Modern Data Management Practice
Trusted, governed, AI-ready data at enterprise scale
Master data, data quality, cataloging and governance delivered as one connected program, with AI agents doing the heavy lifting and your stewards making the calls.
The 1-10-100 rule. The cost of a bad record grows tenfold at every stage it goes unmanaged.
Untrusted data gets paid for three times
In cost, in risk and in missed opportunity. These are the outcomes our clients come to us for.
360-degree insight
One trusted view of customers, products, suppliers and locations for reporting and AI.
Digital and e-commerce growth
Accurate, enriched product data that lifts conversion and online sales.
M&A and ERP transformation
Cleansed, harmonized data for faster consolidation, conversion and cutover.
Operational efficiency
Fewer order errors and faster onboarding of items, suppliers, customers and staff.
Compliance and risk reduction
Governed, auditable data for regulatory reporting, privacy and policy.
AI readiness
Well-described, governed data that AI models and agents can find and trust.
One integrated practice, four core disciplines
AI-augmented, built on a modern data foundation, and designed so each discipline strengthens the others.
Master & reference data management
Model, match and harmonize master and reference data into governed golden records across source systems.
Data quality & observability
Profile, cleanse, standardize, deduplicate and enrich data, then monitor quality KPIs continuously.
Data catalog & metadata management
Discover, describe and catalog data assets, glossary and lineage so data is easy to find, understand and trust.
Data governance & stewardship
A business-owned operating model, policies, stewardship and access that manage critical data across its lifecycle.
Where is your data management today?
We meet you at your current level and build a practical path to the next one. Select the level that sounds most like your organization.
Level 2 of 5
Managed
Project-level standards and tools. Quality is handled system by system, not enterprise-wide. This is where most organizations we meet sit today.
Core capabilities
What we deliver in each discipline, how AI changes the work, and the platforms we deliver on.
What we deliver
- MDM strategy, domain prioritization and business case
- Multi-domain data models, hierarchies and cross-references
- Match, merge and survivorship design, deterministic and probabilistic
- Hub integration, inbound and outbound, batch to real-time, ERP and CRM sync
- Stewardship workflows and third-party enrichment (D&B, Experian, Loqate)
- Optimization and migration of existing MDM platforms
How we use AI
- ML-assisted matchingModels tune match rules and score candidate pairs, cutting false positives and manual review.
- Survivorship recommendationsAI proposes best-value attributes with the evidence behind each choice for steward approval.
- Steward copilotNatural-language search, merge explanations and suggested resolutions for review queues.
- Smart source onboardingLLMs map new source attributes and classify product descriptions to the master model.
Medical device manufacturer. A new customer data platform and governance delivered a 360-degree view of the hospital network client base.
Platforms: Informatica IDMC MDM, Reltio, Profisee, SAP MDG, Stibo, Oracle EDM, IBM MDM, Acxiom, Amperity
What we deliver
- Data profiling and DQ assessment against business-defined KPIs
- Rules for completeness, uniqueness, accuracy, conformity, consistency and integrity
- Cleansing, standardization, deduplication and enrichment
- In-pipeline validation with the KPI Data Validator (CI/CD and Airflow)
- DQ scorecards and dashboards, trended over time with steward remediation
- Migration reconciliation for ERP and cloud platform moves
How we use AI
- AI rule generationAI profiles data and proposes DQ rules and thresholds in business language for approval.
- Anomaly detectionML learns normal volume, distribution and freshness patterns and alerts on drift.
- Assisted remediationAI suggests corrections, standardizations and likely duplicates, ranked by business impact.
- Root-cause insightLineage-aware AI traces failed checks back to the source job or system.
Fortune 300 automotive parts retailer. Item, supplier and customer DQ cleansing plus DQ dashboards enabled AI/ML demand and assortment models.
