With the aim of building a subject-oriented and integrated database, data warehouse is the key ingredient in taking quick and intelligent business decisions. The quality of data and data warehousing is thus a critical concern for the organizations. Moreover, since the data in the warehouse gets collected from multiple disparate sources, it needs attention in standardizing and cleansing the data before loading it to the destination. All in all, data quality is a must-have while dealing with such large amounts of data and maintaining it in a centralized warehouse.
Mainly, data quality focuses on including duplicate-free data, confirming to standards and creating conformed data marts and cleaning data at each stage before final loading. The system must check for any duplicates or near duplicates to insist on clear understanding of the data warehouse and receiving appropriate results when the DWH is queried. Data should also be meaningful while it transfers from one system to another and during formation of OLAP cubes. Further, data cleaning in batch and real-time processing benefits both the business groups and clients on the whole.
The DWH Quality Management:
There are primarily four phases in data quality management lifecycle:
In the Quality assessment phase, the quality of the source data is determined by adopting the process of Data Profiling. Data profiling discovers and unravels irregularities, inconsistencies and redundancy occurring in the content, structure and relationships within data. Thus, you can asses an list down the data anomalies before proceeding further.
The next phase refers to Quality design, which enables business people and groups to design their quality processes. For instance, individuals can enumerate legal data and relationships within data objects complying the data standards and rules. In this management step, the managers and administrators also rectify and improve the data using data quality operators. Similarly, they can also design data transformations or data mappings to ensure quality.
Next, the Quality Transformation phase runs correction mappings used for correcting the source data.
The last phase of this cycle include Quality Monitoring, which refers to the examining and investigating the data at different time intervals and receiving notification if the data breaches any business standards or rules.
Data Profiling process integrates with ETL processes in the data warehouse including the cleaning algorithms and other data rules and schemas specified. It helps users to find:
Such findings will enable you to manage data and data warehousing in better way.
DWH Quality Management Tools
Lately, there are various quality solutions provided to feed data warehouses and improve their inherent quality. Informatica, Pentaho, Talend Open Studio are a few to mention.
Talend Data Quality for a Data Warehouse
Advantages of using Talend Open Studio as Data Warehouse Management tool:
Talend data quality allow users to take advantage of the most time-saving and cost-effective processes as it promotes the following features:
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