Data Quality Tool (DQT)

Clean Data – A prerequisite for your business success

Cost savings, reputation and legal compliance through higher data quality

Today’s business world is dominated by digital data processing. Smooth data exchange and efficient processes require clean and accurate master data. However, the problems start with the collection and retrieval of the data: Incomplete information from customers, incorrect entries due to similar sounding names, careless and typing errors during input, missing information in optional fields or missinterpreted content often occur systematically. In most cases, immediate adjustment is not feasible for time and money reasons.

1. IMPROVEMENT

  • Analysis and Structuring
  • Cleansing and preparation
  • Unification and standardization
  • Error detection and correction
  • Verification and validation

2. CONTEXTUALIZATION

  • Identification of similarity (resulting from the input, semantics, phonetics)
  • Duplicate checking and merging
  • Comparison with internal data sources

3. ENRICHMENT

  • Enrichment with additional information
  • Comparison to external data sources (PEPs, Terror)
  • Linking to other databases

Master data is further subject to ongoing and constant changes and – if not maintained properly – can easily result in data chaos: customer entries (base data) may be recorded repeatedly (duplicate data entries), suppliers may be changed or entire companies may be merged or taken over. DQT supports companies in this process to ensure clean, accurate, correct and consistent master data in order to avoid unnecessary costs and to maintain legal compliance.

The system offers diverse application possibilities ranging from customer master data (CRM), customer and article master data (e-commerce) to components and semi-finished products with different item/part numbers (ERP). Based on comprehensive research and innovative algorithms econob provides – highly performant and scalable – accurate data and supports companies to comply with future data legislation (Data Protection Regulation (EU) 2018).

Download: DQT_Flyer

Use Cases

Data Analysis and Field Recognition

Identification of unknown data fields using the header and content information

Data Preparation

Cleaning, standardization, completion, correction, and validatation of data sets

Similarity Detection

Elaborated comparison of data fields and complete data entries

Duplicate Identification

Identification of (near-)duplicates within a dataset

Screening of Terrorist Individuals

Verification of records against official lists of terrorist individuals

Examination of Politically Exposed Persons (PEPs)

Verification of records against official lists of Political Exposed Persons (PEPs)

Data Quality Tool (DQT)

Improve, contextualize and enrich enterprise data

Customized API

Integrate our services into your software landscape

InfoSpoon

Enrich, model, analyze, visualize and integrate textual data from various sources in real-time

ATRAP

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