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Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Description

Robust algorithms for probabilistic data matching leverage shared identifiers like names, birth dates, gender, addresses, Social Security Numbers, and business names, among others. These algorithms facilitate the importation of data from various desktop and corporate database systems, enhancing versatility. Such data matching software can identify up to 99% or more of all possible matches. For businesses, this capability can translate into substantial additional revenue or significant cost reductions, while also improving fraud detection efforts. In the realm of medical research, effective data matching can determine whether a project succeeds in yielding meaningful findings or ultimately falls short. LinkageWiz stands out as an efficient and user-friendly solution, offering exceptional value by integrating many features typically found in separate products into one comprehensive package, making it a preferred choice for various applications. Furthermore, its streamlined interface allows users with varying levels of expertise to navigate the software with ease.

Description

Sliq is an innovative platform powered by artificial intelligence that swiftly cleans up disorganized raw datasets, making them ready for analysis within minutes by automatically identifying and resolving prevalent quality concerns such as format discrepancies, absent values, schema variations, and formatting mistakes. This efficiency allows analysts and engineers to minimize time spent on tedious maintenance tasks and focus more on deriving insights and building models. By utilizing context-sensitive intelligence, Sliq comprehends the semantic context of the uploaded datasets—whether they pertain to finance, e-commerce, or healthcare—and devises a customized cleaning strategy tailored specifically for each dataset instead of relying on generic solutions. Users have the flexibility to either upload files directly or connect programmatically with existing workflows, and Sliq is compatible with popular data formats like CSV, JSON, and Parquet, ensuring smooth integration into current data environments. Additionally, this platform enhances productivity by streamlining the data preparation process, allowing teams to drive more impactful decision-making through improved data quality.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Parquet No 
Google Sheets No 
JSON No 
Microsoft Excel No 
Polaris No 
pandas No 

Integrations

Apache Parquet Yes 
Google Sheets Yes 
JSON Yes 
Microsoft Excel Yes 
Polaris Yes 
pandas Yes 

Pricing Details

$199 one-time payment
Free Trial Yes 
Free Version No 

Pricing Details

$30
Free Trial Yes 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

LinkageWiz

Country

Australia

Website

www.linkagewiz.net

Vendor Details

Company Name

Sliq

Country

United States

Website

sliqdata.com

Product Features

Data Cleansing

Address/ZIP Code Cleaning No 
Charting No 
Data Consolidation / ETL No 
Data Mapping No 
Multi Data Format Support No 
Phone/Email Validation No 
Raw Data Ingestion No 
Sample Testing No 
Validation / Matching / Reconciliation No 

Product Features

Data Cleansing

Address/ZIP Code Cleaning No 
Charting No 
Data Consolidation / ETL No 
Data Mapping No 
Multi Data Format Support No 
Phone/Email Validation No 
Raw Data Ingestion No 
Sample Testing No 
Validation / Matching / Reconciliation No 

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