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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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7 top initial steps you need before you start HR predictive analytics

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Top criteria you need before you start analytics in the Human Resource department. I am sure you need many approvals to start analytics in HR.
hr analytics

The risks involved to start analytics in the Human Resource department

  1. You must comply with the legal requirements in which you operate as it relates to the use of people data. The reason is the analytical insights should reflect the cultural and social marks of your organization.
  2. You need to get involved all stakeholders involved and what the cost of what you're doing is relative to the benefit of doing it.
  3. Use analytics through accountable processes, one of which should be acknowledging that using predictive analytics with the workforce has the potential for negative impact, not just positive impact, Walzer said.
  4. Engage the legal department to make sure you understand any implications before you've done something, not after the fact.
  5. Assess whether the use of analytics involves sensitive areas, which it often will, Walzer said. But, she added, these are often accommodated by using reasonable safeguards.
  6. Know what data you just shouldn't collect. 
  7. One example is prescription drug use of employees. "Many employers have access to it through third-party health care providers, but the idea that you're going to bring it in poses a lot of liability to the organization

References

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