The master data management definition describes a disciplined, technology-enabled process that links all critical enterprise data to a single source of truth, ensuring accuracy and consistency across every department, system, and geography. Inconsistent and fragmented data lead to wrong decisions, poor customer experience, and inefficient operations.
In this article, DIGI-TEXX covers the definition, key benefits, best practices, and the 3 most common challenges organizations face when deploying master data management.
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What Is Master Data Management (MDM)?
Master data management (MDM) is the discipline of creating and maintaining trusted records for shared business entities such as customers, products, suppliers and locations. It combines ownership, quality rules, matching and controlled distribution so systems can refer to the same entity consistently. MDM governs core business records; it does not mean collecting all enterprise data in one database. [1]
DIGI-TEXX provides data cleansing, validation and document-processing services that can support the preparation of data for an MDM programme. This guide explains master data, golden records, implementation styles and practical steps for building a master data management strategy. It also separates data preparation from the software, governance and integration responsibilities needed to operate MDM. [6][7]
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What Is Master Data?
Master data describes the relatively persistent business entities used in multiple processes. A customer record identifies the person or organisation buying a service; a product record identifies what is sold; a supplier record identifies whom the business purchases from. These records change over time, but they are distinct from individual purchases, payments and support interactions.
For example, an order is a transaction. The customer and product identifiers on that order point to master records. A country code is reference data used to classify an address. A field definition, data owner or source timestamp is metadata describing the data. Treating these categories separately helps a team define what its MDM initiative should govern.
Customer data can include an enterprise identifier, approved name and relevant contact attributes. Product data can include product identifiers, descriptions and units of measure. Supplier data can include legal-entity identifiers and approved business details. Location data can identify stores, warehouses or service sites. The organisation must decide which fields are authoritative and which are specific to a local process.
Not every field belongs in a universal record. A delivery address may differ from a billing address, and product attributes may vary by market. Preserve those meanings rather than forcing every system to use a single flattened value. For personal or sensitive attributes, collect and distribute only what the approved business purpose requires.
Master Data vs. Transactional Data, Reference Data and Metadata
The examples below are illustrative. Exact classification can depend on the organisation’s data model.
| Data Category | What It Describes | Example | Role In The Workflow |
| Master data | Shared business entity | Customer ID C-104 and approved billing name | Identifies who or what the transaction concerns |
| Transactional data | Business event | Order O-812 for customer C-104 | Records the purchase and quantity |
| Reference data | Permitted classification values | Country or currency codes | Constrains and standardises field values |
| Metadata | Information about data | Field definition source owner and refresh time | Explains meaning provenance and responsibility |
What Is A Golden Record?
A golden record is the trusted representation of an entity produced under agreed matching and attribute-selection rules. It can combine values from different sources while retaining the links needed to trace them. The newest record is not automatically the most reliable record, and a golden record is not inherently error-free. [2]
Consider an illustrative supplier appearing as “Northstar Components Ltd” in procurement and “Northstar Components Limited” in finance. A shared verified identifier may support a match, while an address difference may require investigation. The approved record should retain each source-system ID and the reason for selecting each important attribute. Similar names alone are not enough to merge two legal entities.
MDM Versus Data Management Data Governance And Data Warehouses
Data Management Is The Broader Discipline
Data Management is a broad discipline that covers the entire lifecycle of data, including data collection, storage, integration, security, and usage across systems.
MDM focuses on the shared entity records within that broader discipline. Backup, transaction processing, analytics and integration remain necessary even when the master records are well governed.
Data Governance Sets Accountability
Data Governance defines the policies, standards, roles, and responsibilities for managing data within an organization. It answers questions such as:
Who owns the data?
Who can update or access it?
What rules must data follow?
MDM operates within this governance framework. It applies those rules in practice by enforcing data quality, standardization, and consistency across systems.
A practical governance decision might name procurement as the owner of a supplier’s approved trading status and finance as the approver of a sensitive payment-related change. The MDM workflow should apply those decisions and record exceptions rather than allowing the technology team to resolve business policy disputes informally.
A Data Warehouse Supports Analysis
A Data Warehouse is designed to store large volumes of historical data for reporting and analytics. It aggregates data from multiple sources to support business intelligence and decision-making.
