用于数据清洗的 AI：AI 如何清洗你的数据并为你节省数小时和金钱

发布于 9 月 12 日

Dirty data in analytics industry: Almost every organization deals with some unreliability in data. Data practitioners spend 80% on data preparation and 20% on analysis due to the 80/20 rule (Pareto Principle). Poor data leads to poor insights, causing various problems like goal failure, cost increase, and customer dissatisfaction.
Data cleaning: It's the final stage of data entry, involving cleaning according to specific rules. Data errors can occur due to bad entry, source issues, mismatches, etc. Cleaning data means deleting wrong, corrupted, or incomplete information. Key red flags of bad data include duplicates, missing values, invalid data, and inconsistent formatting. Most bad data comes from human error.
How to clean incoming data:
- Examine and identify problems by selecting rows with particular values and reviewing each row.
- Handle duplicate values carefully, considering data file size and computation.
- Find outliers to spot anomalies.
- Validate data to ensure correctness.
AI and its role in data cleaning: AI and machine learning have changed data cleansing. Traditional methods couldn't handle large amounts of data. Now, ML-based solutions can analyze and improve data. It simplifies data arrangement, substitutes bad data with good data, and gets better with scale. ML algorithms can detect model flaws and improve predictions. Automation ensures clean, standardized data, reduces coding time, and allows easy integration of 3rd party apps. ML-based programs often use the cloud for customizable data solutions.

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