Data munging is the process of manual data cleansing prior to analysis. It is a time consuming process that often gets in the way of extracting true value and potential from data. In many 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.
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.
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.
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.
Data Munging Examples
Data munging happens all the time. Even if you’re not an analyst, data scientist, or another data analysis professional, you’ve probably engaged in at least one part of the data munging processes (especially the cleaning data stage).
Some examples of data munging include:
- Data aggregation: Combining data from multiple sources (e.g. spreadsheets, cloud databases, source systems, etc.) by importing, joining tables, and summarizing it based on specific criteria
- Correcting missing data: Inputting missing values, deleting rows or columns with a high percentage of missing data, and using interpolation to estimate missing values
- Converting data types: Changing strings to numeric values, converting datetime formats, and converting categorical data into numerical representations
- Filtering and sorting: Selecting specific rows or columns based on certain criteria or reordering data based on specific values
- Removing duplicates: Identifying and eliminating duplicate rows or records in the data set
- Data normalization: Standardizing or scaling data values to meet a specific range
- Feature engineering: Creating new features or variables from existing data, such as calculating the difference between two columns
- Outlier detection and handling: Seeking out outliers within the data and removing them, capping them, or transforming them if they could affect analysis results
- Text cleaning and processing: Removing unnecessary characters such as whitespace or punctuation, tokenizing text, converting text to lowercase, or stemming/lemmatizing words
- Data transformation: Applying mathematical or statistical transformations to the data, such as taking the logarithm, square root, or exponential of a variable
Cloud-based analytics platforms can offer advantages over traditional on-prem tools, such as scalability, cost efficiency cost, and easier collaboration. Platforms that automate repetitive and time-consuming data munging tasks can deliver more impact by reducing risk, shortening processing times, and increasing data quality and reliability.
Here are some of the advantages of using cloud analytics for data munging:
- Scalability and performance: Cloud analytics platforms can easily scale to handle large volumes of data, making it easier and faster to process and analyze big datasets. This can be particularly useful for data munging tasks that require significant processing power or storage, such as data cleaning, aggregation, or transformation.
- Data integration and storage: Cloud analytics platforms often offer built-in data integration capabilities, allowing users to easily connect to various data sources (such as databases, data lakes, or APIs) and import data into a centralized, cloud-based storage system. This can simplify the process of gathering, organizing, and transforming data from multiple sources for analysis.
- Collaboration and accessibility: Cloud-based platforms enable users to access data and analytics tools from anywhere with an internet connection, making it easier for teams to collaborate on data munging tasks in real time, without sacrificing governance or security. Version control and access permissions can also be managed more effectively in a cloud environment.
- Cost efficiency: Cloud platforms typically offer a pay-as-you-go pricing model, which allows organizations to pay only for the resources they use for their data munging tasks. This can help reduce costs compared to purchasing and maintaining on-prem hardware and software.
Data Munging Tools
For a detailed guide with real data displaying how each step of the data munging process can be done quickly and efficiently using Designer Cloud, download our eBook: Transform Your Data and Your Business in Six Steps.