Tired and unfocused during long meetings? Im going to help you with that. Last week, I had a seven hour Zoom conference call, with about 20 people in it. (I know. I know.) It was necessary, and all parties were actively engaged, and needed to be. This was an authors retreat for a large research project, where Inciter is serving as the data visualization team. It was important that I stay engaged, not space out, and listen to the findings, the nuances, the decisions people were making.

This is the eighth and final post in our Data Cleaning in Excel blog post series. As the series comes to a close, wed like to take an opportunity to organize and recap material weve covered. Well review some of the useful Excel functions weve used, and provide some guidance for incorporating the techniques into your data cleaning routine. Every dataset has its own issues. Our posts in this series have been written to address ad-hoc common problems.

In this part of our data cleaning series, well be focusing on managing blank cells. There isnt a singular approach to handling blank cells in your dataset. Because there are numerous reasons why a cell might be blank, context is key when determining how to fill them. Sometimes, youll need to fill every blank cell in your data with the same constant. Other times, youll pick up clues as to what should be there from the surrounding data.

In this part of our data cleaning series, well help you find and remove duplicate entries in Excel. Repeats are a very common data entry mistake or error from a data pull. Duplicates in your data can create a variety of unfortunate consequences in administrative duties and analysis. Worst of all, they lead to a real misrepresentation of your results. This is Part 6 in our Data Cleaning in Excel 101 series.

Ever try to do a calculation with numbers in Excel and get an error or the numbers don’t seem to be adding up? Ever fight with ZIP code formatting? Below you can find some methods of dealing with numbers that just aren’t acting like numbers. Well also review some instances in which youll actually want Excel to store numbers as text, and how to convert them. This is Part 5 in our Data Cleaning in Excel 101 series.

Having the right data in the right columns to meet specific requirements for your analysis plays a major role in the data cleaning process. In Parts 2, 3, and 4 of our Data Cleaning in Excel series, well show you how to solve common issues by utilizing both standard and Excels powerful Flash Fill shortcut. Part 2 focuses on splitting data from one cell to multiple cells. Part 3 covers the opposite: combining data from multiple columns into one column.

Having the right data in the right columns to meet specific requirements for your analysis plays a major role in the data cleaning process. In Parts 2, 3, and 4 of our Data Cleaning in Excel series, well show you how to solve common issues by utilizing both standard and Excels powerful Flash Fill shortcut. Part 2 focuses on splitting data from one cell to multiple cells. Here, in Part 3, well cover the opposite: combining data from multiple columns into one column.

Having the right data in the right columns to meet specific requirements for your analysis plays a major role in the data cleaning process. Over the next 3 parts in our Data Cleaning in Excel series, well show you how to solve common issues by utilizing both standard and Excels powerful Flash Fill shortcut. Part 2 focuses on splitting data from one cell to multiple cells. Part 3 covers the opposite: combining data from multiple columns into one column.

At Inciter, we use many tools to clean data. Google Sheets and Excel are widely available and powerful tools for basic data cleaning of small to medium data sets. In this series, we will describe different methods for cleaning data that have common problems such as blank rows, duplicates, multiple values in one column, data split into too many columns, leading zeroes, numbers that dont act like numbers, basic data recoding, and comparing two versions of a spreadsheet.