Corporate Data - Critical To Organization's Success

Data is a valuable corporate asset and ensuring its quality is must to an organization's success

. Data quality can be defined as a data that is accurate, valid, timely, consistent and complete. The other way to define data quality is to describe the degree of quality of data needed for each element loaded into the data storehouse.

As data quality is the first and foremost thing an organization needs to put into effect, quality assurance of data refers to the verification of the data to check its accuracy and then correction of the data is made. And if required, this process would further ask for cleansing of existing data. However, it is not certainly possible for any company to rectify all of its uncleaned data with the simple primitive method, effective procedures need to be put in place to guarantee data quality at the source.

In simpler words, we can say that the achievement of this task would only be possible if necessary modifications in business processes and designing data quality into the system are made. The requirements of data quality can be established by identifying each data item and determining its usefulness to the ultimate user of each of the data. Here are few points explained below to ensure data quality in global data warehouse:

Define correct source system

The application system, geographic region and line of business should be treated differently to each other at the time of evaluation or identification of candidate source systems. Most of the times, the variations lie in the same system of same line of business or having similar local market conditions of different region are not minor and negligible . Therefore, the term source system is used in the paper to carry out a unique combination of application system, line of business, and geographic region together.

Individual focus on each data class and selecting source system

The adequacy of a data source for warehouse largely dependent on the data class for which the source system is taken into consideration. The data class can be identified as client, account or transaction or things alike. And the evaluation source system should be conducted in a focused manner for every data class. And to make the evaluation process easily successful, a set of criteria is required to form which may include how the current source data should be, granularity of source data as per the needs of target warehouse, ease of availability of required data, quality and completion of the data.

Conduct Up-Front Assessment of Data Quality in Source Systems

To call the data authentic, there should not be a difference in the definition of source data and the actual data of source files/tables. This mismatch creates disaster for the project team and business users at the next half stage of the project in the result like fail to deliver the objective of the project, delays in projects, lost sponsorship, etc. Therefore, to avoid such scenario, an up-front assessment of data quality in the source system has to be conducted. The assessment is done in light of warehouse data model, warehouse requirements, and any data mapping between the source and the target.

Unreliable data affects the entire organization if the data is used for strategic decision making. Also, the available inaccuracy in the data in a data warehouse will subject to numerous problems to an organization's success. If organizations wish to use data warehouse for competitive advantage or to reap benefits, ensuring data quality is crucial. The benefits of CRM initiatives and data warehousing will only be realized when data quality will be recognized and valued as a corporate asset by each member of the organization.

by: Maneet Puri

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