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RFP Package · Data & Analytics

Master Data Management (MDM) RFP questions and template

122 questions, 10 demo scenarios and a five-vendor scorecard for choosing Master Data Management (MDM) software, in one Excel workbook.

Buy the workbook, from $499Read sample questions

What this package is for

Use it to run a Master Data Management (MDM) software selection, from the first long list to the final scorecard.

What the category covers. Questions for buying master data management software: domain modeling, matching, survivorship, data quality, hierarchies, reference data, stewardship workflow, integration and AI-assisted stewardship, tested on the buyer's own records. Includes 10 demo scenarios written as instructions to shortlisted vendors.

A selection usually runs in three rounds. The package has questions for each:

  • RFI, to the long list. 24 questions screen out products that lack something you need.
  • RFP, to the shortlist. 63 questions ask how each product does the work.
  • Deep dive, to the finalists. 35 questions ask for proof on your own data.

10 demo scenarios tell each vendor what to load and what to show, so every product does the same work in front of you. 90 due-diligence questions cover security, integration, implementation and exit. The scorecard weights the answers and ranks up to five vendors.

Each question comes with why it matters, what a good answer looks like and the red flags, so the people scoring the replies know what to look for.

3 questions from the package

From the RFI round. The first shows part of the guide each question carries; the workbook adds follow-ups, how to verify the answer, a priority and a weight.

1. Which of [domains in scope] can your product master in one hub instance that uses a single shared data model?

Why it matters. If each domain needs its own instance or its own model, the buyer has to run and reconcile several hubs. Links between customers, products, suppliers and locations then depend on custom integration.

Good answer
  • Lists each domain in scope and states that it runs in the same instance and model as the others
  • Explains how an entity in one domain references an entity in another within that model
  • Names any domain that needs a separate instance or engine and explains why
Red flags
  • Calls the product multidomain while customer and product run on separate engines or repositories
  • Answers with a list of prebuilt templates and does not say whether they share one model
  • Cannot say which domains have been run together in a single production instance

2. Describe what your product keeps and what it removes when a steward retires a [domain] record.

Why it matters. If retiring a record deletes it, the buyer loses the audit trail and the link between old source keys and the master record. Transactions in consuming systems that still point to that record then cannot be traced.

3. Which comparison methods can a single match rule in your product combine for [domain] records: exact deterministic comparison, fuzzy string comparison, phonetic comparison, or probabilistic weighting?

Why it matters. With exact rules only, duplicates that differ by typos, abbreviations or word order go unmatched. With probabilistic scoring only, the buyer cannot force a match on an identifier it trusts.

Capability areas

Domain model and mastering styles (11)

Defining entities, attributes, data types, allowed values and record-type attribute sets by configuration across customer, product, supplier, location and other domains; supported mastering styles (registry, consolidation, coexistence, centralized); attribute metadata; and model changes and model versions applied to live data. Out: hierarchies, reference code sets and release promotion, which have their own areas.

Record authoring, search and history (9)

Steward forms that enforce the model, role-based screen layouts, search by any attribute with fuzzy and phonetic tolerance, bulk edits and imports, record retirement, full attribute history and as-of-date views of a record. Out: duplicate prevention logic and approval routing, covered under matching and stewardship workflow.

Matching and entity resolution (12)

Deterministic and probabilistic match rules per entity and source, match scores, auto-merge thresholds and review bands, duplicate checks at record creation, bulk deduplication of existing data, explanation of why records matched, and testing rule changes against existing data before go-live. Out: how the golden record is assembled after a match, covered under survivorship.

Survivorship, merge and cross-reference (11)

Attribute-level survivorship and trust rules, steward match review queues, manual merge, unmerge and re-link, per-attribute source lineage on the golden record, persistent IDs, the source-key cross-reference, and reconciliation of hub records against source systems. Out: match rule design and tuning.

