technology with a long history and theoretical background that specializes in these sorts of tasks. That technology is relational databases. The most common way people insert data into and extract data from relational databases is via the language of Structured Query Language.

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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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So why does data that inevitably finds its way into a Microsoft Excel spreadsheet often suffer from the problems outlined above. The reasons are many. If the data is imported, it may have been sourced from a combination of other spreadsheets, databases, systems, reports, word documents, emails or web pages. If the data has been entered manually it may have been poorly done so by an inexperienced computer users such as administrative or junior staff with a lack of understanding for data structures. Excel is easy to use and widely accessible, so an inexperienced colleague can quite easily update your spreadsheet with a false sense of confidence and inadvertently enter new data incorrectly. And finally, unlike a fully functional software system, data entry in Excel generally has no automatic validating rules, unless carefully setup by the spreadsheet‘s creator.

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Microsoft Excel is a phenomenally powerful calculator. You can create spreadsheets with 10,000 lines of data and calculate subtotals instantly. Indeed, if you change your data, any totals will get automatically updated. Arguably that‘s not too impressive. If we have quarterly revenues of $1m, and we secure another $20k, we can update our subtotal without summing revenues from scratch. So it‘s more impressive that Excel can do the same thing with statistical functions. If you‘ve ever plotted a chart on Excel, you may be aware that you can add a best fit line. These best fit lines are calculated using a method known as regression. Basically, you have to calculate the distance of every single point from the line, and minimise the sum. The maths is a little more sophisticated but the key point is that, every time you change the data, you need to perform the analysis all over again.

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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.

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One of the topics I cover on my Advanced Excel courses is hardly ‘advanced‘ at all, but it is a very useful and popular technique with my students. It makes use of the OLE capability to create invoices by embedding Excel data. First you need to create an Excel spreadsheet and format it in an appropriate manner, keeping in mind that this will form the basic structure of your invoice and will eventually be seen by your clients. You don‘t include any Company contact details or logos in the spreadsheet though as these will be incorporated into the Word document. The next step is to lay out the invoice itself in a Word document, based upon your normal Company letterhead. Leave the main body of the document empty as this is where the Excel spreadsheet will be embedded. All you need in this master Word document is your usual Company branding and contact information.

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