How can you clean and visualize data from legacy systems and databases?

1 Identify your data sources

The first step is to identify where your data is coming from and how you can access it. Legacy systems and databases may use different protocols, formats, and languages, such as FTP, CSV, XML, SQL, or COBOL. You may need to use specific software, drivers, or connectors to extract the data from these sources. You may also need to consider the security, privacy, and compliance requirements of the data, especially if it contains sensitive or personal information. Make sure you have the necessary permissions and credentials to access the data and follow the best practices for data governance.

2 Assess your data quality

The next step is to assess the quality of your data and identify any issues that may affect your analysis and visualization. Legacy systems and databases may have data quality problems, such as missing values, duplicates, inconsistencies, errors, outliers, or irrelevant data. You can use tools such as Excel, Python, or R to perform some basic data quality checks, such as counting the number of rows and columns, calculating summary statistics, checking for null values, or plotting histograms. You can also use data profiling tools, such as Trifacta, Talend, or DataCleaner, to automate some of these tasks and generate data quality reports.

3 Clean your data

The third step is to clean your data and fix any issues that you have identified. Data cleaning involves applying various techniques, such as filtering, sorting, merging, splitting, transforming, or imputing data, to make it more accurate, consistent, and suitable for analysis and visualization. You can use tools such as Excel, Python, or R to perform data cleaning operations, such as removing duplicates, filling in missing values, standardizing formats, correcting errors, or removing outliers. You can also use data wrangling tools, such as Trifacta, Talend, or DataCleaner, to simplify and automate some of these tasks and generate data cleaning scripts.

4 Visualize your data

The final step is to visualize your data and create charts, graphs, and dashboards that can convey your message and insights. Data visualization involves choosing the right type of chart, selecting the appropriate variables, colors, and labels, and applying design principles, such as clarity, simplicity, and consistency. You can use tools such as Excel, Power BI, Tableau, or R to create data visualizations, such as bar charts, line charts, pie charts, scatter plots, or heat maps. You can also use data storytelling tools, such as Flourish, Infogram, or Datawrapper, to add interactivity, animation, and narration to your data visualizations.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?