Data quality and standardization (11)

Validation rules on entry and on load, real-time issue flagging, standardization of names, addresses, phones, units and codes, postal address verification, third-party enrichment with source tracking, quality metrics, thresholds, alerts and dashboards per domain and source. Out: routing failed records through approval workflows, covered under stewardship workflow.

Hierarchies and relationships (10)

Corporate family, product taxonomy, territory and household hierarchies; multiple hierarchies over the same records; relationship cardinality and attributes; effective-dated hierarchy versions; rule-proposed relationships; invalid-change prevention; graph display and node moves subject to approval. Out: reference data taxonomies, covered under reference data.

Reference data management (8)

Code sets and taxonomies with their own owners and approval, crosswalks between system code values, validation before publication, multilingual names and descriptions, versioned code sets with scheduled publication and change comparison, and CSV and API import and export. Out: master record hierarchies.

Stewardship workflow and governance (12)

Approval workflows for create, change and retire requests, a visual workflow designer, governance policies enforced as rules, workflow versions and regional variants, a business request portal, task assignment and comments, queue aging and cycle time, scheduled stewardship automation, notifications, data subject request handling across sources, and glossary and catalog tie-in. Out: the generic identity and notification plumbing, covered by cross-cutting modules.

Integration, write-back and syndication (12)

Inbound mapping and transformation, initial bulk load with load reports, batch, real-time and event-driven synchronization, golden-record change events including merge and unmerge, bidirectional write-back to source systems, per-subscriber publication rules, ordered resend after outages, product content syndication to channels, and job monitoring. Out: generic API, SSO and connector-catalog questions, covered by the integration module.

Master data access control and audit (7)

Record visibility and edit rights by role, domain and business unit; attribute- and relationship-level restrictions; masking of sensitive attributes; an audit log that cannot be edited and records views, merges, unmerges and deletes; and retention and purge of record history. Out: platform-level security controls, encryption and SIEM export, covered by the security module.

Scale, performance and release management (11)

Search, match and load performance at the buyer's record volumes, real-time call throughput, concurrent steward load, full rematch after rule changes, restore of a single record, merge or hierarchy to an earlier state, promotion of model, rule and workflow changes across environments as a package, and preservation of configuration through upgrades. Out: availability, failover and disaster recovery, covered by the continuity module.

AI-assisted matching and stewardship (8)

Machine-learning and LLM-assisted match suggestions with steward feedback, natural-language search and stewardship, anomaly and duplicate detection, AI-generated rules or data products, and governed access by AI agents to golden records. Out: model governance, bias, safety and AI cost controls, covered by the AI cross-cutting modules.

Demo scenarios

Each scenario lists the data to load before the demo, then the steps to show, and the questions it scores.

  1. Deduplicating our messiest customer records
  2. Steward fixes a survivorship conflict without help
  3. Undoing a wrong customer merge
  4. Adding a supplier domain by configuration
  5. Business user requests a new customer
  6. Restructuring a corporate family after an acquisition
  7. Publishing a new code set version
  8. Testing a match rule change before go-live
  9. Handling a data subject request for one person
  10. Reviewing AI match suggestions and scoping an agent

Due diligence

The workbook carries the screening questions from these modules. Each module is also sold on its own.

Questions about this package

How many Master Data Management (MDM) RFP questions are there?

122 solution questions in 12 capability areas: 24 for the RFI, 63 for the RFP and 35 deep-dive questions for the finalists. The workbook adds 90 due-diligence questions on security, integration, implementation and exit.

What comes with each question?

Why it matters, good-answer signals, red flags, follow-up questions, how to verify the answer (a demo step, a test or a document), and a suggested priority and weight for scoring.

Can I edit the questions?

Yes. The workbook is an ordinary Excel file. Change, add or remove questions, and change the weights; the scorecard recalculates.

Which license do I need?

The Enterprise License covers any number of evaluations inside one organization. The Consultancy License covers use with any number of clients. Neither allows reselling or republishing the questions.

Before you shortlist

The buyer guide compares the products in this category and what decides between them.

Buyer Guide
Master Data Management (MDM)

For the business side of the same change: