Improving Data Quality by Going to the Source (with an assist from Data Governance)

Data Governance is the lifeblood of an enterprise.  Why?  It is mainly because a business cannot stand on its own without its patrons.  These customers vary in demographics; hence, their personal information varies from one another.  The quality of customer information tends to dwindle due to the number of sources where the information comes from. The more we get the information “close to the source”, the better the quality of information that we gather all-together.  

In order to achieve utmost data quality, it has to start from the bottom-up. An enterprise needs to solidify and integrate this information into one silo and create processes and profiles to collect the data upstream.  Effective Data Governance requires that customer information be available where need be at the most opportune time when it is needed. Data should be centralized in a way that it would be easy for the Data Stewards to look for information and make necessary changes where applicable.

We need to gain a complete and timely understanding of our customers in order to effectively gather information. We need to know which information is needed at a given time. When all the needed information has already been collected, it should be compiled and consolidated into one big effective structure that can easily be accessed by people within the organization.  To reduce operational risks, and to lessen situations where customers get irate because of wrong information given to them, Data Quality should (as always) be observed. This regulating body ensures that various processes are being met in order to provide a flawless data structure.

A unified front end system must be employed by every enterprise if they want to maintain the integrity of their systems and their data.  With the overwhelming amount of information from both internal and external customers, Global organizations should start to see Data Governance not only as a single entity in an enterprise but its vital importance to the welfare of the enterprise as a whole.

Data Governance & Data Quality

Obtaining and maintaining credibility is one of the key focus areas of any Data Governance Team.  The type of credibility a data governance team looks for is that they help ensure that the data being entered into a system is complete, accurate, and understood from an enterprise viewpoint.  To manage the lower-level data quality, the birth of a Data Quality Team is usually necessary.  This team would consist of representatives from various departments within the enterprise. Likewise, a Data Quality Team can be composed of totally different sets of people who will check for quality from the outside looking in.

A Data Quality Team is tasked to use software, tools, analysis, and historical knowledge to check for errors within the data structure, ensure that the data being entered is foolproof and ultimately, ensures that data updates are correct and can be pushed live after new sets of information come in.

The following are the main focus areas of a data quality program:

  • Raw Data
  • Classified Data
  • Collected Data
  • Data needed by data stakeholders and data stewards

Because of the increasing demand for accurate data, another role of the Data Quality Team is to stay in constant communication with the various departments within the organization and come up with quality-related procedures or initiatives that will help the enterprise in a global way.

The Data Quality Team is accountable for the following:

  • After gathering all data quality processes/initiatives from the various departments, it is the main responsibility of the Data Quality Team to consolidate these and make it into one large, fluid process that can be applicable to all
  • This team needs to ensure that quality-related updates are being disseminated to concerned and affected departments
  • This team must ensure that there are no quality gaps, inconsistencies, or  variances where data quality is concerned within the system
  • Quality-focused goals should be streamlined to match that of the enterprise’s mission/vision