Proven processes for improving data quality
DataPARC is a proven process enabled by a complete set of tools to take a proactive and holistic approach to managing Data. Discover and explore your data to set metrics, establish the company process, run audits and assign action items to drive data improvements. PARC your data with DvSum to maximize and accelerate value, boost productivity and save money.
Click on each tab to get quick information about the key features of DataPARC
Profile
Data Profiling is a key step in understanding you data within the system landscape and establishing data metrics, KPI targets and determining the required rules. In addition to generating the typical data profiling characteristics, DataPARC also automatically classifies data attributes or metrics to automate discovery and analysis.
Data Exploration is a differentiated capability in DvSum that allows you to discover and explore all the data that exists across your multiple data sources. Visualizations map key statistics and integrates data relationships, profiles and audits all in one place, to quickly improve the understanding of data.
Audit
Data Auditing runs specified rules to validate the quality of data and generate exceptions - not just within single applications, but across and between data sources. DataPARC provides a comprehensive set of rules and enables you to modify templates or to create your own rules.
Rules can be run to manage a wide range of data at different levels including:
- Master Data
- Transactional Data
- Aggregated Data
- Variance
- Plan Quality
- Integrity of data between multiple data sources
- Batch run-time Performance
With Predictive Modeling, you can write your DQ rules in plain English sentences and the system converts it into standardized system based rules that can be executed. It helps increase the engagement and participation from business users
Review
After an Audit run, DataPARC automatically analyzes the data quality issues and assist in finding the root-causes so you can fix them.
For transactional audits, record-level exceptions are generated to take action or assign follow up. For analytical audits, slices of data with largest variances are automatically identified. A history of all audits is maintained for trend analysis and progress tracking.
Comply
The end goal of a data quality process is increasing and sustaining high quality data. With DataPARC, the process is built into the solution to establish a schedule of audits, assign ownership and actions and manage progress. The Data Quality scorecard allows stakeholders and data owners to set targets and milestones to monitor the progress to achieving the goals. Personalized email digests provide immediate visibility and accountability for data quality performance, while built-in issue tracking drives actions and ownership.
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