Enable your data to drive your strategic direction by implementing a strong data quality management (DQM) strategy.
Poor data quality hampers the value of your enterprise information, delivering imprecise and unreliable results. By implementing a sound DQM strategy, organization may measures, monitors, and improves the quality of the data on an on-going basis so that data issues become more identifiable, easily and efficiently fixed, allowing for data-dependent business processes and applications deliver expected results.
Adastra’s DQM services set the foundation for a strong data strategy, catalyzing your digital transformation initiatives. Implementing these in tandem with other award winning services, our team ensures your enterise information is always accurate, complete, and well maintained.
89 % of executives agree that inaccurate data hampers an organisation's ability to provide excellent customer experience.
Components of Data Quality Management
Accuracy
Data reflects the real world and maintains a consistent format.
Complete and Timely
Data is available and up to date.
Duplication
The same information occurs in a the same forms across the all datasets.
Integrity
The relationship to another table upheld
Data Quality Methodology
Adastra safe guards your most valuable asset by employing an iterative framework, which is institutionalized and operationalized as part of Data Governance initiatives, developing a solution tailored specifically to your organization.
Understand Data Assets
Through a series of activities, including data classification, metadata collection, data profiling, etc. Adastra will assert expectation and objectives of your data, driven by business and technical requirements to help lay the foundation ofyour DQM Strategy.
Measuring Data Quality
Establishing rules to determine the reliability and validity of data assets, along with thresholds to identify classifications of quality levels; allowing for separate lines of business to maintain consistent assessments of their information.
Monitoring and Reporting on Data Quality
Creating a formalized process to keep track of the level of conformance over time, of the data against the defined rules.
Improve the Level of Data Quality
Creating automated rules and processes driven by technical and business requirements, either through Data Quality tools, or manual intervention and workflow processes such as:
- Filtering
- Parsing
- Cleansing
- De-duplication
- Standardizing
- Enriching
- etc.
Master Data Management
Once the data quality processes have been established and a threshold for the level of data quality can be met, Adastra will implement a Master Data Management strategy as the next step in the process. Leveraging tactics such as de-duplication a single version of the truth is delivered to the organization to provide more reliable and consistent data, among other aspects.
Improving your data quality through cyclical iterations, Adastra will help deliver a framework to meet your organization’s needs and deliver accurate and on-time data.
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Director and Governance Practice Lead
Rahim Hajee
SVP Delivery and Practice Management
Krasen Paskalev