An MDM programme can provide consistent entity identifiers and attributes to a warehouse. It does not automatically fix missing transactions, incorrect measures or flawed analytical models. Microsoft’s documented MDM integration example distinguishes curated master records from the transactional data loaded into an analytics platform. [4]
MDM Is Not The Same As CRM Or Document Management
A CRM manages customer-facing activity; it can be a source and consumer of customer master data. A document management system manages files and their lifecycle. A supplier contract may be stored in a DMS while its supplier ID and approved legal name are maintained through MDM. Link the document to the entity without treating the document repository as an MDM platform.
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MDM vs Data Management vs Data Governance
Master Data Management (MDM), Data Management, and Data Governance are closely related concepts, but they serve different roles within an organization’s data strategy. Understanding the differences helps businesses choose the right approach and avoid overlapping initiatives.
MDM vs Data Management
Data Management is a broad discipline that covers the entire lifecycle of data, including data collection, storage, integration, security, and usage across systems.
MDM, on the other hand, is a specialized subset of data management focused specifically on managing core business entities such as customers, products, suppliers, and locations. Its primary goal is to create and maintain a consistent “golden record” across all systems.
MDM vs Data Governance
Data Governance defines the policies, standards, roles, and responsibilities for managing data within an organization. It answers questions such as:
- Who owns the data?
- Who can update or access it?
- What rules must data follow?
MDM operates within this governance framework. It applies those rules in practice by enforcing data quality, standardization, and consistency across systems.
MDM vs Data Warehouse
A Data Warehouse is designed to store large volumes of historical data for reporting and analytics. It aggregates data from multiple sources to support business intelligence and decision-making.
MDM does not replace a data warehouse. Instead, it ensures that the data entering the warehouse is clean, consistent, and standardized.
| Aspect | Data Management | Data Governance | Master Data Management |
| Scope | All enterprise data | Policies & rules | Core business entities |
| Focus | Storage, processing, security | Control & accountability | Data consistency & accuracy |
| Role | Operational foundation | Strategic control layer | Execution & standardization |
| Outcome | Data availability | Data trust & compliance | Single source of truth |
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How Master Data Management Works
The following workflow is a practical design framework. The sequence and system responsibilities depend on the chosen implementation style; not every programme copies all source data into a hub or sends mastered values back to every application.
1. Identify Sources And Profile The Data
MDM begins by collecting master data from multiple sources such as ERP, CRM, databases, and external platforms.
For each source, record its identifier, owner, fields, permitted uses and update frequency. Profile missing values, invalid formats, duplicate candidates and broken relationships before designing rules. Preserve source IDs during ingestion so reviewers can trace an output back to its origin.
2. Standardise Fields Without Losing Meaning
Normalise fields using an approved model: dates, units, country codes and address components need consistent representation. Preserve original values where required for review. A format check is not proof that the information is factually correct; a well-formed email address may still be wrong for that customer.
Keep changes reversible and auditable. For instance, converting a product’s unit of measure requires an approved conversion rule, not only replacing its label. Values that cannot be mapped confidently belong in an exception queue.
3. Match Records And Review Uncertainty
Use suitable identifiers and matching rules to identify records that may refer to the same entity. Exact identifiers can be strong evidence when they are trustworthy; names and addresses may require contextual comparison. SAP describes matching, match review, rule-based best-record calculation and validation as configurable consolidation steps. [3]
Set separate decisions for automatic matches, manual review and non-matches. Test both false merges and missed duplicates using labelled examples from your own domain. Two people sharing a household address should not be merged simply because their contact details overlap. Keep an authorised unmerge or correction path.
4. Apply Survivorship Rules And Approve The Record
Survivorship rules decide which source value is retained when records conflict. Set rules by attribute, considering source authority, validation, recency and business context. For example, a verified legal name may come from one source while an operational contact comes from another.
Document the selected value, source and approval where appropriate. Missing or contradictory evidence should remain visible. Do not fill unknown attributes with invented values merely to improve a completeness score.
5. Publish According To The Operating Model
Define which systems receive which attributes and how they acknowledge updates. A consolidation programme may serve analytics without changing source applications. A coexistence programme needs conflict handling because both source systems and the hub can participate in maintenance. [2]
Test delayed messages, rejected updates and repeated delivery. If a destination rejects a record, retain the exception and route it to an owner; do not report a successful update merely because the integration sent a message.
6. Monitor Quality And Maintain Ownership
Monitor new exceptions, ageing review queues and recurring errors at source. Microsoft’s documented integration example routes failed validations and uncertain matches to data stewards before records are accepted for use. This illustrates why an MDM workflow needs human responsibility as well as automated rules. [4]
Review rules when products, markets or source systems change. A successful initial clean-up does not prevent new duplicates from entering through an unchanged registration or supplier-onboarding process.
