Precisely Data Matching and Entity Resolution vs Splink
Precisely Data Matching and Entity Resolution
precisely.comSplink
moj-analytical-services.github.ioSide by side
| Figure | Precisely Data Matching and Entity Resolution | Splink |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Identity Resolution Software | 7.2 (#19) | 8.4 (#2) |
| Purpose | Connects records across systems that refer to the same real-world entity to create a trusted view of customers, suppliers, assets, and other entities. | Splink is a Python package for probabilistic record linkage that deduplicates and links records without unique identifiers. |
| Support | Precisely offers customer support plans, including 24/7 severity support, and a portal with documentation, downloads, API information, and forums. | Users are directed to a public GitHub discussion forum for questions after consulting the documentation. |
| Use cases | The maker lists customer 360, anti-money laundering, fraud detection, and confident business decisions as use cases. | The maker lists users across government, academia, and other sectors, including the Office for National Statistics, NHS England, and the Australian Bureau of Statistics. |
Plans
Precisely Data Matching and Entity Resolution
- Data matching and entity resolution solutionsPriced on request
In their own words
Precisely Data Matching and Entity Resolution
Precisely Data Matching and Entity Resolution links records across systems when they refer to the same real-world entity. It is designed to create trusted views of people, households, businesses, suppliers, assets, products, parts, and locations for uses such as customer 360, fraud detection, anti-money laundering, and business decisions. Configurable matching and AI/ML support work across large, complex datasets with less dependence on IT. Precisely solutions can profile data, apply business rules, and enrich records with demographic, firmographic, and location intelligence before matching. Spectrum Quality provides phonetic, distance-based, and country-specific algorithms for B2B and B2C names across more than 200 countries. The solutions work in on-premises and cloud environments; listed platforms include Linux, Windows, web, and self-hosted. Batch file import, household matching, and organization matching are supported. Precisely reports that Schwan’s experienced a 25% decrease in new customer duplicate data. Pricing is available on request. The company was founded in 1968 and is headquartered in Burlington, Massachusetts, USA.
Splink
Splink is a free Python package for probabilistic record linkage, used to deduplicate records and connect them when unique identifiers are absent. Its linkage method follows the Fellegi-Sunter model and can be trained without labeled examples. Matching can include term-frequency adjustments and user-defined fuzzy logic. Splink creates SQL for a selected backend, with documented support for DuckDB, Spark, SQLite, and PostgreSQL. DuckDB is recommended for most users; Spark is recommended for very large linkages or when a Spark cluster is more accessible. The maker says it can run on DuckDB or big-data backends such as Spark for linkages of 100+ million records. It works best with standardized data across several columns that are not highly correlated, rather than a single bag-of-words column. Interactive visualizations help examine and diagnose models, predictions, and clusters. Install with pip or conda, with optional backend-specific installation for Spark and PostgreSQL. It runs on Linux, macOS, and Windows, as well as self-hosted environments.