Best Practices in Data Validation

Data Quality is the buzz word in the digital age.

What is data quality and why is it so important?

“Data quality” is the term that is probably hidden but plays an important role in many streams. Data plays a vital role in acquiring a market place, especially in enterprise data management stream.

Data Quality Examples

Following are some examples which emphasize the need for data quality.

• A customer shouldn’t be allowed to enter his age where he has to mention his marital status.
• When a customer enters a store, there is a high possibility that he might miss out his original details to be filled up with the forms, some of it can be in a hurry not mentioning a correct phone number.
• There is also a possibility of the billing staff to wrongly enter the store address as default in place of the customer address which contributes to a bad quality data that gets persisted in the system.

This data may be crucial as the customer might not just be a Guest customer and the customers’ viable interest towards the store becomes obscure.

This blog post speaks on Data Quality, the significance of Data Quality, business impacts, best practices to be followed, and Mastech InfoTrellis’ specialization in Data validation

Business Impacts on Data Quality

Recent researches from Gartner indicate that poor data quality is a primary reason for about 40% of failing business initiatives.

A low-quality data costs around $600 billion dollars for American businesses alone which in turn causes the failure of any advanced data and technology initiatives.

Significance of Data Quality

The successors of the big business clearly understand the importance of quality data.

The quality of data is directly proportional to the:

• The marketing campaigns cost and the determination of the right audience
• Knowing the customers interest
• Converting the prospects into sales
• The turnaround time for converting a prospect into sales
• The precise business decisions that are made
• How accurately you can make business decisions

The integral part is played by the Quality assurance consultants in revving up the data and ensuring that the data that is consumed by the upstream and downstream are credible.

Data Quality Techniques

For any data to be consumed by the system, the data need to be cleansed to understand the data model of the customer and post cleanse, the data needs to be profiled for a deeper understanding of the data model/ the pattern the data is accumulated.

Learn more @: http://www.infotrellis.com/best-practices-data-validation/