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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Use Chaid Useful for Data Science Developers

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CHAID

The Chaid is one of the most asked skills for Data Science engineers. The CHAID Analysis (Chi-Square Automatic Interaction Detection) is a form of analysis that determines how variables best combine to explain the outcome in a given dependent variable.

Chaid Model


  • The model can be used in cases of market penetration, predicting and interpreting responses, or a multitude of other research problems.

  • CHAID analysis is especially useful for data expressing categorized values instead of continuous values.

  • For this kind of data, some common statistical tools such as regression are not applicable and CHAID analysis is a perfect tool to discover the relationship between variables. 

  • One of the outstanding advantages of CHAID analysis is that it can visualize the relationship between the target (dependent) variable and the related factors with a tree


1. CHAID Analysis for Surveys


Analysis

  • Most survey answers have categorized values instead of continuous values. 

  • Finding out the statistical relationship in this kind of data is a challenge. 



2. CHAID Analysis for Customer Profiling


Profiling


  • Based on historical customer data, CHAID Analysis can be used to analyze all characteristics within the file. 

  • For example, product/service purchased, the dollar amount spent, major demographics, and demography of the customers, and so on. 

  • A blueprint can be produced to provide an understanding of the customer profile: strong or weak sales of products/services; active or inactive customers; factors affecting customers’ decisions or preferences, and so on. 

  • Such a customer profile will give the Sales & Marketing Team a clear picture of which type of person is most likely to buy the products and services based on factual purchase history, geo-demographics, and lifestyle attributes.


3. CHAID Analysis for Customer Targeting


Customer Targetting


  • Recruiting new customers via direct contact (phone or mail) is a time-consuming and costly effort.

  • For most products or services, the hit rate is less than 1%. That means, in order to get a new customer, over one hundred contacts are required.

  • By mapping the current customer list to a general population database (e.g., SMR Residential Database that contains 12 million listed households), CHAID Analysis can find the household clusters that have much higher incidence rates than the average.

  • By concentrating on these household clusters, the actual hit rate can be dramatically raised. The result is “Fewer phone calls or mail pieces with higher sales returns!”.

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