Data Quality Intelligence: 6-WK POC

One of the greatest challenges for companies today is maintaining accurate, clean, and reliable data. Our innovative solution is designed to address this critical issue by harnessing the power of AI to ensure top-tier data quality within organizations.

Cluster Reply, part of Reply group offers Advanced Data Quality solutions (and thanks to our expertise and professional serviecs), we perform thorough Data Quality controls, apply Data Remediation, and enhance our datasets through Data Enrichment using state-of-the-art AI technologies.

Our solution, integrated within the Microsoft ecosystem, consists of a back-end component and a user-friendly front-end interface. The front-end interface allows users to specify the required controls during Anomaly Detection, accept suggested corrections from Data Remediation, and view a monitoring dashboard of the performed checks.

The solution is modular, comprising three distinct modules:

1. Anomaly Detection: This module focuses on identifying records with anomalies, featuring three main functionalities:
• Deterministic Anomaly Detection: Applies hard-coded checks to identify anomalies.
• Contextual Anomaly Detection: Uses a generative AI engine to automatically detect discordant records.
• Natural Language-Based Anomaly Detection: Applies data quality checks based on natural language commands.
2. Data Remediation: This module aims to provide reliable values for identified anomalies, featuring three key functionalities:
• ML-Based Prediction: Utilizes AutoML models to provide trustworthy values.
• Normalization via Generative AI: Normalizes erroneous data using GenAI.
• Deduplication: Retains only the most informative record in cases of duplicate data.
3. Data Enrichment: Extracts fields from textual descriptions using GenAI

By concentrating on these three core modules - Anomaly Detection, Data Remediation, and Data Enrichment - we effectively identify data inconsistencies, correct inaccuracies, and enrich datasets, enhancing analytics and decision-making.

The Generative AI engine within the solution utilizes Azure OpenAI with the GPT-4o model. Auto-ML models are employed for the Data Remediation phase.

Proof of Concept (PoC)

Based on consumer needs, we offer a 6-8 week Proof of Concept (PoC), focused on the solution’s back-end engine. During the PoC, we will:

• Ingest data from files
• Define Data Quality (DQ) controls
• Execute DQ rules
• Perform associated Data Remediation
• Provide an example of Data Enrichment

An output file in .xlsx format containing the results will be delivered.

Moreover, a deployment plan (including associated costs and timelines) for the industrialization of the back-end engine and the creation of the front-end monitoring system will be provided.