For any organization with an intensive need for consistent data validation, data enrichment services are a simple and strategic approach to higher analytical productivity. Possessing a large database may not mean much for your business if the information contained is incorrect or inaccurate. As one of the top data cleansing outsourcing companies, MM Technologies will optimize the potency of your database, and marketing and sales efforts – resulting in an increased return on investment for your marketing activities.
We help businesses format, classify, modify, replace, organize, delete and correct collected information across multiple data fields. When you outsource data cleansing services to MM Technologies, we help you channel your database resourcefully and devise better targeted sales and direct marketing campaigns.
We rectify all irregularities in data, updating old and obsolete data, performing referential integrity checks, organizing mailing lists, ensuring consistent attribute names and create homogenous pools of data to provide our clients with more effective data.
- Identifying and removing duplicate records
- Identifying and revising irrelevant, inaccurate, incomplete, missing, spurious, invalid, corrupt or obsolete data
- Identifying key variables in an existing database
- Suggesting new variables to enrich a database
- Data auditing and aggregation
- Database cleaning services
- Address Data Cleansing
- Adding missing details such as first and last names, date of birth, telephone numbers and postal codes
- Suppressing information against industry standard files such as MPS, GAS, TBR, NSF etc.
- Enhancing databases with additional information like product attributes, images and manufacturer specifications
- Tagging similar records after a manual review
- Comparing and removing records that march any third party information – e.g. opt-in and opt-out lists
- Matching and correlating data across a number of fields
- Interlinking and consolidating multiple data sources
- Converting CRM systems from a set of records, tables or databases
- Correcting values against a known set of entities, viz. name, gender, pronunciation, address, contact details and lower/upper case specifications
- Rectifying discrepancies with respect to spellings, abbreviations and type errors