data wrangling
What It Means
Data wrangling is the messy, time-consuming work of getting raw data ready for AI models and analytics. It involves finding the right data sources, cleaning up errors and inconsistencies, combining data from different systems, and formatting everything so your AI tools can actually use it effectively.
Why Chief AI Officers Care
This process typically consumes 60-80% of any AI project timeline and budget, making it the biggest bottleneck to AI deployment at scale. Poor data wrangling leads to biased models, inaccurate predictions, and failed AI initiatives that waste millions in investment while damaging stakeholder confidence in your AI strategy.
Real-World Example
A retail company wants to build a customer recommendation engine but their customer data is scattered across their CRM (with duplicate entries), their e-commerce platform (missing purchase dates), and their loyalty program database (using different customer IDs). Data wrangling involves deduplicating customers, standardizing the ID systems, filling in missing information, and creating a single clean dataset that the recommendation algorithm can actually learn from.
Common Confusion
People often think data wrangling is just about cleaning dirty data, but it's actually about the entire pipeline of making data AI-ready. It's frequently confused with data engineering, but data wrangling is more exploratory and iterative, while data engineering focuses on building robust, automated data pipelines.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, data wrangling involves standardizing patient records from multiple EHRs, cleaning medication lists with ...
Finance: In finance, data wrangling is critical for ensuring regulatory compliance and accurate risk modeling, as financial insti...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"process by which the data required by an application is identified, extracted, cleaned and integrated, to yield a data set that is suitable for exploration and analysis."Source: Furche,_Tim
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