Entity resolution software

Seamlessly link records within seconds across millions of data points from disparate sources. Leverage a suite of fuzzy matching algorithms to yield precise matches with minimal false positives.

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Definition

What is entity resolution?

Entity resolution, is a core data quality process used to identify records that refer to the same entity within or across data sources. This could be done for deduplication and cleansing purposes, or to enrich and create golden records that absorb entity fragments across your business and create a unified entity profile.

As data grows at unexpected speed, the process of entity resolution is also getting complicated. It is difficult to find uniquely identifying attributes residing across databases for the same entities. And so, complex and specialized data cleansing, matching, and merging capabilities are required in fields of crime detection, law enforcement, finance and insurance, etc.

Process

How does entity resolution work?

Ingestion

Bring data together at one place, since it is scattered across disparate sources, and resolve any conflicting changes in database schemas to allow further processing.

Data standardization

Fix data standardization issues highlighted in the previous step, including filling in empty data, replacing inaccurate or invalid information, standardizing values against defined patterns and formats, etc.

Canonicalization

Merge information from duplicate records with the help of canonical rules, so that maximum information is combined into one golden record which represents the completeness of that entity.

Data discovery

Discover and highlight any statistical anomalies that may be present in the form of missing, incomplete or invalid data values.

Entity record linking

Match records within and across databases and identify potential records that relate to the same entity. Datasets usually lack standardized uniquely identifying attributes, and so a combination of intelligent fuzzy matching algorithms may be needed to increase accuracy.

Canonicalization

Merge information from duplicate records with the help of canonical rules, so that maximum information is combined into one golden record which represents the completeness of that entity.

Solution

Let Data Ladder handle your entity resolution process

See DataMatch Enterprise at work

DataMatch Enterprise is a highly visual and intuitive data scrubbing software that has the suite of features to inspect, reconcile, and remove data errors at scale in an intuitive and affordable manner.

DataMatch leverages a plethora of industry-standard and proprietary algorithms to detect phonetic, fuzzy, mis-keyed, and abbreviated variations. The suite allows you to build scalable configurations for data standardization, deduplication, record linkage, enhancement, and enrichment across datasets from multiple and disparate sources, such as Excel, text files, SQL and Hadoop-based repositories, and APIs.

Business benefits

How can entity resolution benefit you?

Customer identity resolution

Reconcile conflicting identities by creating unified customer profiles to confidently track customers across omni-channel interactions.

Enhanced Patient matching

Ensure efficient and timely healthcare diagnosis and treatment by matching patient IDs correctly with EHR records.

Fraud prevention

Detect fraudulent activities such as overdue payments or multiple claims within or across several datasets with unique identifiers.

Lower customer acquisition costs

Remove duplicates from contact lists, CRMs, and databases to avoid marketing expenditure on erroneous and redundant leads.

Regulatory compliance

Accurately match datasets against watchlists to comply with federal including OFAC, KYC, AML, and much more.

Lower time-to-insight

Improve time-to-insight from weeks to hours by saving hundreds of man-hours and complete projects weeks ahead of deadlines.

Let’s compare

How accurate is our solution?

In-house implementations have a 10% chance of losing in-house personnel, so over 5 years, half of the in-house implementations lose the core member who ran and understood the matching program.

Detailed tests were completed on 15 different product comparisons with university, government, and private companies (80K to 8M records), and these results were found: (Note: this includes the effect of false positives)

Features of the solutionData LadderIBM Quality StageSAS DatafluxIn-House SolutionsComments
Match Accuracy (Between 40K to 8M record samples)96%91%84%65-85%Multi-threaded, in-memory, no-SQL processing to optimize for speed and accuracy. Speed is important, because the more match iterations you can run, the more accurate your results will be.
Software SpeedVery FastFastFastSlowA metric for ease of use. Here speed indicates time to first result, not necessary full cleansing.
Time to First Result15 Minutes2 Months+2 Months+3 Months+
Purchasing/Licensing Costing80 to 95% Below Competition$370K+$220K+$250K+Includes base license costs.

Frequently asked questions

Got more questions? Check this out

Simply put, an entity is a single unique object that exists in the real word. Usually, in the realm of data management, the word entity is normally used to describe an individual, customer, employee, product, organization, etc.
As data grows exponentially, a large-scale entity resolution process is required that can: span across multiple sources, work with millions of entities at a time, incorporate differences of data formats and standards, as well as cluster and merge information to prevent data loss.

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