University of Maryland

Data Literacy & Evidence Building

University Of Maryland | Coleridge Initiative

Week 2: Data Linkage


  1. Understand why linkage matters
  2. Learn how to preprocess data
  3. Review the basics of linkage methodology
  4. Understand what can go wrong (and why)

Data linkage is how private sector data companies make money

One of the most important activities for data scientists – and a major reason that they are paid so much – is linking data records. It has many terms in computer science, including “entity or reference resolution”, “disambiguation”, “duplicate detection” and “object consolidation”. The simple reason for its importance is that the better a company can connect a variety of different datasets, the more profitable they tend to be – about 6% more profitable according to some estimates. It enables companies to be more efficient in targeting information, resulting in increased sales and lowered costs. That’s a big reason companies ask for so much personal information – our birthdate or our email address – when we visit a website, buy something at a store, or use a social media app. Those types of link keys make it easier to connect individuals across different datasets. It’s so important to companies, that, for example, in 2022 when Apple changed its privacy settings for app tracking to be opt-in rather than opt-out, Facebook reported that the 2022 cost in sales would be about $10 billion. READ MORE

Data linkage is critical for evidence building

Source: Commission on Evidence based Policymaking

The same reasoning applies to the public sector. As surveys have become more poorer in quality and more expensive, governments have pivoted to using that are trying to target policies to be most helpful to those who need them, without wasting tax payer dollars, need the best data possible. Bringing that point home, the Commission on Evidence based Policymaking identified three key functions of the National Secure Data Service: data access, privacy AND data linkage. The reason is that administrative data are limited in what data are collected. In our use case, education data have no information about the jobs that graduates get so they have to be linked to unemployment insurance wage records if students are to learn what earnings they can expect to get from different majors, or schools want to learn whether their graduates are getting “good”. READ MORE

Understanding the problem

The problem is deceptively simple. There are two files: A and B. The data scientist has to look at each row in data file A and compare it to each row in data file B. Then she has to decide if it’s a match or not. There are a series of challenges. The first is scale. If there are 25 rows in each file, that means that the scientist has to make 625 pairwise comparison. If there are 100 in each file, then there are 10,000. Obviously this is not a problem that can be done by humans. So you have to tell a computer what to do. In telling the computer what to do, you first have to standardize the data in each file (pre-processing). Then you have to decide when to tell the computer what is a match and what is not a match – whether the pair of rows in each of the sets in the figure are correct or not. That requires developing a model which gives the computer a set of rules, or uses probabilistic linkage methods, or uses machine learning methods to make the decision. READ MORE

Setting the stage: Basics (and importance) of preprocessing

An Introduction to Probabilistic Record Linkage (John “Mac” MacDonald

Before we can successfully link data from different sources, there is an essential step that must be taken: preprocessing. Preprocessing is the process of cleaning and organizing data to prepare it for further analysis. It’s like preparing ingredients before cooking a meal – we need to wash, peel, and chop before we can start combining them. Similarly, data from different sources might come in different formats, have missing or incorrect values, or contain inconsistencies. Preprocessing helps address these issues, ensuring that the data is in the best possible shape before we attempt to link it. This step is crucial because the quality of the preprocessing work can significantly impact the accuracy and usefulness of the linked data. Poor preprocessing might lead to errors or biases in the final dataset, potentially misleading our analysis. Thus, understanding the basics of preprocessing and its importance is a vital part of the data linkage process. READ MORE

How linkage works: rules, people, and computers

Data linkage is a process that involves rules, people, and computer systems working together. The rules define how data from different sources should be matched or linked. For example, data might be linked based on common identifiers like social security numbers or names. People are involved in establishing these rules, ensuring data quality, and interpreting the results. Computers, of course, perform the actual linking, rapidly comparing large amounts of data according to the defined rules.

One fundamental concept in data linkage is the idea of “joins”. Joins refer to the ways in which data from different sources can be combined. There are four main types of joins: Left, Right, Inner, and Outer. A Left Join includes all the records from the left dataset and any matching records from the right dataset. If there is no match, the result is NA on the right side. A Right Join, conversely, includes all the records from the right dataset and any matching records from the left dataset. If there is no match, the result is NA on the left side. An Inner Join only includes records that have matching values in both datasets. An Outer Join includes all records from both datasets, filling in NAs for missing matches. Understanding these different types of joins and how to use them is a key part of successful data linkage. READ MORE

What can go wrong

Data linkage provides massive opportunities.  It provides new ways of describing how individuals move into and out of education, as well as how they move into and out of jobs. But combining data also comes with its own set of challenges. The focus is often on incorrect classifications: 1) false positives – when a decision is made that two records belong to the same person when in fact they belong to different people and 2) false negatives – when the decision is made that two records belong to different people when they in fact belong to the same person (17).   But one of the most critical issues to consider is missing data, and its interpretation. When we link data from multiple sources, the chances of encountering missing data increase. In our use case, people might not show up with earnings because they got a job in a different state, for example. There are a LOT of potential biases to look for. READ MORE

Food for thought

As David Hand points out(1)

Challenge 12. Be aware of the risks that are associated with linked data sets and the potential effect on the accuracy and validity of any conclusions. Recognize that quality issues of individual databases may propagate and amplify in linked data. Develop better measures of overall combined data quality. Challenge 13. Continue to develop statistically principled and sound methods for record linkage and evidence assimilation, especially from non-structured data and data of different modes.

Challenge 14. Develop improved methods for data triangulation, combining different sources and types of data to yield improved estimates.



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