What is Data Munging?

Data munging, also known as data wrangling, is the process of converting raw data into a more usable format. Often, data munging occurs as a precursor to data analytics or data integration. High-quality data is essential for sophisticated data operations.

The munging process typically begins with a large volume of raw data. Data scientists will mung the data into shape by removing any errors or inconsistencies. They will then organize the data according to the destination schema so that it’s ready to use at the endpoint. Munging is generally a permanent data transformation process.

Why Use Data Munging?

Most organizations have multiple, disparate sources of incoming data. These sources will all have different standards for validating data and catching errors. Some may simply output the data “as-is.”

Data consumers need to have clean, organized, high-quality data. These consumers can include:

People: Data scientists and analytics teams require a steady stream of data. To provide them with this, the business needs to implement a munging process. This guarantees a supply of high-quality information, which they can then use for detailed analysis. The organization can also make munged data available to business users through data marts.

Processes: Automated processes might require data from other systems. For instance, an order fulfillment system might require different pieces of customer data from across the network. Munging helps to remove any data inconsistencies, allowing these processes to run smoothly in the background.

Repositories: Organizations often store vast quantities of information in a data lake or data warehouse. There’s no point in storing low-quality data, and a munging process eliminates issues and ensures that everything stored is of value. Munging can also help standardize data, which makes it easier to store in a data warehouse.

Data munging is an important process whenever the data source does not perform its own form of data preparation.

How to Do Data Munging

The term “data munging” has been around since the 1960s when data scientists used manual methods to wrangle their data into the correct format. This kind of process led to the jokey acronym, “Mash Until No Good.”

These days, data scientists use tools like Python and SQL to help them perform faster munging. The modern data munging process now involves six main steps:

1. Discover: First, the data scientist performs a degree of data exploration. This is a first glance at the data to establish the most important patterns. It also allows the scientist to identify any major structural issues, such as invalid data formats.

2. Structure: Raw data might not have an appropriate structure for the intended usage. The data scientists will organize and normalize the data so that it’s more manageable. This also makes it easier to perform the next steps in the munging process.

3. Clean: Raw data can contain corrupt, empty, or invalid cells. There may also be values that require conversions, such as dates and currencies. Part of the cleaning operation is to ensure there’s consistency across all values. For instance, the state in a customer's address might appear as Texas, Tex, or TX. The cleaning process will standardize this value for every address.

4. Enrich: Data enrichment is the process of filling in missing details by referring to other data sources. For example, the raw data might contain partial customer addresses. Data enrichment lets you fill in all address fields by looking up the missing values elsewhere, such as in the CRM database or a postal records lookup.

5. Validate: Finally, it’s time to ensure that all data values are logically consistent. This means checking things like whether all phone numbers have nine digits, that there are no numbers in name fields, and that all dates are valid calendar dates. Data validation also involves some deeper checks, such as ensuring that all values are compatible with the specified data type.

6. Publish: When the data munging process is complete, the data science team will push it towards its final destination. Often this is a data repository, where it will integrate with data from other sources. This will make the munged data permanently available to all consumers.

Without automation, this process can take a long time. Most data scientists now rely on automated processes like ETL to replace older methods of data munging.

Issues with Data Munging

Data munging processes sometimes present issues such as:

Resource overheads: When data scientists oversee the munging process, it can take up a substantial amount of their time. Many data professionals spend a substantial chunk of their workday on data wrangling tasks rather than data analysis.

Data loss: Data munging is usually a one-way process. Data scientists permanently transform the incoming data, and there may not be an extant copy of the original data. If there’s no record of the transformations that took place, this could lead to inadvertent data loss.

Flexibility: Munging often has one objective in mind, such as preparing data for analytics. This means that the data may not be in an appropriate format for other uses, such as warehousing.

Process errors: If the munging process is manual or semi-automatic, there's a chance for errors to creep in. Sometimes, these arise from a lack of business knowledge on the part of data scientists. An automated process gives business experts an opportunity to get involved in the data mapping process.

An automated ETL is generally the preferred workaround for all these issues. It makes the data transformation process more flexible and transparent while reducing the burden on data scientists.