In today's fast-paced business world, data is more important than ever. It drives everything from marketing campaigns to product development and can provide valuable insights into customer behaviour and trends. As a result, it is crucial for businesses to have clean, accurate, and well-organized data.

Unfortunately, many businesses overlook the importance of preparing their data, and this can have disastrous consequences. When data is not properly prepared, it can be difficult to analyse, leading to incorrect conclusions and misguided decisions. This can damage a company's reputation and affect its bottom line.

One of the biggest challenges with data preparation is ensuring that the data is accurate and free of errors. There are many common errors when it comes to working with data; one of the most common is that of missing data - whether that arrives from a data entry error, technical issues, data extraction, or simply a gap in the data collection (a non-answer from a source for example). However, there are several other potential issues when dealing with data, such as duplicated data either from duplicated entries or from multiple sensors recording the same information - or inconsistent data and formatting, generally, an issue arising from user input methods.

Within the data itself, there could also be values that you wish to normalize or clean, such as outliers - which could be of importance to the stakeholder or could be a result of a measurement error. These outliers can have major impacts on the results of statistical analysis and machine learning models.

Finally, there is also the issue of bias within data, however, this can be a little harder to correct as it may not be apparent on the face value of the data source. You can read more about bias in data collection here.

Another challenge with data preparation is organizing the data in a way that makes it easy to analyze and understand. This may involve organizing the data into tables or files, or creating specific fields or columns for different types of data. Preparation can be difficult, especially when dealing with large datasets or multiple sources of data. However, it is essential to spend the time and effort to clean and validate the data, as errors can lead to incorrect conclusions and poor decision-making. Thus to save the arduous task of manually correcting errors within a data set, analysts use preparation software such as Tableau Prep and Alteryx.

In addition to accuracy and organization, data preparation also involves ensuring that the data is in the correct format - whether that is coded as a string, date, or integer. Different types of data may need to be converted or transformed in order to be compatible with analysis tools or visualization software. You can read about the different data types here.

Overall, failing to prepare your data is preparing to fail your clients. This short series will explore the ways in which data can be created, converted, validated, organized, and analyzed - through two software packages, Alteryx, and Tableau Prep. While Altyerx is not used as often due to its enormous expense, it is a very expansive and useful tool. Tableau prep is a lot more common within the Tableau community as it can be bolted onto a Tableau license.

Click here to read Chapter 2: An Introduction to Preparing Data in Alteryx

Click here to read Chapter 3: An Introduction to Preparing Data in Tableau Prep