DATA QUALITY

Data quality is a critical component of business assurance, the practice of reducing risk exposure and improving operational efficiency through business controls and compliance policies. Poor quality data exposes organizations to risk, jeopardizes the performance of operational systems and undermines the value of business intelligence systems on which organizations rely when making key decisions, which can be overcomed by Data Quality Management.

Data quality management incorporates a “virtuous cycle” in which continuous analysis, observation, and improvement leads to overall improvement in the quality of organizational information across the board. The results of each iteration can improve the value of an organization’s data asset and the ways that data asset supports the achievement of business objectives.

This cycle turns on the execution of five fundamental data quality management practices, which are ultimately implemented using a combination of core data services.Those practices are:

• Data quality assessment
• Data quality measurement
• Integrating data quality into the application infrastructure
• Operational data quality improvement
• Data quality incident management

Data quality refers to the condition of a set of values of qualitative or quantitative variables. Furthermore, apart from these definitions, as the data volume increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality often are in disagreement, even when discussing the same set of data used for the same purpose. Data cleansing may be required in order to ensure data quality.

Data Quality: High-impact Strategies.

• The degree of excellence exhibited by the data in relation to the portrayal of the actual scenario.
• The state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use.
• The totality of features and characteristics of data that bears on its ability to satisfy a given purpose; the sum of the degrees of excellence for factors related to data.
• The processes and technologies involved in ensuring the conformance of data values to business requirements and acceptance criteria.
• Complete, standards based, consistent, accurate and time stamped.

There are many elements that determine data quality, and each can be prioritized differently by different organizations. The prioritization could change depending on the stage of growth of an organization or even its current business cycle. Pleasant data quality tools provide a stable and steady mechanism that collects from multiple sources, fills gaps and intelligently reconciles conflicting values to improve IT management.

Data Quality Assessment

Smart organizations want to maximize their investment in data quality management, and this means understanding how poor data quality negatively impacts the achievement of business objectives. By quantifying that value gap, the data quality practitioner can determine the cost-effectiveness, feasibility, and speed of any proposed data quality improvement.

This practice incorporates processes for identifying, assessing, quantifying, and prioritizing data quality issues by Business Impact Analysis, Data Quality Assessment using Data Profiling, Data Quality assessment anomaly review and by defining Measures of Data Quality.

Challenges in Data Quality are

• Issue tracking, Remediation, logging, improvement, Credibility, Definition/Documentation, Metadata, Integrity, Completeness, Auditability, Fitness, Readability and Structure
• The diversity of data sources brings abundant data types and complex data structures and increases the difficulty of data integration.
• Data volume is tremendous, and it is difficult to judge data quality within a reasonable amount of time.
• Data changes very fast and the “timeliness” of data are very short, which necessitates higher requirements for processing technology.
• There are seven characteristics that define the data quality. They are Accuracy and Precision, Legitimacy and Validity, Reliability and Consistency, Timeliness and Relevance, Completeness and Comprehensiveness, Availability and Accessibility, Granularity and Uniqueness, Authorization.

Conclusion:

Till today there is no one step solution available in the market which can solve the entire range of problems. Each of the issues in data quality is unique in it and needs different and unique approach to solve it.

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