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What Are The Four MDM Implementation Styles?
Registry, consolidation, coexistence and centralised MDM describe different arrangements for maintaining and sharing records. SAP identifies these as common implementation styles. Select the approach around business ownership and application constraints; the strongest central control is not automatically the best fit. [2]
| Style | Where Records Are Maintained | Common Application | Key Decision |
| Registry [2] | Source records linked through an index | Entity visibility across existing systems | Which links are trusted and how sources are accessed |
| Consolidation [2] | Source systems plus a consolidated hub | Shared analytical or downstream view | Whether changes are sent back to operational sources |
| Coexistence [2] | Sources and hub with agreed synchronisation | Shared maintenance across applications | How conflicting updates and failures are resolved |
| Centralised [2] | Master record authoring controlled centrally | Governed creation and distribution | How applications adopt central approvals and availability |
In a registry approach, pay particular attention to cross-reference quality and source availability. In consolidation, test whether users understand that corrected analytical records may not update operational systems. In coexistence, define conflict resolution and update direction. In centralised MDM, plan approvals, application adoption and service continuity before moving authoring responsibility.
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Benefits Of Master Data Management And Their Limits
More Consistent Reporting
Shared identifiers help teams group records by the same customer, product or supplier. Validate that improvement with a representative report: compare how many records remain unmatched and whether business totals reconcile. Consistent master data supports analysis but does not guarantee correct calculations or predictions.
Less Repeated Reconciliation
When teams agree the trusted record and change process, they can spend less time resolving the same conflicts independently. Measure repeat corrections, manual review hours and exception turnaround. Automation only creates value when it handles the right cases and directs unresolved work to someone who can act.
More Reliable Operational Handoffs
Approved product and supplier attributes can help systems exchange records without repeated format and identity corrections. Test an actual workflow from creation through downstream use. A clean central dataset has limited operational benefit if teams continue using ungoverned local copies.
Stronger Traceability
Ownership, source links and change history can support internal reviews and compliance work. They do not establish compliance on their own. Applicable requirements, access rules and retention decisions still need to be defined and implemented by the responsible organisation.
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When Does A Business Need MDM?
Consider MDM when several systems describe the same entities and disagreements repeatedly affect operations: duplicate supplier setup, fragmented customer IDs, incompatible product codes or inconsistent organisational hierarchies. A merger, ERP replacement or multi-channel expansion may expose these problems.
Separate the business need from delivery readiness. The need is the recurring operational problem; readiness means having owners, resources and a realistic integration path. A single application with a manageable list may first benefit from better validation and stewardship rather than a separate enterprise MDM platform.
Before committing, quantify one problem and identify who can approve the rules. If no team can decide what an entity means or which source is authoritative, buying software will not resolve that gap.
Common MDM Challenges And How To Address Them
Conflicting Definitions And Unclear Ownership
MDM requires a designated owner for every data domain. Without data stewards and a governance board, departments dispute which system holds the authoritative version of a record. This ambiguity produces inconsistent updates, conflicting golden records, and eroded trust across the organization.
Agree definitions at the level where decisions happen. A customer account, a person and a household are different entities. Record those distinctions in the data model and escalation process, with an accountable owner for disputed changes.
Poor Source Data And Unsafe Merging
Do not assume a universal percentage of bad records. Profile the actual sources and publish the measurement rules. Start with conservative match decisions, review uncertain cases and investigate the cause of recurring errors rather than repeatedly correcting their symptoms.
Legacy Integration And Synchronisation Failures
Inventory the systems that must consume mastered data, their interfaces and their acceptance rules. Pilot difficult cases such as an identifier change or a retired product. Include monitoring and reconciliation in the scope; an integration that works only during an initial import is not an ongoing MDM process.
Adoption And Maintenance
Users need a clear way to request changes and receive feedback. Slow or opaque approval processes encourage local workarounds. Track review queues and train business teams on ownership, permitted updates and the consequences of creating a duplicate.
When Should You Implement MDM?
Master data management (MDM) becomes necessary when data issues start disrupting daily operations and decision-making. As organizations grow and use more systems, data fragmentation, duplication, and inconsistency often increase.
The following signs indicate that your business may need to implement MDM:
- Clear business objectives for improving data quality or consistency.
