Data Quality

Delivering Quality Data to initiate AI journey

Data quality is a critical aspect of any data-driven organization. It refers to the accuracy, completeness, consistency and timeliness of the data used for informed business decisions.

According to Gartner, poor data quality drains a company on average $8.2 million annually.

Forrester research study reveals that 88% of the companies not able to use data-driven intelligence to guide key business functions and corporate strategies due to the waste, inefficiencies and lost opportunities that dirty data creates.

Data Quality Management

At TuTeck, we follow 4 step AERI (pronounced as “airy”) model to address the Data Quality issues of Enterprises to move it towards 100% data quality compliance. Customer’s Data Quality maturity depends on the state where it belongs

Data Quality Management Framework

At TuTeck, we implement the Data Quality with DMAIT (pronounced dee-mate) framework which iterates withing the following activities to meet 100% DQ Compliance

Define → Measure → Analyze → Improve →Track

Define - Rules, Metrics, Scope, Priority, Feasibility, Goals

  • Perform data Quality surveys on multiple heterogeneous sources with different domains

  • Conduct Q&A sessions with all stakeholders including Source, technical and business SMEs

  • Assess DQ needs and define DQ rules/KPIs/ Metrics for monitoring and improving health of data

Measure - Completeness, Consistency, Duplicity, Accuracy, Integrity

  • Measuring quality of data through different dimensions

  • Performing Data Quality Profiling using either a Data Quality tool or custom queries

  • Creation of the Data Quality Profiling/Audit Reports

Analyze - Categorize, Gap Analysis, Cause, Evaluate, Identify

  • Validate all Data Profiling reports and analyze all data anomalies

  • Analyze and Evaluate the Join analysis, Dependency assessment & Relationship management to identify the reason for redundant/ duplicate data

  • Validate & document all DQ and business Requirements and associated impact on change

Improve - Cleanse, Validate, Standardize, Consolidate, Enrich

  • Perform data cleansing and validation based on business requirement
  • Consolidate data from multiple sources
  • Enrich the quality of data through standardization and other processes using different tools

Track - Dashboards, Reports, Alerts, Audit, Stewardship

  • Monitor the quality of data on an ongoing basis through reports and dashboards

  • DQ KPIs  are continuously pursued to monitor the improvement in overall quality of data

  • Immediate notification alerts to Data stewards, Source SMEs with CDE specific threshold breach information