Data Quality & Enrichment Agent Clean Trusted Data at Scale

Name: Put It Forward Explained: Head of Product
Uploaded: 2024-07-14T00:00:00+00:00
Duration: 2 min 9 s
Channel: Put It Forward
Description: Put It Forward head of product, Mark Cowan shares a brief overview of the platform, intelligent automation. Focused on how we can make better decisions with the todays data and systems to drive better outcomes.

For data & ops teams - AI detects duplicates, missing values & enrichment gaps then auto-cleanses.

Reduce duplicate records 91% with AI-powered fuzzy matching, confidence scoring & governed merge workflows
Auto-detect missing values, invalid fields & enrichment gaps across CRM, ERP & 500+ connected systems
Trigger cleansing, merge & enrichment workflows with no-code rules - data teams configure in minutes
"2-day implementation" guarantee - Most clients go live in days, not months
SOC 2 + ISO 27001 compliance - Enterprise-grade security and governance built-in

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Trusted by Fortune 500 leaders in financial services, technology, and global enterprise.

Bad Data Costs You Millions - Duplicates, Gaps & Invalid Values Compound Daily

Every duplicate record, missing field, and invalid value silently degrades your analytics, inflates your reporting, and erodes trust in the systems your teams depend on. Manual cleansing runs quarterly at best and catches a fraction of the problems. The Data Quality & Enrichment Agent scans every record across every connected system with predictive AI, detects duplicates, missing values, and enrichment gaps in real time, and triggers governed cleansing, merge, and enrichment workflows - so your data teams stop firefighting quality issues and start delivering trusted data at scale.

Data Quality & Enrichment Agent - Duplicate Detection and Merge Use Case

Detect & Merge Duplicate Records at Scale

Reduce duplicate records 91% with predictive AI fuzzy matching and governed merge workflows across all connected systems.

Scenario: An enterprise has 2.4M customer records across CRM, ERP, and marketing systems. Duplicates inflate reporting by 18%, trigger conflicting outreach, and corrupt analytics. Manual dedup audits run quarterly and catch less than 40% of duplicates.

Solution: The agent applies predictive fuzzy matching algorithms across all connected systems, scoring duplicates by confidence level, auto-merging high-confidence matches within guardrails, and routing low-confidence pairs to analysts with full comparison views.

Results: Duplicate records drop 91% within 60 days. Merge accuracy exceeds 96% with governed confidence thresholds. Reporting inflation shrinks from 18% to under 2% as the agent continuously monitors for new duplicates across every connected system.

Data Quality & Enrichment Agent - Missing and Invalid Value Cleansing Use Case

Auto-Cleanse Missing & Invalid Field Values

Cut missing and invalid field values 78% with auto-detection and governed cleansing rules triggered in real time.

Scenario: A mid-market company's CRM has 35% field incompleteness across contact, account, and opportunity records. Sales reps waste 6+ hours weekly on manual data entry. Invalid phone numbers, outdated emails, and malformed fields degrade campaign ROI.

Solution: The agent scans all records continuously via predictive analytics, detecting missing required fields, invalid formats, and value anomalies in real time - then triggers governed cleansing workflows that auto-correct, flag, or route exceptions to data stewards.

Results: Field completeness rises from 65% to 94% within 60 days. Invalid value rates drop 78% as auto-cleansing rules correct formats in real time. Sales reps reclaim 6+ hours weekly as manual data entry shifts to governed auto-correction workflows.

Data Quality & Enrichment Agent - Enrichment Gap Closure Use Case

Close Enrichment Gaps Across Connected Systems

Close 65% of enrichment gaps within 60 days by auto-identifying incomplete records and triggering third-party enrichment.

Scenario: An ops team runs 40+ integration flows but has no visibility into which records lack firmographic, technographic, or intent data. Enrichment runs manually via spreadsheet uploads, missing 60% of eligible records and creating stale data pockets.

Solution: The agent maps record completeness across all connected systems via predictive analytics, identifies enrichment-eligible records, and auto-triggers third-party enrichment workflows - filling firmographic, contact, and intent data gaps without manual effort.

Results: Enrichment coverage expands from 40% to 87% within 60 days. The agent identifies 15,000+ enrichment-eligible records per month and fills gaps automatically. Stale data pockets shrink 70% as continuous enrichment replaces quarterly batch uploads.

Predict, Decide & Act - How the Data Quality & Enrichment Agent Works

Data Quality & Enrichment Agent Workflow

From reactive quarterly cleanups to predictive, continuous data quality and enrichment in 6 steps - no code, no manual audits, no stale data.

