Data Munging

What Is Data Munging?

Data munging is the process of cleaning and transforming data prior to use or analysis. Without the right tools, this process can be manual, time-consuming, and error-prone. Many organizations use tools such as Excel for data munging. While Excel can be used for the data munging process, it lacks the sophistication and automation to make the process efficient. In most organizations, 80% of the time spent on data analytics is allocated to data munging, where IT manually cleans the data to pass over to business users who perform analytics. Data munging can be a time consuming and disjointed process that stands in the way of extracting true value and potential from data.

Why Is Data Munging Important?

Data’s messy, and before it can be used for analysis and driving business objectives, it needs a little tidying up. Data munging helps remove errors and missing data so that data can be used for analysis. Here’s a look at some of the more important roles data munging plays in data management.

Data Preparation, Integration, and Quality

If all data was housed in one area in the same format and structure, things would be simple. Instead, data is everywhere, and it usually comes from multiple sources in different formats.

Incomplete and inconsistent data leads to less accurate and trustworthy analysis, which can make machine learning, data science, and AI processes impossible to execute. Data munging helps identify and correct errors, fill in missing values, and ensure data formatting is standardized before passing it to data workers for analysis or to ML models for use.

Data Enrichments and Transformation

Data enrichment is often used to enhance ML models or analytics. But before datasets can be used for machine learning algorithms, statistical models, or data visualization tools they need to be of high quality and in a consistent format. The data munging (or data transformation) process can involve feature engineering, normalization, and encoding of categorical values for consistency and quality, especially when using complex data.

Data Analysis

The end goal of that data munging process is to produce high-quality, consistent data that data analysts and data scientists can use immediately. Clean, well-structured data is crucial for the analysis to be accurate and reliable. Data munging ensures the data being used for analysis is suitable and contains as little risk as possible for inaccuracy.

Time and Resource Efficiency

Data munging improves an organization’s efficiency and resource use. Keeping a repository of well-prepared data means other analysts and data scientists can grab the data and immediately begin analyzing it. This process saves companies time and money, especially if they’re paying for the data they download and upload.

Reproducibility

Datasets that have been thoroughly prepared for analysis make it easier for others to understand, reproduce, and build upon your work. This is particularly important in research settings and promotes transparency and trust in the results.

Data Munging and Wrangling Process

The data munging process includes many steps—all with the purpose of deriving insights from raw data.

• Discovery: Also known as data profiling. Learn what’s in your raw data sets to think ahead about the best approach for your analytic explorations. This step involves gathering data from data sources and forming a high-level picture of the distribution, type, and format of data values. It allows you to understand unique elements of the data such as outliers and value distribution to inform the analysis process.
• Enriching: Before you structure and cleanse your data, what else could you add to provide more value to your analysis? Enrichment is often about joins and complex derivations. For example, if you’re looking at biking data, perhaps a weather dataset would be an important factor in your analysis.
• Structuring: This is a critical step because data can come in all shapes and sizes, and it is up to you to decide the best format to visualize and explore it. Separating, blending, and un-nesting are all important actions in this step.
• Cleaning: This step is essential to standardizing your data to ensure that all inconsistencies (such as null and misspelled values) are addressed. Other data may need to be standardized to a single format, such as state abbreviations.
• Validating: Verify if you’ve caught all the data quality and consistency issues and go back to address anything you may have missed. Data validation should be done on multiple dimensions.
• Publishing and orchestrating: This is where you can download and deliver the results of your wrangling effort to downstream analytics tools. Once you’ve published your data it’s time to move onto the next step, analytics.