PRODUCT AND SERVICES DATA CLEANSING
Duplicates
Duplicates refer to two or more identical or very similar entries within a dataset. They can occur in various contexts and for various reasons, but they essentially represent redundant information.
The consequences of duplicate data include:
- Unreliable KPIs
- Increased costs
- Reduction in data integrity
- Operational inefficiencies
Our system searches for duplicate names, descriptions, and numbers on a column and row level.
The user is then given the option to edit, delete, or ignore the duplicates.
Language
- Miscommunication
- Operational inefficiencies
- Integration Issues
- Decreased trust and credibility
Missing Data
Missing Data or “Data Profiling” identifies blank values, field data types, recurring patterns, and other descriptive statistics for an instant 360-degree view of your data. As an example, a data profile can be useful in identifying opportunities for data cleansing and assessing how well your data is being maintained based on various quality dimensions.
The user can drill down and see which product item records are affected, as well as sort, filter, and conceal information about products.
Anomaly Detection
- Inefficient resource allocation
- Compromised data analysis
- False alarms
- Errors in decision-making
- Reduced predictive accuracy
Want to learn more about our services? Have a look at our enrichment, creation, and comparison sections.