Step 1 - Connect: The agent connects to CRM, ERP, marketing, finance, and data warehouse systems via Integration Designer with 500+ connectors, ingesting records, field metadata, validation rules, and enrichment source configurations in real time.
Step 2 - Analyze: Automated profiling scans all records to establish field completeness baselines, duplicate cluster maps, value distribution patterns, and enrichment gap inventories - creating a unified data quality scorecard across every connected system.
Step 3 - Predict: Predictive analytics run continuously, producing duplicate probability scores, field anomaly alerts, completeness degradation warnings, and enrichment opportunity rankings for every record set in your data environment.
Step 4 - Decide: Business rules and guardrails convert predictions into decisions: auto-merge high-confidence duplicates, cleanse invalid fields, route low-confidence matches for review, or trigger enrichment - all configurable with no code.
Step 5 - Act: The agent executes across systems: merging duplicates, correcting invalid values, filling missing fields, triggering enrichment APIs, updating master records, and notifying teams via Slack, Teams, or ticketing systems.
Step 6 - Learn: Merge and cleansing outcomes feed back into predictive analytics, refining duplicate detection models, recalibrating field validation rules, and improving enrichment targeting accuracy through embedded analytics.

ROI Benefits: Data Quality & Enrichment Agent

Quantified outcomes from replacing manual data cleanup with predictive AI-driven quality detection, governed cleansing & continuous enrichment.

Duplicate Elimination: Reduce duplicate records 91% within 60 days - predictive fuzzy matching with confidence scoring auto-merges high-confidence duplicates within governed guardrails, continuously monitoring all connected systems for new duplicates.
Field Completeness Lift: Increase field completeness from 65% to 94% within 60 days - the agent auto-detects missing required fields and invalid formats in real time, triggering governed cleansing workflows that correct values without manual intervention.
Enrichment Coverage Expansion: Close 65% of enrichment gaps within 60 days - the agent identifies 15,000+ enrichment-eligible records monthly and auto-triggers third-party enrichment workflows for firmographic, contact, and intent data.
Manual Effort Reduction: Cut manual data cleansing effort 83% - data teams shift from quarterly audit cycles to continuous governed automation, reclaiming thousands of hours annually for strategic data initiatives instead of firefighting.
Analytics Trust Improvement: Increase downstream analytics accuracy 34% within 90 days - cleaner, more complete, and continuously enriched data eliminates the silent corruption that inflates reports and misdirects business decisions.

Data Quality & Enrichment Agent Leader

David Hrynk

Director of Program Management

“Having our global teams all working from the same page is critical to our success. Put It Forward exceeded way beyond where others died.”

Uma Asthana

Director of Operations and Technology

“What you just did for our teams' productivity and how we work was magic - you guys are rock stars, I’m truly blown away”

Udo Waibel

CTO

“Put It Forward takes us where no others could - we struggled for years with an enterprise data story - this solved it across the board”

Sarika Saoji

Marketing Platform Technologist

“For me when our internal teams tried to replicate the Put It Forward technology that was when the pin dropped … these are really smart people”

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Why Teams Choose Agentic AI Over Rules, Chatbots, and Manual Work

The Only Option Built for Safe, Explainable, Multi‑System Decisions

17 agent capabilities that matter most when choosing between rules, generic LLM chatbots, and manual processes.

Capability	Put It Forward Agent	Traditional Rules / Workflow Automation	Generic LLM Chatbot	Manual Human Process
Agent execution & scale
No‑Code Agent Configuration	Yes, configure via UI + templates	Limited, technical admin	Limited, prompt‑based only	No
Multi‑System Context Awareness	Yes, native across connected systems	Yes, with complex wiring	No, single‑channel context	Yes, but inconsistent
Data Preparation & Validation	Yes, uses integration layer transforms and validators	Build and maintain logic	No state or very limited	Yes, in people’s heads/spreadsheets
Stateful, Long‑Running Workflows	Yes, native	Limited, brittle state handling	No state or very limited	Yes, in people’s heads/spreadsheets
Enterprise Integration Footprint	Runs on the same governed integration fabric (APIs, services, on‑prem)	Build per system	Channel‑only	System by system
Decision Intelligence & Autonomy
Business Rules + AI Policies	Rules + ML + policy guardrails	Rules only	Ad hoc LLM behavior	Tribal knowledge
End‑to‑End Decision + Action	Yes, orchestrates decisions and API actions across systems	Yes, but static and brittle	Suggests, doesn’t execute across systems	Yes, but slow and inconsistent
Continuous Process Intelligence	Yes, Embedded	No	No	Manual analysis
Autonomy Modes	Simulate, Recommend, Auto‑Act Within Guardrails	Auto‑Act only, no simulation or learning	Suggest only, no structured guardrails	Manual judgment only
Trust, Control & Ops for Agents
Policy & Guardrail Management	Central policies, RBAC, data scopes	Scattered in config and code	Prompt only, no enforcement	Policy documents, inconsistent enforcement
Safe Failure Handling	Native error capture, auto‑rollback/compensation options	Limited, build your own	Opaque failures	Manual investigation & fixes
Agent Performance & Impact Analytics	KPIs, action logs, impact by process (Q2C/O2C)	Basic logs, no business KPI tie‑in	No structured reporting	Manual reporting
Explainability & Audit Trail	Why‑logs for each action, full audit trail	Limited technical logs	Nearly none	Email, tickets, inconsistent records
Agent Extensibility & Integration APIs	APIs/SDKs to embed and extend agents + integration	Varies, often product‑specific	Mostly channel APIs, not orchestration	N/A
Agent Design & Tuning Support	Full design/tuning support and best‑practice playbooks	Self‑serve / ad‑hoc	Self‑serve / ad‑hoc	Self‑serve / ad‑hoc
Agent & Integration Roadmap Alignment	Co‑evolves with connector and system API roadmaps	No / lagging	No / lagging	No / lagging

