Companies all around the world are wasting their funds due to the poor data quality. Rationally speaking as the volume of processed data increase, the volume of error data increase too. This diploma thesis explains what is it data quality about, what are the causes of data quality errors, the impact of poor data and the way it can be measured. If you can measure it, you can improve it. This is where data quality tools are used. There are vendors that offer commercial solutions and there are also vendors that offer open-source solutions of data quality tools. Comparing DataCleaner (open-source tool) with DataFlux (commercial tool) using defined criteria this diploma thesis proves that those two tools could be equal in terms of data profiling, data enhancement and data monitoring. DataFlux is slightly better in standardization and data validation. Data deduplication is not included in tested version of DataCleaner, although DataCleaner's vendor claimed it should be. One of the biggest obstacles why companies don't buy data quality tools could be its price. At this moment, it is possible to consider DataCleaner as an inexpensive solution for companies looking for data profiling tool. If Human Inference added data deduplication to DataCleaner, it could be also possible to consider it as an inexpensive solution covers whole data quality process.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:198427 |
| Date | January 2013 |
| Creators | Černý, Jan |
| Contributors | Pejčoch, David, Máša, Petr |
| Publisher | Vysoká škola ekonomická v Praze |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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