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Analysis, decision making and strategy formation all depend upon correct information at just the right point in time. In order to reach the best decision and maintain a high level of data quality and information, using data quality management tools is essential. Accessing and extracting data and information become easier if standard tools are used in organizations. These tools can help organizations gain success and achieve goals in the long run while maintaining a good reputation in their respective markets. As an organization grows, the issues with data reliability, data complexities, data format synchronization, data duplication, data redundancy and other topics accumulate throughout the organization. These dimensions should be addressed through appropriate data quality management methodologies including a proper data quality plan and the tools required.

Here are the various features which must be present in data quality management tools:

Data Governance

A sound data quality management tool should be able to define quality and quality management in measurable terms. Governance includes content definition, databases, reference data, calculation engines and reporting tools. A good tool should maintain a uniform level of data quality governance across the organization with a unique platform for reporting across groups.

Visibility of Data Architecture

Good data quality management tools should have the following required features in this context

  • Full support for the framework chosen by the organization
  • The enterprise models must be represented in a manner which allows non-technical stakeholders to understand and relate
  • Traceability of requirements
  • Supporting enterprise features like multi-user collaboration support

Provisions For Data Retention And Archiving

Data archiving and retention features should be present in order to support requests to perform trend analysis, regulatory requirements, etc.

Features Of Standard Data Quality Management Tools

A data quality management tool should be able to handle all the key elements of data quality management, including:

  • Defining and auditing data cleansing rules
  • Data quality planning
  • Data quality checklist
  • Enhancement of data
  • Defining and evaluating rules for data profiling
  • Incorporation of data standards specific to the organization
  • Using data quality rules in the life cycle of data management process to correct source systems

Master Data Management

Data quality management tools should perform master data management which includes data versioning, hierarchies, auditing, continuous merge, and single and multiple copies. This master data management is required to maintain the quality of data and data enhancement when there are data complexities due to heavy integration of software platforms in the system.

Managing The Metadata

The enterprise data quality management applications should address metadata management perfectly. The metadata allows end users to interpret the data elements in the business term. The tools should perform the following functions:

  • Extraction of data from different layers and reporting
  • Representing mappings and reports which were affected due to changes in database structuring
  • Providing reports helpful to data architects in order to maintain complex data warehouses

The author:

RingLead offers a complete end-to-end suite of products to clean, protect and enhance company and contact information, leading to improved organizational efficiency, reliable business intelligence, and maximized ROI on CRM and marketing automation investments.Since 2003 RingLead has helped solve the dirty data problems of large enterprises, Fortune 500 companies and small businesses across the globe.