DataGroomr Features
What are the features of DataGroomr?
Functionality
- Identification
- Correction
- Normalization
- Preventative Cleaning
- Data Matching
Management
- Reporting
- Automation
- Quality Audits
- Dashboard
- Governance
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DataGroomr Categories on G2
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Data Source Access
Breadth of Data Sources | Provides a wide range of possible data connections, including cloud applications, on-premise databases, and big data distributions, among others | Not enough data | |
Ease of Data Connectivity | Allows businesses to easily connect to any data source | Not enough data | |
API Connectivity | Offers API connections for cloud-based applications and data sources | Not enough data |
Data Interaction
Profiling and Classification | Permits profiling of data sets for increased organization, both by users and machine learning | Not enough data | |
Metadata Management | Indexes metadata descriptions for easier searching and enhanced insights | Not enough data | |
Data Modeling | Tools to (re)structure data in a manner that enables quick and accurate insight extraction | Not enough data | |
Data Joining | Allows self-service joining of tables | Not enough data | |
Data Blending | Provides the ability to combine data sources into one data set | Not enough data | |
Data Quality and Cleansing | Allows users and administrators to easily clean data to maintain quality and integrity | Not enough data | |
Data Sharing | Offers collaborative functionality for sharing queries and data sets | Not enough data | |
Data Governance | Ensures user access management, data lineage, and data encryption | Not enough data |
Data Exporting
Breadth of Integrations | Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools | Not enough data | |
Ease of Integrations | Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools | Not enough data | |
Data Workflows | Operationalizes data workflows to easily scale repeatable preparation needs | Not enough data |
Functionality
Identification | Correctly identify inaccurate, incomplete, or duplicated data from a data source. 21 reviewers of DataGroomr have provided feedback on this feature. | 94% (Based on 21 reviews) | |
Correction | As reported in 21 DataGroomr reviews. Utilize deletion, modification, appending, merging, or other methods to correct bad data. | 94% (Based on 21 reviews) | |
Normalization | Standardize data formatting for uniformity and easier data usage. 20 reviewers of DataGroomr have provided feedback on this feature. | 97% (Based on 20 reviews) | |
Preventative Cleaning | Clean data as it enters the data source to prevent mixing bad data with cleaned data. This feature was mentioned in 20 DataGroomr reviews. | 92% (Based on 20 reviews) | |
Data Matching | As reported in 21 DataGroomr reviews. Finds duplicates using the fuzzy logic technology or an advance search feature. | 96% (Based on 21 reviews) |
Management
Reporting | Based on 20 DataGroomr reviews. Provide follow-up information after data cleanings through a visual dashboard or reports. | 93% (Based on 20 reviews) | |
Automation | As reported in 19 DataGroomr reviews. Automatically run data identification, correction, and normalization on data sources. | 94% (Based on 19 reviews) | |
Quality Audits | Schedule automated audits to identify data anomalies over time based on set business rules. This feature was mentioned in 19 DataGroomr reviews. | 89% (Based on 19 reviews) | |
Dashboard | As reported in 21 DataGroomr reviews. Gives a view of the entire data quality management ecosystem. | 94% (Based on 21 reviews) | |
Governance | As reported in 18 DataGroomr reviews. Allows user role-based access and actions to authorization for specific tasks. | 91% (Based on 18 reviews) |
