Data Quality
In today’s data-driven world, the quality of your data is paramount. High-quality data is essential for accurate analytics, decision-making, and overall business success. Poor data quality can lead to erroneous conclusions, misguided strategies, and lost opportunities. Ensuring data quality means maintaining the integrity, accuracy, completeness, timeliness, validity, uniqueness, and consistency of your data across all your systems.
Data Quality Dimensions
At Sparkflows, we know data quality is crucial. That's why we've integrated robust tools into our platform to help you manage, monitor, and enhance your data quality with ease. Whether you're handling large datasets or real-time data, Sparkflows ensures your data is always reliable and trustworthy.
Accessibility
Data is available, easily retrieved and integrated into business processes
Accuracy
Data value accurately reflects the real-world objects or events that the data is intended to model
Completeness
Records are not missing fields, and datasets are not missing instances
Consistency
Data that exists in multiple locations is similarly represented and structured
Precision
Data is recorded with the precision required by business processes
Relevancy
Data is applicable to business processes or decisions
Timeliness
Data is updated with sufficient frequency to meet business requirements
Uniqueness
Each data record should be unique based on how it is identified
Validity
Data conforms to the defined business rules/requirements and comes from
a verifiable source
Data Quality Techniques & Technology
Data Cleansing and Deduplication
Data Matching and Merging(Entity Resolution)
Data Classification
Data Curation and Enrichment
Standardization and Transformation
Data
Profiling
Data Quality Monitoring/
Data Observability
Issue Resolution and Workflow
Business
Rules
Data
Validation
Lineage
Data Catalog/
Metadata
Outlier and Anomaly Detection
Pattern and Trend
Discovery
Data Quality
Prediction
Powerful One-Click Data Profiling
Sparkflows offers out-of-the-box data profiling capability required for understanding data.
Sparkflows Data Quality Features
Custom Validation Rules
Data Masking
Imputation
Data Classification
Skewness / Bias test
Relationship discovery
Deduplication
Consistency Check
Outlier detection
Anomaly Patterns
Data Cleansing
Remediation
Cross Column/Table Analysis
Consistency Check
Fuzzy Matching
Analyze Text Quality
Structural Analysis
Regulatory Compliance Check
Data Classification
Versioning of Data Quality Workflows
Sharing Data Quality Projects
Scheduling by time
Timely Alerts and Notifications
Management Reports
Data Quality Report
Auto Data Quality Tool provides detailed data quality report for the selected dataset.
Overall Data Quality Health
Auto Data Quality Tool provides overall statistics of data validation health.
Data Quality Job Metrics
Auto Data Quality Tool provides operational statistics of Data Quality.
Rule Execution Status
Auto Data Quality Tool also provides execution result details.
Sample Workflows
Sparkflows Data Quality Nodes. It supports an extensive list of great expectation and prebuilt data validation nodes.
Data Quality Impact
Business Benefits