- Alignment between business and IT teams.
- Defined data ownership and governance roles.
- Sufficient resources for implementation and ongoing maintenance.
- Measurable data issues that justify the investment.
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How to Build a Master Data Management Strategy: 7 Steps
A master data management strategy is a business-led plan for deciding which shared entities to govern, who is accountable for them, how trusted records will be created and distributed, and how success will be measured. It connects the business objective to the data model, governance rules, implementation style and operating budget. Buying an MDM platform is one possible part of that plan; ownership and reliable maintenance must also be defined.
A useful strategy should produce concrete decisions: the first data domain and use case, the systems in scope, an attribute-level authority map, approval and escalation rules, a pilot acceptance checklist, and a roadmap for expanding after results have been reviewed. Use the following seven steps to develop those decisions.
1. Start With One Measurable Business Outcome
Choose an outcome such as reducing duplicate supplier setups or shortening customer-record review. Define the current baseline, affected workflow, responsible owner and measurement period. Avoid promising a percentage improvement before profiling and a representative pilot.
2. Establish Ownership And Governance
Establish strong data governance: Don’t treat data governance as an administrative task. Build a realistic data governance framework with clear roles, responsibilities, and decision-making processes, supported by leadership.
Name the domain owner, data stewards, technical custodians and users who approve sensitive changes. Agree when a case must be escalated, how evidence is recorded and who can reverse an incorrect merge.
3. Define The Domain And Data Contract
Specify entity definitions, identifiers, required attributes, valid values and relationships. Create an attribute-level authority map and document how updates move between systems. Include the treatment of missing values, historical records and retired entities.
4. Choose An Implementation Style And Toolset
Compare the required authoring, matching, stewardship, integration and audit capabilities. Use the same representative dataset and scenarios for each demonstration. A platform should show how reviewers resolve an ambiguous match, explain a selected value and export records with their source links.
Product lifecycle matters. Microsoft states that Master Data Services is removed in SQL Server 2025 (17.x). Microsoft Purview documentation describes integration with partner MDM solutions; it should not be presented as an automatic replacement for every MDS implementation. Assess the actual installed version and migration requirements. [5][10]
5. Pilot The Entire workflow
Think big, but start small: Build an overall MDM roadmap for the entire enterprise, but start with one data domain (e.g,. products) and a few source systems. Achieving quick wins will create good momentum for the next stages.
Include duplicates, similar-but-distinct entities, missing fields, conflicting values and failed downstream updates. Set acceptance criteria before starting. A pilot should demonstrate approved outputs, review responsibilities, recoverability and downstream reconciliation, not only a high matching score.
6. Plan Costs and Operating Capacity
Compare subscriptions or licences, integration, migration, data preparation, stewardship, reviewer training, storage and ongoing support over the same period. Estimate the workload that requires human review and what happens during a source-system change. This guide does not provide a verified universal MDM price.
7. Train Users and Expand with Evidence
Focus on change management: MDM changes the way people work with data. Therefore, continuous communication about the benefits, project goals and providing thorough training to end users are extremely important to ensure successful implementation.
Expand to the next domain after the first one demonstrates reliable ownership and measurable results. Keep a change log for data rules and explain how changes affect consuming applications. Review the business outcome as well as the technical quality measures.
Master Data Management Strategy Example: A Supplier Pilot
Consider a company whose procurement and finance teams maintain separate supplier lists. The same legal entity may have different names or addresses in each system, while two distinct suppliers may share a similar trading name. The example below is an illustrative planning template, not a DIGI-TEXX client result or a promised delivery schedule.
Start by defining the outcome: make approved supplier identities consistent across the two systems and reduce avoidable duplicate creation. Limit the initial scope to an agreed supplier group and the attributes needed for onboarding. Record the baseline duplicate rate, review effort and unresolved exceptions before making changes, so the team can compare the pilot with the starting position.
Assign procurement responsibility for supplier identity, finance responsibility for the financial attributes it is authorised to approve, and technical teams responsibility for delivery and reconciliation. Document what evidence each decision requires. A source priority rule should not override a verified correction, and a similarity score should not automatically authorise a change to sensitive payment details.
Profile both source lists, preserve their identifiers and prepare candidate matches. Review ambiguous pairs, agree the approved values for each attribute, and retain the source evidence and decision history. Test how an approved record reaches the receiving system, how a failed update is detected, and how an incorrect merge can be reversed without losing the original records.