Platforms: Informatica DQ, Ataccama, Collibra DQ, Talend, Monte Carlo, Soda, Bigeye, Databricks, Snowflake, KPI Data Validator
What we deliver
- Catalog platform selection, architecture and implementation
- Automated scanning, classification, collections and domain design
- Business glossary, data dictionary and data product publishing
- End-to-end lineage, with custom bridges where native connectors stop
- Catalog of Catalogs: federating Unity Catalog, Purview, Collibra and Alation into one place to find and trust data
- Certification, search and role-based enablement to drive adoption
How we use AI
- Auto-documentationAI drafts descriptions for thousands of scanned assets from schema, metadata and code.
- Glossary draftingAI proposes business terms mapped to assets, routed to stewards for approval before publishing.
- Lineage from codeAI reads SQL, ETL and pipeline logic in Git and DevOps to generate lineage in bulk.
- Sensitive data classificationML detects PII and sensitive data, then applies tags and policies.
Global life sciences leader. An enterprise catalog federated with Databricks Unity Catalog across North America, EMEA and APAC.
Platforms: Microsoft Purview, Databricks Unity Catalog, Collibra, Alation, Informatica CDGC, AWS Glue, Google Dataplex, Oracle Data Catalog
What we deliver
- Governance strategy, charter and operating model (central CoE plus domain stewards)
- Roles, RACI and decision rights for owners, stewards and custodians
- Policies and standards for privacy (GDPR, CCPA, HIPAA), security, retention and access
- Critical data element identification and stewardship workflows
- Governance KPIs, compliance dashboards and audit readiness
- AI governance: governed, explainable data for models and agents
How we use AI
- CDE discoveryAgents scan sources against your critical data element criteria and nominate candidates with evidence.
- Policy automationAI classifies data and maps it to the right policies and access rules.
- Governance by exceptionAgents monitor compliance continuously and route only exceptions to stewards.
- Steward assistantNatural-language answers on policies, owners, definitions and approvals.
Leading energy producer. Governed air-emissions IoT data streamlined environmental regulatory reporting.
Platforms: Collibra, Microsoft Purview, Informatica Axon and CDGC, Alation, Databricks Unity Catalog, SAP MDG, ServiceNow
AI for data management: augment first, replace only where proven
Agents do the heavy lifting at scale. Your stewards set the rules and govern by exception.
Augment
AI does the first pass, humans review
- Draft glossary definitions
- Propose CDE candidates
- Generate lineage and flag gaps
Accelerate
AI runs continuously, humans spot-check
- Keep definitions current
- Re-scan for new CDEs on change
- Alert on DQ and lineage drift
Replace where proven
AI plus a light governed layer
- Reduce heavyweight tool modules
- Governed metadata store as the engine
- Tools optional, not the system of record
Glossary Agent
Proposes and maintains business definitions with cited evidence.
CDE Discovery Agent
Finds critical data elements by your own CDE definition.
Lineage Agent
Builds and refreshes column-level lineage from code.
DQ Rule & Anomaly Agent
Generates rules, detects anomalies and suggests fixes.
Match & Steward Copilot
Scores matches and explains merges for stewards.
Human in the loop by design. Steward-approved, with evidence and citations, confidence thresholds, full traceability, and vendor-neutral.
Many catalogs? Connect before you replace
Most enterprises already run several catalogs: Unity Catalog, Purview and Glue arrive with the platforms you own, and acquisitions bring their own. We make them work as one governed, AI-ready layer.
Consolidate
Migrate everything into one platform
- Cleanest end state
- Highest cost and longest timeline
- Rarely finishes as new native catalogs appear
Federate
One governed layer over every catalog
- Faster and lower cost than consolidation
- Each catalog stays best at its job
- One place for users to find and trust data
- Central policy, delegated stewardship
Do nothing
Let each catalog run independently
- No upfront effort
- Conflicting metadata and no authority
- Users cannot tell which catalog to trust
Turn existing investments into value
The Modern Data Management Optimization Program is a phased path for MDM, data quality, catalog and governance platforms. Each phase is independently valuable and scoped from what we learn in Baseline.
