Why Data Quality Management ?

It impacts your bottom line...

$600 billion dollars annually. That’s what poor data quality costs American businesses, according to the Data Warehousing Institute. To ensure overall operational effectiveness, key decision makers need to have access to complete and accurate data. Manual data validation is prone to errors, duplication, conflicts, and missing information that can lead to cost overruns, project delays, and wasted resources.

What makes managing data quality so difficult?

Lack of Metrics & Oversight

Business owners, IT and stakeholders across the business all understand the importance of data. Usually there is clarity on the goal to improve data quality, but not sufficient clarity on how to do it? Data quality measurement is typically non-existent or an IT function at the database level. 
Not being able to define data quality in measurable / quantifiable terms also makes it difficult to manage oversight and establish accountability and ownership of data.

Lack of Clear Ownership

Data is owned by business, however systems and processes managing data are owned by IT. 
The business wants IT to do more to ensure the data is right, and IT expects the business to keep it clean in the first place. IT doesn't know the business context and requirements, and isn't responsible for keeping it compliant. This leaves the business doing visual spot checks and manually fixing errors. The inefficient and reactive process never quite catches up, leading to frequent escalations.

Lack of Automation

When a company does put specific focus on data quality, most of the times it is done manually. Most IT tools don't address the specific business needs and requires technical skills the business may not have. Manual processes become common, where users dump data in excel to do spot checks, taking time away from their primary tasks. The result is high effort and high cost, and is not scalable or sufficient to meet the data quality needs of the business.

Stressed business executive impacted by good data to run her project - DvSum

So how does DvSum help companies fix and maintain their data, mitigate risks and save money?

Introducing DvSum ?