User Tools

Site Tools


concepts:data-quality
  • Bookmark "concepts:data-quality" at del.icio.us
  • Bookmark "concepts:data-quality" at Digg
  • Bookmark "concepts:data-quality" at Furl
  • Bookmark "concepts:data-quality" at Reddit
  • Bookmark "concepts:data-quality" at Ask
  • Bookmark "concepts:data-quality" at Google
  • Bookmark "concepts:data-quality" at Netscape
  • Bookmark "concepts:data-quality" at StumbleUpon
  • Bookmark "concepts:data-quality" at Technorati
  • Bookmark "concepts:data-quality" at Live Bookmarks
  • Bookmark "concepts:data-quality" at Yahoo! Myweb
  • Bookmark "concepts:data-quality" at Facebook
  • Bookmark "concepts:data-quality" at Newsvine
  • Bookmark "concepts:data-quality" at Yahoo! Bookmarks
  • Bookmark "concepts:data-quality" at Twitter
  • Bookmark "concepts:data-quality" at myAOL
  • Bookmark "concepts:data-quality" at Slashdot
  • Bookmark "concepts:data-quality" at Mister Wong

Data Quality

What is Data Quality and why is it so important?

Data Quality or the accuracy, completeness and legal compliance of data is measured and collected uniformly. It reflects all the necessary informations of data. High-quality data is the basis for making the right decisions: to improve the program, manage it effectively and deliver realistic goals.

The quantity of data is always focused on in the traditional database systems. The creation, maintenance and use of large volumes of data will be supported. But we need a database system, which could find the correct answers to our queries using the data in the database. (Data Quality – An overview) Therefore, the Data Quality has become one of the most big challenges to data management.

Data Quality Management

Today there are many companies that want to develop their own data quality management systems to detect and correct errors in the data. According to Wenfei Fan and Floris Geerts (Foundations of Data Quality Management), “the market for data quality tools is growing at 16% annually, way above the 7% average forecast for other IT segments”. But it is not easy to deploy a data quality management. Some challenges must be overcome, include:

• The cross-functional ooperation will be required. • Discipline will be required. • Investments of financial and human resources will be required

http://www2.sas.com/proceedings/sugi29/098-29.pdf

Data Quality Issues

The Data Quality has five largest major issues: data consistency, data deduplication, data accuracy, information completeness and data currency.

Data Consistency: The data has consistency from beginning to end, sooner or later no contradictory and contrary. It represents the validity and integrity of data entities in the real world.

Data Deduplication: Data Duplication reduces processing overhead and storage costs. This data process will eliminate redundant copies of data. http://searchstorage.techtarget.com/definition/data-deduplication

Data Accuracy: In Data Warehouse Data Accuracy is a very important aspect. “It refers to whether the data values stored for an object are the correct values.” http://etutorials.org/Misc/data+quality/Part+I+Understanding+Data+Accuracy/Chapter+2+Definition+of+Accurate+Data/2.3+Data+Accuracy+Defined/

Information Completeness: It assures that the database must be complete and enough to react to the queries.

Data Currency: The current values of entities represented by tuples in a database will be identified. The queries is also reacted with the current values of the data.

Data Quality Tools

There are so many tools which help to evaluate the quality of the data. According to the Best Data Quality Softwares 2017 of G2 Crowd, https://www.g2crowd.com/categories/data-quality, top leading tools are Oceanos, Melissa Data, Listware, Demand Tools and Oracle Data Quality, etc.

Summary

In order to run efficiently the data analysis applications, all organizations require a high level of data quality. The aberrance of data such as inconcistancies, duplicate data or incompleteness will have bad influences on the company results. So we need to have the right methods and tools to improve the quality of the data, generally in life and particulary in business.

Bibliography

1. Wenfei Fan, Floris Gerts: Foundations of Data Quality Management, 2012.

  • Bookmark "concepts:data-quality" at del.icio.us
  • Bookmark "concepts:data-quality" at Digg
  • Bookmark "concepts:data-quality" at Furl
  • Bookmark "concepts:data-quality" at Reddit
  • Bookmark "concepts:data-quality" at Ask
  • Bookmark "concepts:data-quality" at Google
  • Bookmark "concepts:data-quality" at Netscape
  • Bookmark "concepts:data-quality" at StumbleUpon
  • Bookmark "concepts:data-quality" at Technorati
  • Bookmark "concepts:data-quality" at Live Bookmarks
  • Bookmark "concepts:data-quality" at Yahoo! Myweb
  • Bookmark "concepts:data-quality" at Facebook
  • Bookmark "concepts:data-quality" at Newsvine
  • Bookmark "concepts:data-quality" at Yahoo! Bookmarks
  • Bookmark "concepts:data-quality" at Twitter
  • Bookmark "concepts:data-quality" at myAOL
  • Bookmark "concepts:data-quality" at Slashdot
  • Bookmark "concepts:data-quality" at Mister Wong
concepts/data-quality.txt · Last modified: 2017/04/03 22:52 by lethu-n