In my 10+ years as a data extraction expert, few things have delivered a bigger boost to analytics than automating the data wrangling process. Let‘s explore the key steps for wrangling and how automation makes each one faster, easier, and more scalable.
What is Data Wrangling?
Data wrangling refers to the process of transforming raw, messy data into a structured, analysis-ready format. It typically involves:
- Cleaning – Fixing missing values, duplicates, errors
- Shaping – Converting to rows/columns, pivoting
- Enriching – Augmenting with additional data
- Validating – Testing for anomalies, inaccuracies
Forrester estimates that data professionals spend up to 80% of their time on these tedious data prep tasks. But wrangled data serves as the essential fuel for analytics engines.
6 Essential Steps for Data Wrangling
Effective data wrangling typically follows these core steps:
Step 1: Data Discovery
The first step is exploring the raw dataset to understand its structure, completeness, and anomalies. This involves:
- Sampling data for early insights
- Profiling datasets and columns – data types, value ranges, distributions
- Quantifying missing values and errors
- Identifying naming inconsistencies
- Flagging outliers and data quality issues
Discovery provides the lay of the land before diving into deeper wrangling.
Step 2: Data Structuring
Next, impose structure by organizing data into a relational format. Key structuring tasks include:
- Setting up tables, columns, rows, and keys
- Reshaping unstructured data into rows and columns
- Splitting columns like full name into first and last name
- Pivoting data from row to column orientation and vice versa
- Assigning unique identifiers to relate records across tables
With structure in place, data can now be manipulated more easily.
Step 3: Data Cleaning
Arguably the most critical wrangling step, cleaning aims to weed out errors, inconsistencies, and noise from data. Activities encompass:
- Fixing formatting problems and typos
- Removing duplicate records
- Tackling missing values via deletion or imputation
- Detecting and removing outliers
- Standardizing abbreviations and metadata
- Enforcing integrity constraints
- Parsing unstructured data with natural language and AI
This produces a higher quality dataset for analysis. According to Experian, poor data quality costs companies over $600,000 annually.
Step 4: Data Enrichment
Enrichment augments existing data by merging in supplemental sources. Techniques include:
- Blending in external demographic data
- Joining transactional data to customer profiles
- Adding calculated metrics, aggregates, and lag/lead values
- Expanding contextual data with web scraping or APIs
- Linking survey responses to behavioral records
Added variables enable deeper insights.
Step 5: Data Validation
Next, inspect data for lingering quality issues before analysis. This may involve:
- Visualizing distributions and summary statistics
- Testing against data quality rules and constraints
- Programmatically detecting outliers and anomalies
- Running test queries and models to catch hidden errors
- Sampling to manually verify portions of data
- Comparing column distributions vs. expectations
Fixing validation findings prevents misleading analytics.
Step 6: Data Publishing
Finally, publish clean, validated data for use across the organization. This reusable dataset can now power reporting, business intelligence, and predictive modeling.
Key publishing steps:
- Outputting analysis-ready data to databases or warehouses
- Establishing data governance policies like user permissions
- Documenting definitions, business rules, and other metadata
- Making data discoverable via catalogs and search tools
| Task | Manual Process | Automated Process |
|---|---|---|
| Data Cleaning | Manually inspecting and fixing records | Automated parsing, validation, and editing |
| Enrichment | Manual joins and lookups | Automated blending of datasets |
| Validation | Manual sampling and checks | Automated constraint testing and profiling |
The Case for Automating Data Wrangling
Performing these steps manually presents a number of challenges:
- Extremely time consuming – wrangling can take up to 80% of an analysts time
- Difficult to scale – data volumes are growing exponentially
- Prone to errors – manual processes have limited quality control
- Not reusable – starts from scratch every time
- Fragmented workflows – disjointed mix of scripts, tools, and GUIs
Automating all or parts of the wrangling process delivers major benefits:
- Time savings – Gartner found data prep time can be reduced by 80% with automation
- Scalability – scripted workflows accommodate growing data volumes
- Consistency – minimize manual errors and omissions
- Reusability – script once, apply repeatedly to new data
- End-to-End automation – orchestrate entire workflows with scheduling, monitoring, and versioning
- Productivity lift – data scientists focus on high-value analysis vs. grunt work
Data Wrangling Automation in Action
Let‘s examine a real-world example of how automated data wrangling accelerates analytics at a retail chain.
The analytics team needed to merge sales transaction data with customer demographic data to power a churn prediction model. Doing this manually via Excel would have required:
- Exporting, sampling, and profiling both datasets
- Manually inspecting, fixing, and standardizing records
- Appending datasets and correcting key mismatches
- Extensive QA testing to catch issues
After several weeks of work, they would end up with a brittle, hard-to-reproduce output dataset.
With a scalable data wrangling platform like Trifacta, Alteryx, or Informatica, they could automate this workflow in hours or days versus weeks:
- Visually profile datasets and automatically catch anomalies
- Leverage pre-built parsers to structure unorganized fields
- Join datasets using fuzzy matching to align keys
- Orchestrate scheduling, notifications, and monitoring of job runs
- Version and re-run the script on fresh transaction data
The result is a reusable, scalable process delivering analysis-ready data with minimal manual intervention. The data science team could then focus their time on developing and tuning the predictive model rather than just prepping the input data.
Key Takeaways
- Data wrangling is crucial but time-intensive. Steps include discovery, structuring, cleaning, enrichment, validation, and publishing.
- Automating parts of the process saves time, improves quality, and increases scalability.
- Mature data wrangling platforms enable end-to-end workflow automation.
- Organizations see major productivity and analytical benefits from automation.
With the volumes and complexity of data increasing, wrangling automation has become table stakes for robust analytics. By taking the time and effort to implement the right solutions, data teams can tap into the full potential of their data assets.