A unique feature to automatically detect duplicate customer records based on input parameters such as First Name, Last Name, Specialty, etc.
How we can help you to manage and integrate customer data.
Do you recognize this:
- Data from an HCP is spread over different systems
- No one system with up-to-date and accurate HCP records
- Record of an HCP differs from system to system
- New data not matched with an existing record
Improving customer data quality:
- consent registrations are compared to existing records
- preventing duplicate records
- merging data leads to golden records
- enrichment of OneKey database with missing HCPs
- matching algorithm according to specifications of client
- consent collected, which cannot be matched (yet) is stored in M&M
Our data quality improvement process consists of 4 steps:
1. Data quality assessment
Your data from different sources should be provided to us according to a defined set up. Based on a quick scan we will determine the quality of your data and present conclusions and next steps.
2. Action plan
In this phase we will define the business rules based on your requirements and the quality of the data. We will present timelines and the proposed budget involved.
3. Data cleaning
Now we will upload your data and our algorithm will compare records from different databases and applications. The results are deduplication of records, merging of records and/or relevant data and the creation of so-called golden records.
4. MedUnite
Algorithm which can be integrated into your IT infrastructure in which HCP data sources are connected and records are automatically matched and merged.
35
%
Deduplication: significant reduction of HCP records
15
%
Consent status: 15% of those records with correct opt-in/opt-out status
10
%
Golden records: 10% turned into complete HCP records
0
%
HCP data consistency in different systems: no more manual merging of data
Where we can help you to integrate
consent and HCP data in one record:
The Match & Merge functionality executes an automatic matching process via an algorithm (based on Initials, Last name, Postal Code, email and Professional Registration number) and in case of a failure in automatic matching, it facilitates the manual matching by making suggestions for matching. Consent collected, which cannot be matched, is recovered because it is stored in Match & Merge until further actions are taken.
Improved HCP
data quality
in a short period of time
