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Hampshire Company Spreadsheet

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Brigette Wendy May 12, 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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In a well-designed spreadsheet, any output can be calculated from the raw data. However, that‘s not always enough. Sometimes the output is fixed and the raw data is variable. Let‘s say you run an investment company and want to offer your clients a fixed return. An Excel expert could create a very complex model to calculate the likely return on investments over a fixed period. You could then calculate the internal rate of return being offered to clients. The problem is that you‘re not interested in the return offered to clients; that is, after all, fixed. Instead you‘re concerned with how much money you expect to draw from the investment fund, whilst still offering your investors a satisfactory return. If you have $1 and owe investors a quarter, you can calculate your profits using a simple formula.

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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.

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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.

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When Microsoft Excel is used to manipulate, store and analyse data it can become extremely difficult to manage, let alone efficiently work to produce any meaningful insights. This is because with data sets large and small, the data must be meaningful, logical, structured, internally consistent and clean. This holds true regardless of whether the data has been imported into excel from another system or manually entered. In this computing age, most people know that for any data set to be useable it must first be relatively structured and clean. A spreadsheet and its table layout naturally encourages data to be somewhat structured, however ensuring data is clean is also difficult.

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Lester‘s temporary office at the Factory was glassed on all sides, and surrounded by the sights, sounds, searing temperatures, and smells of the smelting and pouring areas. Originally, the cubbyhole had been used for storing coal and coke until the plant converted to gas-fired furnaces in the mid-‘50s. Over the next three decades a succession of plant superintendents used the room to boink their secretaries, which necessitated its windows being painted a squalid olive drab. During 10 years of performing this chore every six months, Lester had scraped two panes clear, so now he could gaze into the murky, smoky, smelly pit outside as he waited for the grinding computer and clackety printer to spit out a stream of spreadsheets.

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