Home
Forms Library
The Data Matching Process - Springer

Get the up-to-date The Data Matching Process - Springer 2025 now

The Data Matching Process - Springer Preview on Page 1

Show details

4.8 out of 5

43 votes

Chapter 2 outlines the data matching process, detailing its five major steps: data pre-processing, indexing, record pair comparison, classification, and evaluation. It emphasizes the importance of standardizing data formats and cleaning records to ensure accurate matches between databases. The chapter provides a small example to illustrate challenges in data matching and discusses techniques for indexing to reduce computational complexity. It also covers methods for comparing record pairs and classifying them as matches or non-matches, highlighting the need for careful evaluation of matching quality and completeness.

Here's how it works

01. Edit your form online

Type text, add images, blackout confidential details, add comments, highlights and more.

02. Sign it in a few clicks

Draw your signature, type it, upload its image, or use your mobile device as a signature pad.

03. Share your form with others

Send it via email, link, or fax. You can also download it, export it or print it out.

The best way to modify The Data Matching Process - Springer in PDF format online

Ease of Setup

DocHub User Ratings on G2

Ease of Use

DocHub User Ratings on G2

Adjusting documents with our comprehensive and intuitive PDF editor is simple. Make the steps below to complete The Data Matching Process - Springer online easily and quickly:

Log in to your account. Sign up with your credentials or create a free account to try the product prior to upgrading the subscription.
Import a document. Drag and drop the file from your device or import it from other services, like Google Drive, OneDrive, Dropbox, or an external link.
Edit The Data Matching Process - Springer. Effortlessly add and highlight text, insert pictures, checkmarks, and symbols, drop new fillable areas, and rearrange or remove pages from your document.
Get the The Data Matching Process - Springer accomplished. Download your adjusted document, export it to the cloud, print it from the editor, or share it with other people through a Shareable link or as an email attachment.

Take advantage of DocHub, the most straightforward editor to rapidly handle your paperwork online!

be ready to get more

Complete this form in 5 minutes or less

Get form

Got questions?

We have answers to the most popular questions from our customers. If you can't find an answer to your question, please contact us.

What is the process of data matching?

Data matching refers to the process of comparing two different sets of data and matching them against each other. The purpose of the process is to find the data that refer to the same entity. Many times the data come from two or more different sets of data and have no common identifiers.

What are the different types of data matching?

What are the Different Types of Data Match Algorithms? Deterministic Matching. Deterministic Matching Uses exact rules or identifiers (e.g., SSN, email) to match records. Probabilistic Matching. Fuzzy Matching. AI-Driven Matching.

What are the benefits of DQS matching process?

You define which fields are assessed for matching, and which are not. DQS enables you to create a matching policy using a computer-assisted process, modify it interactively based upon matching results, and add it to a knowledge base that is reusable.

What is the difference between data mining and data matching?

Data Matching: Is considered a precursor to data cleaning and quality analysis, often requiring exact matches or algorithms to identify potential matches. Data Mining: Engages complex algorithms, statistical analysis, and machine learning techniques to extrapolate predictive patterns.

What does it mean when data is matched?

Data matching ensures that duplicate or related records representing the same real-world entity are correctly identified and linked. This process improves data accuracy by eliminating redundancies and inconsistencies, thereby enhancing overall data quality and integrity.

How does data matching work?

Data matching, also known as record linkage or entity resolution, focuses on identifying and linking records that refer to the same real-world entity across disparate datasets. It involves resolving references to entities (such as individuals, products, or organizations) to merge or link related records.

Get the up-to-date The Data Matching Process - Springer 2025 now

Here's how it works