Data Wrangling: What, How, and Why Shipyard's Your Huckleberry
Data Operations

Data Wrangling: What, How, and Why Shipyard's Your Huckleberry

Shawn Fergus
Shawn Fergus

Data wrangling refers to the process of converting, mapping, enriching, or transforming raw data into a clean, orderly format. Data reformatted through the wrangling process can then be utilized downstream for important functions like data analysis and data visualization, machine learning, and data science, or to produce valuable insights organizations can use to make better business decisions.

This vital process can also be referred to as data munging, data cleaning, and data remediation. However, eight out of 10 Shipyard in-house experts agree that the Wild West/Tombstone-adjacent "wrangling" sounds way, way cooler (apologies to all you munging process fans).

data wrangling

Business users who rely on the high-quality data that the wrangling process produces can vary widely within a single organization and may include data scientists, data architects, business analysts, data analysts, compliance officers, and product managers.

Therefore, it's crucial to understand exactly how data wrangling works, its benefits, best practices, and why data orchestration platforms like Shipyard can help wranglers ride high in the saddle.

Wrangling and its role in the data lifecycle

Information may be the “oil of the 21st century.” But, just like oil, information isn't valuable on its own. Information becomes valuable when put to work. And the better the information, the better the work we can do with it. It's easy to see why data wrangling is now such a critical aspect of DevOps.

Wrangling data (i.e., information) directly increases its quality, providing organizations with confidence that it can be used to fuel analysis and decision-making. Wrangling data before it's used frees up valuable time, as more time gets spent working with the data as opposed to actually making it work.

And, increasingly, online business and data sharing spans countries, if not continents. Data processed to be pristine is easier to secure and audit, which helps organizations remain compliant with regulations like the EU’s General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).

It’s a simple concept to get one's head around. But wranglers use several skills and specific steps to process unstructured data correctly:

Data wrangling step-by-step

  1. Data acquisition: Wranglers will first collect data from all necessary sources (often databases, external data feeds, and APIs). During this step, wranglers will also determine what scope, granularity, and data quality a given downstream task requires.
  2. Data profiling: Next up, metadata and statistics reveal any missing values, inconsistencies, and quality issues the newly aggregated data may have.
  3. Data cleansing: The data is then cleansed using predefined rules, lookup tables, or algorithms to deal with any errors, inconsistencies, or outliers identified during the profiling step.
  4. Data transformation: Once the data is corrected, it's converted into whatever format is necessary. This transformative step may include further aggregation, splitting or combining fields, deriving new variables, or reshaping datasets.
  5. Data enrichment: Other data sources may also be joined to the dataset, enriching it with additional fields or valuable context.
  6. Data validation: With all this wrangling in the mix, the data must also be validated, a step that verifies the cleaned and transformed data will still align with the actual conditions it needs to represent.
  7. Data structuring: Finally, wranglers may index, partition, normalize, or denormalize the data to organize it into files, tables, or databases. Structuring the data in this way ensures it will be easy to access and query.

In addition to the skills and steps the data wrangling process requires, DevOps teams also leverage various tools and technologies, including SQL for querying structured data sources like relational databases, python libraries like Pandas and NumPy for data manipulation, and big data technology like Hadoop and Spark for rustlin’ large-scale data.

Breaking down the benefits of data wrangling

Based on what we've covered thus far, it's clear that a properly executed wrangling process creates benefits that extend beyond the hard-working folk who rely on high-quality data to do their jobs.

While not exhaustive, the following organizational benefits give a good sense of the holistic impact data wrangling has on an organization:

Consistently better data: There's an old Western saying: "If you climb in the saddle, be ready for the ride." While its origins are unknown, its note of caution and accountability is relevant to the 87.8% of businesses that reported increases in data investments during 2022 and 93.9% of organizations planning to invest more in data in 2023. As more data gets used in business, the accuracy and completeness that wrangling produces is increasingly mission-critical.

Enhanced governance: More than staying on the right side of the law (i.e., regulation), well-documented data wrangling can include tracking the lineage of data, tracing where data came from at a given point in time, and how it was transformed.

Analytical efficiencies: Better governance helps ensure data is trustworthy, in addition to being easier for data analysts to handle. This useability, combined with far less time spent cleaning data, frees up more time for analysis and generating insights.

Operational efficiencies: Freeing up even more time that can be reinvested into insight generation, wrangling can be automated once you've set it up. This strategic use of automation can reduce the workload for data engineers and their analyst co-workers. Implementing automation also makes scaling easier, as the wrangling process can adapt as demand ebbs and flows.

