More often, answering a research question requires merging data from different data sources. In the social sciences and medicine, these are often data about the same institutions, individuals or companies. The merging of such data is called “record linkage”. Record Linkage is the term used to describe various automatic procedures that can be used to identify cases in records that represent the same object in the real world. This is necessary, for example, when merging multiple data sources (deduplication) or during data cleansing.
Duplicates can arise, for example, from input and transmission errors, because of different spellings and abbreviations or because of different data schemes. For example, addresses can be recorded in an address database from different sources, wherein the same address can be recorded multiple times with variations. By means of duplicate detection, these duplicates are now to be found out and the actual addresses identified as objects.
Types of duplicates
There are two types of duplicates:
Identical Duplicates – Here all values
are identical. The detection and cleanup are trivial, the excess duplicates can be easily deleted without loss of information.
Non-Identical Duplicates – Here one or more values
are different. The second case is more difficult and complex since the duplicates cannot be identified by a simple actual-equal comparison as in the first case. The surplus data records cannot simply be deleted, they must first be consolidated and the values
summarized.
The process of detecting and consolidating duplicates
The process of detecting and consolidating duplicates can be done in four steps –
Preprocessing of the data
Partitioning the data
Detection of duplicates
Consolidation to a record
Treatment after deduplication
Deduplication processing exploits addresses from deduplication based on customer needs:
For database management, the processing selects and enriches the selected address with the elements present in the other records of the group.
For direct marketing operations, processing allows you to select a single record per group, for example, by giving preference to where it comes from.
Why Record Linkage Software?
The record linkage software provides patented data-length deduplication technology with variable length. This technology not only reduces storage and investment costs but also ensures efficient data transfer across the WAN to remote sites and the cloud.
Record Linkage Process Can Be Divided Into The Following Steps –
Provision Of Data
Standardization Of Key Variables
Calculation Similarities
Manual Merging Of Difficult Cases
Actual Linking The Files
Benefits of Record Linkage Software
Participation structures can be integrated into your system just like any other data and give you the most profound insight into your database.
Calculate the global turnover of a group of companies.
Calculate the global expenses to a group of companies.
Strengthen your negotiation base with suppliers. See the push of a button how high your turnover is in terms of the total sales of the supplier – even within the corporate group.
Monitor all business partners to identify potential financial and reputational risks.
Find the company in a group of companies with significant influence. This brings many benefits to both your suppliers and customers.
Check the beneficial owners as part of your due diligence review and protect your reputation.
Access to standardized financial data will help you evaluate companies around the world.
Uniform accounting format, profit and loss statements and profitability ratios all are checked.
A range of financial indicators and financial strength metrics help you compare across industries and regions.
Expected financial data will give you more valuable insights.
A comprehensive data universe of company information, unique identifiers, excellent coverage also of SMEs and the best calculation and visualization of participation structures you can get on the market.
Work with interfaces to SAP, Salesforce, Microsoft Dynamics as well as your own systems.
Record Linkage Software allows:
To confront one or more files of addresses between them and/or with a database.
To remedy the problems of duplicate addresses.
To enrich the nominative data.
To reduce the costs of multiple detrimental mailings in terms of image.
Data Matching – Data Matching Process can reduce duplication of data and improve data source data accuracy. This allows you to decide which record is considered a match, and to perform appropriate processing on the source data.
Matching Process Has The Following Advantages:
By eliminating the differences between data values
that are considered equivalent, you can identify the appropriate values
and reduce errors that may be caused by data differences. For example, it is common to identify data source data (especially customer data, for example) by name or address, but over time, the data may get dirty or get worse. Identifying and correcting those errors by collation makes the data much easier to use and easier to maintain.
You can unify the display of equivalent values
entered in different formats and styles.
An exact match and a fuzzy match are identified and duplicate data can be removed according to the definition. In this definition of processing, you specify points to consider ambiguous matches as matching, and fields to evaluate and fields not to be evaluated.
You can create a collation policy using a computer-aided process, change it interactively based on the collation result, or add it to a reusable knowledge base.
You can select whether to recreate the index of the data copied from the source to the staging table, depending on the collation policy and the state of the source data. Abbreviating index recreation may improve performance.
The matching process can be performed in combination with other data cleansing processes. This will improve the quality of the data as a whole. Data duplication elimination can also be performed using the function built into the master data service.
The record linkage software offers you many opportunities to update, maintain and enrich your CRM system. It can enric
h detailed financial data, financial indicators, and other risk metrics, calculations, and visualizations of current investment structures, business development data.
