BiG EVAL Description
The BiG EVAL platform offers robust software tools essential for ensuring and enhancing data quality throughout the entire information lifecycle. Built on a comprehensive and versatile code base, BiG EVAL's data quality management and testing tools are designed for peak performance and adaptability. Each feature has been developed through practical insights gained from collaborating with our clients. Maintaining high data quality across the full lifecycle is vital for effective data governance and is key to maximizing business value derived from your data. This is where the BiG EVAL DQM automation solution plays a critical role, assisting you with all aspects of data quality management. Continuous quality assessments validate your organization’s data, furnish quality metrics, and aid in addressing any quality challenges. Additionally, BiG EVAL DTA empowers you to automate testing processes within your data-centric projects, streamlining operations and enhancing efficiency. By integrating these tools, organizations can achieve a more reliable data environment that fosters informed decision-making.
BiG EVAL Alternatives
Plauti brings native data quality to Salesforce and Microsoft Dynamics 365 CE, giving teams the confidence to automate, report, and run AI on records they can actually trust. Nothing is exported to outside servers: everything runs inside your own CRM, and your admins keep full control without filing a single IT ticket.
Data quality with Plauti covers the whole record lifecycle:
Get a live view of data health across every object (table) and field, so the whole team works from one accurate picture
Bulk-clean existing duplicates, standardize formatting, and validate emails, phone numbers, and addresses
Stop bad records before they land, whether they come in through manual entry, imports, the API, or AI tools like Agentforce and Copilot
Put clean data to work right away: route it, distribute it downstream, and let AI act on records it can rely on
Because it's built directly on Salesforce and Dynamics 365 CE architecture, Plauti inherits your existing permissions and security setup. There's no separate login to manage, no delay syncing data between systems, and no compliance blind spots.
Learn more about Plauti
Okyline is an Executable Data Design (EDD) platform focused on executable validation contracts and operational data quality control.
Rather than managing separate specifications, validation code, tests, and monitoring dashboards, Okyline centralizes validation and quality supervision around a single readable executable contract acting as the operational reference for enterprise data flows.
The same contract powers deterministic validation, advanced business invariant checks, multi-format execution, data quality gates, and historical quality analytics across APIs, events, files, LLM structured outputs, and distributed operational systems.
Contracts are designed directly from annotated sample data, making validation rules immediately understandable for developers, architects, QA teams, and business analysts.
The Community Edition includes the public specification, a free Java runtime engine, a Claude AI assistant for contract generation, and an online studio supporting executable JSON validation contracts and JSON Schema transpilation.
The Enterprise Edition adds native validation for JSONL, XML, CSV, FIXED, and EDI flows together with operational quality dashboards and data quality gates, without requiring databases or centralized infrastructure.erprise Edition supports direct validation of JSON, JSONL, XML, CSV, FIXED, and EDI flows with operational quality dashboards and analytics, without databases.
Learn more about Okyline
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
Learn more about DataBuck
Verodat
Verodat, a SaaS-platform, gathers, prepares and enriches your business data, then connects it to AI Analytics tools. For results you can trust. Verodat automates data cleansing & consolidates data into a clean, trustworthy data layer to feed downstream reporting. Manages data requests for suppliers. Monitors data workflows to identify bottlenecks and resolve issues. The audit trail is generated to prove quality assurance for each data row. Validation & governance can be customized to your organization. Data preparation time is reduced by 60% allowing analysts to focus more on insights. The central KPI Dashboard provides key metrics about your data pipeline. This allows you to identify bottlenecks and resolve issues, as well as improve performance. The flexible rules engine allows you to create validation and testing that suits your organization's requirements. It's easy to integrate your existing tools with the out-of-the box connections to Snowflake and Azure.
Learn more about Verodat
Pricing
Free Trial:
Yes
Company Details
Company:
BiG EVAL
Year Founded:
2010
Headquarters:
Switzerland
Website:
bigeval.com/platform/
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
Customer Support
Live Rep (24/7)
Online Support
BiG EVAL Features and Options
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