What We Do

Services

Software Development

• Technology Strategy
• Web 2.0 Development
• Product Development
• CRM & Dashboards
• Mobile Application Development
  • iPhone & iPad
  • Android
  • Blackberry
• QA & Testing
• Open Source
• Microsoft Solutions

Data Entry & Process Outsourcing

• Data Entry & Classification
• Video Tagging & Classification
• Company Specific Research
• Lead Research & Qualification
• Content Review & Moderation
• Data reconciliation & Cleansing
• Document Imaging & Scanning
• Online Customer Support

Industries Served Functional Services

Data Reconciliation & Cleansing

Data reconciliation involves comparing related data elements across two or more sources using a combination of manual and automated processes.

1. Ensuring data is correct for calculations
2. Reconciling income statements, invoices, travel & expense receipts
3. Assessing and correcting incorrect data
4. Order & accounts monitoring

Outsourcing data reconciliation tasks frees up your time to focus on analyzing information and making key decisions..

Data Cleansing Services:

Data cleansing or data scrubbing is the act of detecting and correcting (or removing) corrupt or inaccurate records from a database. It involves identifying incomplete, incorrect, inaccurate, irrelevant etc. parts of the data and then replacing, modifying or deleting this bad data.

After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by different data dictionary definitions of similar entities in different stores, may have been caused by user entry errors, or may have been corrupted in transmission or storage.

The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid postal code) or fuzzy (such as correcting records that partially match existing, known records).

High quality data needs to pass a set of quality criteria. Those include:

1. Accuracy: An aggregated value over the criteria of integrity, consistency and density
2. Integrity: An aggregated value over the criteria of completeness and validity
3. Completeness: Achieved by correcting data containing anomalies
4. Validity: Approximated by the amount of data satisfying integrity constraints
5. Consistency: Concerns contradictions and syntactical anomalies
6. Uniformity: Directly related to irregularities
7. Density: The quotient of missing values in the data and the number of total values ought to be known
8. Uniqueness: Related to the number of duplicates in the data