Dirty Data Cleanup: The Missing Piece to Better Business Outcomes

Dirty data is a common problem for organizations. It refers to data that is inaccurate, incomplete, or inconsistent. These problems can happen for various reasons, such as errors in data entry, changes in data sources, or missing data points. Dirty data can lead to poor model performance, inaccurate predictions, and incorrect business decisions.

One of the first steps in dealing with dirty data is identifying it. This process can be done by visualizing the data, looking for outliers or patterns that don't make sense, or using statistical tests to check for inconsistencies. Once the dirty data has been identified, it needs to be cleaned. These improvements can be made by removing or correcting the data, adding missing values, or using data from other sources to fill in gaps.

There are a variety of tools and techniques that can be used to clean data. For example, data cleaning can be automated using Python libraries such as pandas and NumPy. Data visualization tools like iTRACS®, Power BI™, Matplotlib, and Seaborn can be used to identify outliers and patterns in the data. Finally, machine learning algorithms can be used to fix missing values or correct errors in the data.

It's important to note that data cleaning is an iterative process. It's not uncommon for organizations to go through multiple rounds of cleaning before they are satisfied with the quality of their data. Additionally, it's essential to keep track of the cleaning steps performed so they can be reproduced in the future.

Dirty data can be a significant roadblock in data improvement projects but can be effectively cleaned and dealt with using the right tools and techniques. The key is to be diligent in identifying and cleaning the data as early as possible to avoid any issues.

Ultimately, the most important thing is to remember that data improvement is a team effort, and it's essential to have a robust data governance policy and software tools to ensure that your data is accurate, up-to-date, and reliable. This process includes having a designated data owner, regular data quality checks, and straightforward data entry and storage guidelines in systems such as iTRACS. With these practices in place, you'll be well-equipped to handle dirty data and keep your organization's data projects on track.