Baseline
About 8 weeks
- Gap analysis of platform, process and data
- DQ KPIs and metrics baseline
- Technical demos and PoC
- Recommendations and roadmap
Stabilize
About 10 to 12 weeks
- Tune DQ and match rules
- Improve performance, batch toward real-time
- DQ KPI monitoring
- Fully documented system
Enrich
About 12 to 16 weeks
- Complementary technologies
- Third-party enrichment and lookup services
- AI agents for stewardship and quality
Expand
About 8 to 12 weeks, repeatable
- Onboard 1 to 3 new sources or domains
- Repeatable ingestion and onboarding
- DQ KPI monitoring
Clients completing this program typically achieve up to a 40% improvement in key MDM and data quality metrics, driving gains in the business metrics that depend on them. Fixed-fee or time and materials per phase, with blended onshore, nearshore and offshore delivery.
Low-risk ways to get started
Fixed-scope entry points that prove value on your own data in weeks, then scale into the Optimization Program or a full implementation.
Data Management Maturity Assessment
- Maturity scoring across MDM, DQ, catalog and governance
- Stakeholder interviews and data profiling
- Gap analysis and target state
- Prioritized roadmap and business case
Outcome: a board-ready roadmap
Request an assessmentData Quality QuickStart
- DQ tool setup in up to 2 environments
- Profiling and cleansing on 1 source and domain
- 10 to 15 business DQ rules
- 3 to 4 day hands-on workshop and guides
Outcome: a measured DQ baseline and trained team
Ask about QuickStartCatalog & Governance Jumpstart
- Catalog configuration and scanning for a pilot domain
- AI-drafted glossary and descriptions
- End-to-end lineage for priority reports
- Stewardship and operating model design
Outcome: a live, governed pilot domain
Ask about JumpstartAI for Data Management PoC
- One agent: Glossary, CDE, Lineage or DQ
- Your rules encoded as agent policy
- Run on a bounded set of real sources
- Scored results and a scale plan
Outcome: a clear go or no-go on AI value
Ask about the PoCProven across industries
Selected engagements from our Modern Data Management portfolio.
Customer data management platform
Order failures cut with a 360-degree view of the hospital network.
Cloud data lake for marketing
Click-through gains from automated data engineering for marketing models.
Item classification for e-commerce
Annual sales lift in a key category from accurate item classification.
Strategic DQ cleansing
Click-through lift and AI/ML demand models enabled by item data cleanup.
Supplier data management
Suppliers deduplicated into golden records in 4 months for a cloud ERP move.
Sales tracing analytics
ML-based data quality and distributor matching for sell-through analytics.
Reporting governance
Automated reconciliations and governance for compliance reporting.
Emissions data governance
Fines avoided with governed IoT emissions data for regulatory reporting.
Why KPI Partners for modern data management
Governance depth and hands-on AI delivery, from one accountable team.
Integrated practice, not point projects
MDM, data quality, catalog and governance delivered as one connected program with shared KPIs.
AI-augmented delivery
Glossary, CDE, lineage and DQ agents compress months of manual effort into weeks, with humans in the loop.
Vendor-neutral and platform-fluent
Certified across Purview, Unity Catalog, Collibra, Alation, Informatica, Databricks and Snowflake.
Accelerator-led, proven playbook
Discovery and sizing models, DQ rule libraries, the KPI Data Validator and deliverable templates.
Flexible, cost-effective delivery
Blended onshore, nearshore and offshore teams as projects, pods, staff augmentation or managed services.
Strategic partnerships: AWS, Microsoft, Google Cloud, Databricks, Snowflake, Oracle, ServiceNow, Salesforce
Let's baseline your data management
Tell us a little about your data landscape. We'll set up a working session to assess your maturity and map your fastest path to trusted, AI-ready data.
Expect a reply from our Modern Data Management team within one business day.
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