Data Quality Activity

Data Quality Activity is an activity in the data architecture component that ensures the maintenance of high-quality data in an integrated data resource. Data Quality Process is a process that documents and improves data quality by using both the deductive and inductive techniques. It is a systematic process of examining the data resource to determine its level of data quality and ensuring that the data quality is adjusted to the level necessary to support the business information demand.

Implementing a data warehouse may done in a variety of ways depending on the technical people – data architects, data modelers, database developers, programmers, etc – behind the implementation.

But whatever the approach used in the implementation is, data quality task should always be integrated into the project design and planning. This particular aspect of data warehouse implementation is one of the most crucial and important fact.

When data is poor to begin with, a chain effect of errors could follow down the line and this could cause tremendous disaster not just on the data warehouse but the whole business enterprise in general. Careful planning and design at the beginning of each project can guarantee that the right data quality activities are integrity in the entire project.

There are various types of data quality activities. Some of the most common ones are the following:

Data Justification – This activity involves determining issues related to the impact of data quality during the project presentation of the business problem and the corresponding solution.

Data Planning – In this activity, data quality is considered in setting the project scope, schedule and deliverables. Also included are data quality deliverables in the project charter. It is in this activity that planning for data quality control throughout the project is specified in the aspect of data creating, data transmission from one database to another or from one application to another and data setup, configuration and reference.

Data Designing – This activity involves data quality profiling tools in creating or validating data information models. At this point, the technical people makes sure that those analyzing the data can have access to the data specifications and business rules which define the quality.

Development and Testing – This is an iterative process involving data assessment whose results will be provided for data cleansing and transformation rules.

Data Deployment – After an intensive data quality assessment and data extracts are confirmed to be accurate and consistent, data will finally be loaded to the warehouse and other data stores.

Post Production – This activity involves constant monitoring and implementing metrics in order to regularly check data quality and take quick action whenever a need arises.

The Data Quality Process is more systematic and orderly and complements with the aforementioned activities in order to achieve high quality data. The data quality process helps the data warehouse function to give the company quality data through a comprehensive structured approach that takes data from the source undergoing some key steps in the data cleansing process until the data finally arrives at its target destination and functions in the way that it was intended to be.

In general, a data quality process involves the following generic states: accessing of data, interpreting of data, standardization of data, validation, matching and identifying, consolidation, data enhancement and data delivery or deployment.

Of course there are other stages in the data quality processes but the inclusion or revision of some stages in the process depends on the software application developer. But whatever kind of software application being used to power a data warehouse, the basic thing to remember is that good information is the highly dependent on the quality of data input.

Editorial Team at Geekinterview is a team of HR and Career Advice members led by Chandra Vennapoosa.

Editorial Team – who has written posts on Online Learning.

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