Data in Data Warehouse and its Qualities Issues
Arif Ali Wani1, Bansi Lal Raina2
1Arif Ali Wani, Department of Computer Science and Engineering, Glocal University, Saharanpur, India.
2Bansi Lal Raina, Department of Computer Science and Engineering, Glocal University, Saharanpur, India.
Manuscript received on 28 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1753-1756 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8629078919/19©BEIESP | DOI: 10.35940/ijitee.I8629.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data quality (DQ) is as old as the data is. In last few years it is found that DQ can’t be ignored during the process of data warehouse (DW) construction and utilization as it is the major and critical issue for knowledge experts, workers and decision makers who test and query the data for organizational trust and customer satisfaction. Low data quality will lead to high costs, loss in the supply chain and degrade customer relationship management. Hence to ensure the quality before using the data in DW, CRM (Customer Relationship Management), ERP (Enterprise Resource Planning)or business analytics application, it needs to be analyzed and cleansed. In this, we are going to find out the problem regarding dirty data and try to solve them.
Index Terms: Data Warehouse, Data Quality, Customer Relationship Management (CRM), Enterprise Resource Planning (ERP).
Scope of the Article: Data Management