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Building Trust

Building Trust

Building trust into data means continuously monitoring and improving data quality in terms of accuracy, completeness, validity and consistency. Trust in data is demonstrated through competence in one’s approach to data stewardship. Stewardship is more than simple oversight by a business owner -- it is a complete lifecycle plan joining activities from business, development and testing. 

The Path to Data Trust:

Understand – Your Data Integrity Needs
Understanding what is quality data and the consequences of losing data integrity is the first step to data trust. From this understanding, develop your data quality goals along with measurable objectives. SMART* objectives is a good method to obtain your goals.  (*Specific, measurable, achievable, realistic and timed)

Innovate – Data Stewardship and Quality
Create an innovative approach that tightly joins data owners, development and testing. Most QA processes lack the tight collaboration across the different disciplines required for data testing. However, innovation does not mean wholesale QA process replacement.  Rather, develop a creative and effective trust approach specific to data integrity that enhances data stewardship and testing processes.

Stabilize – Data Testing Approach
It is inevitable that things don’t go as planned, so allow time for your trust approach to work and make small changes as needed. Use incremental steps that concentrate on the important integrity objectives first. Whether it is new automation technology or a higher degree of collaboration, let your team practice and get used to testing data.

Optimize – Data Trust Processes
Once elements of the process are stabilized, take steps to improve your process. Optimize the existing activities while increasing the capabilities and coverage for verifying data integrity. Also, consider how ongoing validation is achieved or how to migrate processes to new areas of integration.

Check – Measure Data Quality and Act
The keystone to any data trust process is the ability to continuously validate. For data trust, it is an absolute that integrity must be continuously validated. It is difficult to have 100% test coverage and far too easy to implement untested changes into data integration. It is inevitable that data deficiencies will be detected, whether through ongoing regression testing or field reported defects. Use these deficiencies as an opportunity to strengthen your process, not tear it down. 

Key Steps to Improve Data Quality:

  1. Identify the potential sources for bad data within your organization
  2. Establish goals with measurable data quality objectives
  3. Create data integrity controls as a matter of policy
  4. Ensure that quality tests live beyond the project of the day
  5. Build proactive defensives to faulty data
  6. Define more specific collaboration points with all parties
  7. Automate data testing for ongoing integrity verification
  8. Build-in continuous quality improvement process
  9. Invest in the quality of your staff
  10. Cultivate quality attitudes and behaviors

It is important to build a workable process improvement behavior. Process, polices and procedures alone do not change behaviors especially when it comes to improving data quality. There must also be a change in attitude and human behavior. Change is best accomplished through practice; not policy.