Before expansion, review the pilot against agreed acceptance criteria: required fields are validated, disputed matches have an assigned owner, downstream records reconcile, and the team can manage the review queue with its available capacity. Set numerical thresholds using the baseline and the consequences of errors. If the pilot fails a critical criterion, resolve the cause before adding another source or domain.
Master Data Management Strategy Deliverables
The strategy should leave the business with a scoped use case, named owners, a data dictionary, an authority and matching rulebook, an integration design, and an operating plan. The pilot report should show baseline and outcome measurements, unresolved risks, review workload, and the decision to expand, revise or pause. These deliverables make the strategy usable after the initial project team has handed over responsibility.
Which MDM metrics should you track
Use a documented denominator and the same sampling method over time. The following are proposed measurement definitions, not DIGI-TEXX performance commitments or universal industry targets.
| Metric | Suggested definition | Interpretation |
| Required-field completeness | Populated required fields ÷ required field opportunities | Do not count invented defaults as valid completion |
| Rule validity | Records passing agreed checks ÷ records checked | Format validity does not prove factual accuracy |
| Confirmed duplicate rate | Excess records in confirmed duplicate groups ÷ records reviewed | Define groups and sample scope consistently |
| False-merge rate | Incorrect merges ÷ reviewed merge decisions | Report sample size and severe errors separately |
| Exception turnaround | Elapsed time from exception creation to approved resolution | Report unresolved backlog age too |
| Distribution success | Updates accepted by destinations ÷ updates due for delivery | Do not count messages merely sent as accepted |
| Business outcome | Time or rework in the selected business process | Compare like-for-like baseline and post-pilot periods |
A lower duplicate rate is not sufficient evidence of improvement if distinct entities have been merged incorrectly. Review false merges alongside missed duplicates and investigate severe errors individually. For accuracy, use a trusted reference or expert-reviewed sample; a format-validation pass alone cannot demonstrate factual correctness.
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How DIGI-TEXX can support data readiness for MDM
DIGI-TEXX’s published data cleansing services include correction, validation and database standardisation. Its document-processing offering is relevant where business inputs first need to be turned into structured records. These capabilities can support an agreed data-preparation scope; they do not by themselves establish a complete MDM software platform or an enterprise-wide governance engagement. [6][7]
Define a reviewable preparation deliverable
A proposed scope could include a profiled dataset, normalised fields, duplicate candidates, approved corrections and an exception register linked to source IDs. Agree the rules, output format, review sample and handoff with the MDM owner before processing. These are suggested procurement deliverables, not a claim that every item is included in a standard package.
For a supplier-data pilot, provide representative source files, the target schema, identity rules and a named approver. Evaluate whether accepted records meet the agreed checks and whether unresolved conflicts are returned with enough evidence for a business decision. Confirm integration and ongoing stewardship responsibilities separately.
Use evidence from the correct service
DIGI-TEXX’s published investment-management scanning case describes 1.8 million A4-equivalent pages, indexing and quality control. It is evidence of a document-digitisation project, not an MDM rollout, entity-resolution benchmark or guarantee of golden-record accuracy. Request a directly relevant reference and a pilot for the proposed data-cleansing work. [8]
The company’s certificate page identifies ISO 27001:2022 and ISO 9001:2015. Review the current certificate, covered entity, scope and validity before procurement. QTSC separately lists DIGI-TEXX’s document and data-processing services; the listing corroborates service presence rather than certifying an MDM product or performance result. [9][11]
Frequently asked questions about master data management
What Is Master Data Management In Simple Terms?
MDM is a way to keep shared business records consistent across systems. It establishes which customer, product, supplier or location a record represents, which values are trusted and who can approve changes. The aim is controlled, reliable reuse of the same entity information.
What Is An Example Of Master Data?
A product record containing its unique identifier, approved description and unit of measure is master data. An order containing a quantity and transaction date is transactional data. The order can refer to the product record rather than redefining the product each time.
What Is The Difference Between MDM And Data Cleansing?
Data cleansing corrects quality problems such as inconsistent formats and duplicate candidates. MDM also defines ownership, entity identity, survivorship and the ongoing sharing of approved records. Cleansing can be part of MDM preparation and maintenance; a one-off cleanup is not the entire programme.
Does MDM Require One Central Database?
No. Registry implementations link source records, while other styles consolidate or centrally maintain master data. Choose the architecture based on where records are authored, who approves changes and how applications consume them. A single source of authority is not necessarily a single physical store. [2]
Who Owns Master Data Management?
