Buy custom 4 Critical Success Factors to Cleansing Data

Essay's paper info
Topic: Technology
Number of pages / Number of words: 10 / 2580
Essay's paper body


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It can also be described as anomalies in the data values that renders a wrong representation. Simply put data impacts:

- Ability to process orders

- Ability to share data among internal systems

- Ability to share data electronically with external sources

- Reliable reports

- Correct information for good decision making

Dirty data manifests itself in many different anomalies, below are just a few:

  • Discrepancies in the structure of the data items and specified format
  • Irregularities
  • Integrity constraint violations
  • Contradictions
  • Duplicates
  • Invalid
  • Missing values (part or whole records)
  • Orphaned data

What causes dirty data?

  • Duplicate or overlapping data from :
  • Business Mergers and Acquisitions
  • Combined products
  • Inherited Legacy Systems
  • Poor or undisciplined data capture
  • Internal static data repositories
  • Automating, merging or implementing new ERP and CRM solutions
  • External file feeds
  • Default entries are used during information gathering for missing data elements
  • Aged data
  • No initiative to maintain clean data

So simply put, “dirty data” creates errors that are having to be manually identified, intercepted and corrected by you, your employees and your customers...


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It can also be described as anomalies in the data values that renders a wrong representation. Simply put data impacts:

- Ability to process orders

- Ability to share data among internal systems

- Ability to share data electronically with external sources

- Reliable reports

- Correct information for good decision making

Dirty data manifests itself in many different anomalies, below are just a few:

  • Discrepancies in the structure of the data items and specified format
  • Irregularities
  • Integrity constraint violations
  • Contradictions
  • Duplicates
  • Invalid
  • Missing values (part or whole records)
  • Orphaned data

What causes dirty data?

  • Duplicate or overlapping data from :
  • Business Mergers and Acquisitions
  • Combined products
  • Inherited Legacy Systems
  • Poor or undisciplined data capture
  • Internal static data repositories
  • Automating, merging or implementing new ERP and CRM solutions
  • External file feeds
  • Default entries are used during information gathering for missing data elements
  • Aged data
  • No initiative to maintain clean data

So simply put, “dirty data” creates errors that are having to be manually identified, intercepted and corrected by you, your employees and your customers...


Essay fragment

General points of the essay

Data Mining Discuss Data Gathering and Presentation Skills of Consultants. Hbl Data Data Putting Data To Work: Intelligently Using Data Mining, Data Warehousing And Databases. Addressing the discrepancies between national and international data: A strategy to improve the statistical credibility of the HDR 4 Critical Success Factors to Cleansing Data Accuracy of Data, Data Ouytput Data Warehouses Data Warehousing And The Information Superhighway Data Warehousing and/or Business Intelligence Data Warehousing Data input Digital Data The impacts of implementing a data warehouse in the banking industry

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