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IDC Put It Forward Next Generation Business Requirements

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Next Best Customer Agent Activation

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Trigger multi-step changes with simple conversational commands.
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Help business and technical teams co-create smarter, more agile processes.

Conversational AI Agents

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Convert complex business rules from natural conversation into functions
Go faster without having to learn how Put It Forward works at an expert level
Reduce the costs of IT and increase the quality of your data

3-Day Agent Automation Enhancement, Not 3-Month Projects

Put It Forward Workday ServiceNow Integration Project Plan

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We all implement new technology; a transformation or automation project can be simple, targeted, or enterprise-wide.

Accelerate time-to-value and reduce risk with a proven integration plan.

Our proven methodology ensures low-risk, high-impact integrations. Most clients see measurable ROI in the first year accelerated by best practices and enterprise-grade support.

Most clients see improved intelligent automation performance within 48 hours
Zero disruption guarantee - No downtime to existing systems, pipelines or data loads

Implementation timeframes depend on scope and complexity:

Hour 1-2: Configure connection source and destination
Hour 2-36: Business rule configuration and validation
Hour 36-48: Full deployment

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Solution: Agentic AI Orchestration

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Data Quality & Enrichment Agent - Frequently Asked Questions (FAQs)

How does the Data Quality & Enrichment Agent detect duplicates?

The agent applies predictive fuzzy matching algorithms that combine exact, phonetic, and semantic matching across all connected systems. Each potential duplicate pair receives a confidence score based on field similarity, recency, and source reliability. High-confidence matches auto-merge within governed guardrails while low-confidence pairs route to analysts with full side-by-side comparison views.

What types of data quality issues does it detect beyond duplicates?

The agent detects missing required fields, invalid formats (malformed emails, phone numbers, postal codes), value anomalies (outliers, impossible values), stale records, enrichment gaps (missing firmographic, technographic, or intent data), and field consistency violations across connected systems. All detection runs continuously in real time, not in quarterly batch cycles.

How does the agent handle merge conflicts when duplicates have different field values?

Configurable merge rules determine which field values win during every merge: most recent, most complete, highest source reliability, or custom priority logic. Your data team defines these rules with no code through the Process Designer. Every merge decision is logged with full field-level lineage so original values can be recovered if needed.

Can we customize cleansing rules and enrichment triggers?

Yes. Every cleansing rule, validation threshold, enrichment trigger, and merge confidence level is fully configurable through the no-code Process Designer. Your data team defines which fields require validation, which formats to auto-correct, which enrichment sources to call, and which exceptions route for human review - with changes deployed in minutes.

How does the agent explain its quality decisions and merge actions?

Every duplicate detection, cleansing action, and enrichment trigger includes a full decision rationale: the confidence score, matched fields, contributing signals, applied business rule, and merge outcome. Data teams inspect this in the embedded analytics dashboard or export for audit and compliance review.

How does the agent handle model drift and maintain detection accuracy?

Embedded analytics continuously compare predicted duplicates and quality issues to actual outcomes. When detection accuracy deviates beyond configurable thresholds, the agent triggers automated retraining cycles and alerts your team. Quality scoring models improve over time as merge and cleansing outcomes feed back into predictive analytics.

How does Put It Forward handle security and compliance for data quality operations?

Put It Forward is built with enterprise-grade security, including SOC 2 and ISO 27001 compliance, advanced audit trails, role-based access controls, and data encryption at rest and in transit. All record merges, field corrections, and enrichment actions are logged with full chain-of-custody traceability across every connected system.

What ROI can we expect and how quickly?

Clients typically see 91% duplicate reduction and 78% fewer invalid field values within the first 60 days. The 2-day implementation guarantee means the agent begins scanning and profiling immediately, with full duplicate detection and cleansing accuracy typically calibrated within 14-30 days of deployment.