Business owners should be accountable for domain definitions and approval decisions. Data stewards maintain quality and resolve exceptions, while technical teams implement access, integrations and system controls. The operating model should name these responsibilities explicitly.
Can AI Replace Data Stewards?
AI can assist with matching and classification, but uncertain identity decisions and policy exceptions still need accountable review. Test false matches, preserve source evidence and define approval boundaries. A model’s confidence score should not be treated as proof that two records represent the same entity.
Which MDM Software Should A Business Choose?
Choose against the domain, architecture and review workflow rather than an unsupported popularity ranking. SAP Master Data Governance and MDM solutions integrated with Microsoft Purview are examples to investigate, not endorsements or an exhaustive shortlist. Confirm product scope, support lifecycle, integrations and ownership of implementation work. [3][10]
Is Master Data Management The Same As Mobile Device Management?
No. The acronym MDM is used for both. Master data management governs shared business-entity records. Mobile device management administers devices and their settings. Specify the full term when comparing products so device-management tools are not mistaken for master-data solutions.
How Long Does An MDM Implementation Take?
Timing depends on source quality, domain complexity, approvals and integration scope. Plan milestones for profiling, rule agreement, pilot review, downstream testing and rollout rather than assuming a universal duration. Estimate expansion only after the first domain provides evidence about the work required.
Does MDM Guarantee Compliance Or Accurate AI Outputs?
No. MDM can improve traceability and the consistency of entity data, but compliance depends on the applicable controls and their operation. AI outputs also depend on other inputs, model behaviour and application design. Treat mastered data as one part of that wider system.Master data management definition is a disciplined process that transforms messy data into a trusted strategic asset for your business. By creating a single source of truth for critical data, businesses can increase efficiency and drive growth based on accurate insights. At DIGI-TEXX, we help you build that solid data foundation. Get in touch to discover how our data processing and analytics services can help build MDM for your business.
Build A Practical Master Data Foundation
Start with one domain, one measurable problem and clear ownership. Agree how records will be matched, corrected, approved and used downstream, then validate the approach with a representative pilot before expanding.
To discuss data preparation with DIGI-TEXX, share your data domains, source formats, expected volumes, target schema, quality issues and review requirements. Explore data cleansing services and document processing services, or contact the team to define the scope. [6][7][12]
If you have any questions or would like expert advice on data analytics services, please feel free to contact us using the information below.
DIGI-TEXX Contact Information:
🌐 Website: https://digi-texx.com/
📞 Hotline: +84 28 3715 5325
✉️ Email: [email protected]
🏢 Address:
- Headquarters: Anna Building, QTSC, Trung My Tay Ward
- Office 1: German House, 33 Le Duan, Saigon Ward
- Office 2: DIGI-TEXX Building, 477-479 An Duong Vuong, Binh Phu Ward
- Office 3: Innovation Solution Center, ISC Hau Giang, 198 19 Thang 8 street, Vi Tan Ward
Reference:
[1] IBM master data management — https://www.ibm.com/think/topics/master-data-management
[2] SAP master data management and implementation styles — https://www.sap.com/resources/what-is-mdm
[3] SAP consolidating master data — https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/6d52de87aa0d4fb6a90924720a5b0549/5c1f0c571db4124ce10000000a4450e5.html
[4] Microsoft Purview and Profisee MDM — https://learn.microsoft.com/en-us/azure/purview/how-to-deploy-profisee-purview-integration
[5] Microsoft MDS backward compatibility — https://learn.microsoft.com/en-us/sql/master-data-services/backward-compatibility-master-data-services?view=sql-server-ver17
[6] DIGI-TEXX data cleansing — https://digi-texx.com/data-management/data-cleansing-services/
[7] DIGI-TEXX document processing — https://digi-texx.com/data-management/document-processing-services/
[8] DIGI-TEXX investment-management scanning case — https://digi-texx.com/case-studies/document-scanning-solutions-for-a-top-investment-management-institution/
[9] DIGI-TEXX ISO certificates — https://digi-texx.com/download-center/iso-certificates/
[10] Microsoft Purview and partner MDM solutions — https://learn.microsoft.com/en-us/purview/master-data-management
[11] QTSC DIGI-TEXX directory — https://www.qtsc.com.vn/en/business/digi-texx-vietnam-ltd
[12] Contact DIGI-TEXX — https://digi-texx.com/contact-us/
