Better Data For Intelligent Decisions
Poor data quality hampers the value of your enterprise information, delivering imprecise and unreliable results. Enable your data to drive your strategic direction by implementing a robust data quality management (DQM) strategy along with AI and Machine Learning capabilities.
By implementing a sound DQM strategy and a set of processes, organizations may measure, monitor, and improve the quality of their data on an on-going basis so that data issues become easily identifiable and managed. Thus, allowing for data-dependent business processes and applications to deliver more accurate insights. Adastra’s DQM services, paired with Machine Learning capabilities for self-healing, set the foundation for a reliable data strategy, catalyzing your digital transformation initiatives.
Benefits of Data Quality Management
Accuracy
Data that is more complete, valid, and up to date, reflecting the real world and maintaining consistent format.
Analytics Efficiency
With high-quality data, analytical models can be deemed more accurate, with less variance, and less overhead from data science effort.
Better Client Relationships
Having an accurate view of your client's profile enables you to develop a better relationship, which leads to better customer attention and reduction in churn.
Cost Savings
Having standardized data that can be consolidated allows accurate data that can be decided upon, without costly financial risk.
Data Quality Methodology
Adastra safeguards 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 the expectation and objectives of your data, driven by business and technical requirements, to help lay the foundation of your 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
- Enrichment
- etc.
Machine Learning
As stewards manually correct data quality issues and exceptions, Machine Learning algorithms will re-evaluate the current automated cleansing rules and workload of Data Quality issues. They will make determinations based on thresholds on whether or not to automatically cleanse Data Quality issues, adjust Data Quality rules, or suggest recommendations for integration efficiencies.
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
