- Data Accuracy – the degree to which data reflects the real world
- Data Completeness – inclusion of all relevant attributes of data
- Data Consistency – uniformity of data across the enterprise
- Data Timeliness – Is the data up-to-date?
- Data Audit ability – Is the data reliable?
| Department/End-Users |
Business Challenges
| Data Quality Dimension* |
| Human Resources | The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled | Data consistency, accuracy |
| Marketing | Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email | Data timeliness |
| Customer Service | Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base | Data completeness |
| Sales | Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics | Data consistency, timeliness |
| ‘C’ Level | Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise | Data consistency |
| Cross Functional | Sales and financial reports are not in sync with each other – typically data silos | Data consistency, audit ability |
| Procurement | The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory | Data consistency, accuracy |
| Sales Channel | There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer | Data consistency, accuracy |
- Define and measure metrics for data with business team
- Assess existing data for the metrics – carry out a profiling exercise with IT team
- Implement data quality measures as a joint team
- Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
- Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level
