Data Quality: Opinions and Impressions Matter the Most

October 18, 2012
24 Views

The perception of quality and the reliability of data assets can change with time. It only takes one incident or error for someone to doubt the information on one’s neatly printed payslip (which is almost always correct.) That one mistake can drive someone to verify every subsequent payslip. It is easy to imagine scenarios where a single bad master data record can disturb a well related high quality data asset. Bad perceptions and negative user opinions can drastically reduce the effective usage of even the most reliable and well integrated data assets.

The perception of quality and the reliability of data assets can change with time. It only takes one incident or error for someone to doubt the information on one’s neatly printed payslip (which is almost always correct.) That one mistake can drive someone to verify every subsequent payslip. It is easy to imagine scenarios where a single bad master data record can disturb a well related high quality data asset. Bad perceptions and negative user opinions can drastically reduce the effective usage of even the most reliable and well integrated data assets.

In Gerald M. Weinberg’s article Agile and the Definition of Quality, the author details how quality can be quite relative and abstract. He argues that the definition of quality is more emotionally and politically driven and is more of a relative thing to individuals.

Putting these thoughts into the data quality perspective, the following bullet points stand tall:

  • Data quality is to be driven from the end users’ point of view – while standards, rules and infrastructure best practices to store and retrieve data enable good data quality, the focus on the ultimate consumers of data should not be lost.
  • Opinions and impressions on data reliability counts – often in organizations, people spread the stories around severity 1 tickets especially if they are related to the reliability of data. History of pains caused by past incidents due to bad data qualityare fresh in the minds of the people affected. Before anyone can trust and reliabilty of the data, the fears or past impressions have to be addressed.
  • Insight into active diverse views of data by participating data consumers – data quality initiatives should encompass frequent examination of how the organizational data assets are seen from many diverse eyes of the end users. The program needs to know which data is most critical, frequently accessed, least understood and depended up on to make decisions.

Overall, data as an asset quickly becomes a liability when it is not used by anyone. So, the opinions, impressions and confidence of the consumers on the organizational data is perhaps the single most critical aspect for a good data quality program.

You may be interested

Big Data is the Key to the Future of Multi-Device Marketing
Big Data
0 shares221 views
Big Data
0 shares221 views

Big Data is the Key to the Future of Multi-Device Marketing

Ryan Kh - May 26, 2017

Digital marketers must reach customers across multiple devices. According to Criteo Mobile eCommerce Report, 40% of all online transactions involve…

Empowering Partners and Customers with Data Insights: A Win-Win for Everyone
Analytics
0 shares273 views
Analytics
0 shares273 views

Empowering Partners and Customers with Data Insights: A Win-Win for Everyone

Guy Greenberg - May 26, 2017

All businesses in the digital age rely on analytics for various activities: Product managers rely on analytics to gain insights…

The State of US Cyber Security
IT
0 shares312 views1
IT
0 shares312 views1

The State of US Cyber Security

bcornell - May 25, 2017

During the first week of May 2017 President Donald Trump signed a cyber security executive order focusing on upgrading government…