Cost savings: It's no surprise that all these efficiencies inevitably lead to cost savings as well. In addition to the reduced manual labor costs noted above, a net reduction of errors in the dataset helps business owners avoid costly analytics and operations mistakes.

Improved knowledge sharing: Not all sources of data are external. Data wrangling also ensures inter-organizational information conforms to a consistent set of business rules. Like externally wrangled data, this benefits internal analytics and analysis. But clean, well-structured data is also easier for non-technical stakeholders to understand and use.

Better decision-making: As high-quality data drives more insights and access to those insights increases, leadership gets better at making critical business decisions. Moreover, cleaner, more accurate internal data mitigates risks associated with making decisions based on faulty information.

Competitive advantages: The net benefit of effective data wrangling can be a competitive edge. Smarter decisions, reduced risk, less operational waste, and cost savings enable organizations to innovate, often getting to market faster. And professional cultures where better information and ideas flow freely between employees, departments, and leadership can translate into improved products, services, and customer experiences.

Data wrangling best practices

How best to wrangle data, then? Implementing a productive, effective process requires domain expertise, solid technical know-how, and an understanding of best practices. Consider the following list a starting point for a beginner's wrangling needs.

  • Planning and understanding requirements: Your data wrangling needs to begin by clearly defining the goals of the tasks to be supported. These goals can then guide the scope of the wrangling process. Domain experience ensures wranglers can account for the nuances different business and analytical tasks require.
  • Data exploration and profiling: Before any cleaning or transformation occurs, thoroughly profile your data to identify its initial quality, structure, and character. Visualization tools can help identify patterns, anomalies, and outliers at this point.
  • Documentation and metadata management: Document each step, transformation, and assumption made, in addition to storing metadata about all types of data that pass through the wrangling process. Documentation is essential for debugging, audit trails, and reproducibility. You can use the metadata capture to ensure compliance and traceability as needed.
  • Data quality assurance: Introduce validation frameworks so that data quality gets automatically checked as it's wrangled. And implement further rules and checks to ensure data is consistent across the dataset.
  • Transformation and enrichment: Save future wrangling time by making your transformations modular and reusable. Also, consider opportunities to enrich each dataset by integrating it with other valuable data sources. But make a point to always independently evaluate the quality and applicability of these sources first.
  • Automation and scalability: Whenever possible, leverage automation to handle repetitive data wrangling tasks in addition to your validation frameworks. Maximizing automation will improve efficiency, reduce instances of manual errors, and enable the wrangling process to scale if data volume or complexity grows.
  • Testing and validation: Create unit tests for data transformations to catch any issues early in the process. And, while people tend to be harder to wrangle than data, endeavor to conduct user acceptance testing (UAT) with end-users and stakeholders to ensure data transformed in your process meets expectations.
  • Version control: Data versioning, like code versioning, is quite useful for auditing and debugging. So, also make a point to keep versions of datasets and your transformation logic.
  • Review and collaborate: Finally, to benefit from fresh eyes and perspectives, invite other team members to review your established wrangling process and its output. And remember, creating a feedback loop with end-users will help wrangling within your organization continuously improve.

The importance of data wrangling tools

There's a final, lovely, dusty old saying we'll share here today—you can't work an empty wagon up a hill. To the more bovine-focused wranglers of yesteryear, this was a way of saying that important tasks often need planning and the right tools to get them done correctly.

This is why, before riding off into the sunset, we'll touch on how data orchestration platforms like Shipyard can help the wrangling process get done right.

Wrangling data without an orchestration platform like Shipyard is undoubtedly possible. However, manual efforts in IT environments tend to become labor-intensive over time. In addition to the increased likelihood of errors and inconsistencies this creates, a manual data wrangling process will also be slower. And when data management is slower, every task relying on it downstream feels the drag.

In addition to more errors and a slower overall process, manual wranglers may also struggle to collaborate and scale while attempting to face down integration complexities, additional security risks, and version control challenges.

On the other hand, automated workflow orchestration platforms excel at handling repetitive tasks for you, so they're executed efficiently and consistently. Platforms like Shipyard are quick and easy to set up and can be modified as needed, which can speed up all your data management processes.

Since data wrangling rarely gets relegated to just one cowpoke, it can be immensely helpful that best-in-class orchestration platforms support collaborative efforts in cleaning and transforming data. Platforms like Shipyard ensure data is handled appropriately through any wrangling process, make it easy to roll back or audit changes, and integrate easily with an impressive array of tools.

So, if data wranglers are in your near future, make sure you’re also considering a best-in-class orchestration tool (especially Shipyard).And make sure to stay up to date with the data community (including all those wranglers, mungers, cleaners, and remediators out there) by signing up for our weekly newsletter.