What is the importance of data standardization in Data Visualization?

A crucial step in the data visualization process is data standardization, which guarantees that data—often from different sources or formats—is converted into a uniform, consistent structure that is better suited for analysis and display. It entails eliminating variances that could cause misunderstandings by converting data pieces to a consistent scale, format, or unit of measurement. For example, standardizing currency values in a sales dataset by converting all values to USD enables precise comparisons and facilitates the visualization and analysis of sales performance across various areas or time periods. By removing inconsistencies and facilitating insightful comparisons, data standardization improves the efficacy of data visualization.

It involves normalising, scaling, or converting data to a reference or unit of measurement. Such as changing the scale to be between 0 and 1 or -1 to 1 depending on your use case. This helps integrate the data effectively with consistency & improves compatibility.

In my opinion, data standardization enhances the effectiveness and reliability of data visualization, making it an essential step in the data preparation process.

Data standardisation can take the data value to a whole another level. If the data is standardised it can help prevent siloed data analysis. With respect to visualisation it becomes a lot easier to create interactive dashboards as the data is standardised cross report filters can be used. Data standardisation can play a big role in gaining the trust in data.

Because data standardization is similar to making sure every instrument in an orchestra is set to the same pitch before a performance, it is essential to data visualization. The success of data visualization depends on the data items being in a consistent and harmonized format, just as the harmonious sound of a symphony depends on each instrument playing in unison. By standardizing the data, we remove variances that can skew the visual depiction, like converting all dates to the same format or all currency values to one unit. For example, standardizing currency values in a financial dashboard?to a common currency like USD guarantees data harmonization and facilitates useful insights and accurate comparisons.

The first step in achieving data standardization for data visualization is to pinpoint the precise dataset items that need to be standardized. Take a look, for example, at a sales dataset that contains product pricing in different currencies. First, select a common currency (USD, for example), and then convert all values to that currency using exchange rates. Standardize data formats as well. If your dates are formatted differently, make sure they adhere to a common format, such as YYYY-MM-DD. Automate this procedure using data transformation tools or scripts to ensure accuracy and efficiency. Finally, in order to preserve openness and facilitate consistent use in subsequent analyses, thoroughly record the standardization techniques.

In data visualization, data standardization can take many different shapes. For example, you could use constant exchange rates to convert all sales figures in a worldwide sales dataset to a common currency, such as USD, in order to standardize currency values. Another popular method is standardizing date formats, whereby dates such as "01/15/2023" and "2023-01-15" are converted into a standard format, like "2023-01-15." Ensuring consistency among datasets requires addressing units of measurement, such as converting kilos to pounds or meters to feet. A uniform level of data aggregation or summarization, such as monthly sales totals rather than daily figures, can also be a part of standardization.

In my view, data standardization plays a pivotal role in data visualization. It serves as the fine line between aesthetics and substance. Personally, I believe that a visually appealing dashboard is only as good as the accuracy and precision of the data it presents. Without proper standardization, you might end up with a beautifully designed interface that lacks meaningful, coherent information. In essence, data standardization ensures that every element in your visualization conveys accurate and relevant insights, making it an indispensable aspect of the data visualization process.