Top 8 Master Data Management Tools

Informatica MDMMicrosoft MDSSAP Master Data GovernanceOrchestra Networks EBXStibo STEP MDMTIBCO EBXIBM InfoSphere MDMOracle Data Relationship Management
  1. leader badge
    Its data cleansing capabilities are very valuable. The match and merge and the audit trail functionalities are very good. MDM doesn't require a separate IDQ.
  2. leader badge
    One of the main features I have found useful is the integration with Azure active directory.It's very easy to use.
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  4. We've found the solution to be quite flexible.A good solution for large enterprises.
  5. The initial setup is easy and straightforward.The API and the data model are very valuable features.
  6. It's powerful and it's stable.
  7. The data modeling is very good.There are a lot of possible use cases for this Master Data Management tool.
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  9. There are not really many areas of the product that need improvement because the product stays up-to-date with data management needs. The features that are most valuable are the governance, the end-profiling and the ETL which allows you to see the metadata repositories.

Advice From The Community

Read answers to top Master Data Management questions. 501,499 professionals have gotten help from our community of experts.
What are key differences between MDM and Data Governance? What are the practical differences in which each of these solutions is applied?
author avatarJoel Embry

Data Governance is a collection of practices and processes which help to ensure the formal management of data assets within an organization.

Master data management is a technology-enabled discipline in which business and Information Technology work together to codify and ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of an enterprise's official shared master data assets. MDM is the systemic technology that enables and enforces Data Governance.

author avatarDelmar Assis
Real User

The DG solution addresses mainly business glossaries, policies, rules, meanings, complainces like GDPR, DG worflows, table references, data catalog, data flow (lineage, impact) and data profing; MDM must manage the main data of the business domains (customers, suppliers, products ...) however MDM must provide meanings of terms/semantic and definitions of the master data, so there is an intersection area between both; DG is a umbrella and MDM is focused on specific subset of definitions.

What steps can be taken to address the challenges associated with implementing master data management?
author avatarJaime Rendon

What are the biggest challenges in master data management implementation?

The biggest challenges I have experienced are:

Data Quality of the source systems, that prevent to quickly identify, and consolidate master entities.

Diversity of sources with different levels of normalization that require rework on the data integration processes.

Nonstructured data required to be integrated to feed the MDM master entities, from on-premise and cloud systems.

Multiple Excel spreadsheets containing business rules required to be integrated into a repository in order to be included in the MDM process. For example to identify a unique customer or business units.

Special characters not being identified in the integration processes nor data quality, which causes the process to crash if there is not a code page standardization to use Unicode in all the systems.

What steps can be taken to address the challenges associated with implementing master data management?

Establishing a data quality strategy before implementing the MDM, that will support to have a clear data integration process and identify in a faster way the key information to create the Master Entities, avoiding rework and further headaches.

Create an Operational Data Integration repository to consolidate the diversity of sources with different levels of normalization.

Extend Integration strategy with a Data Virtualization solution to include non-structured data required to be integrated to feed the MDM master entities, and provide flexibility for APIs.

This is also covered with the Data Virtualization solution.

Establish a Governance Strategy to standardize code pages thru Unicode in all the systems.

author avatarZaheer Khan

Integration of multiple source data, managing and addressing the data quality challenges, de-duplication of data, address matching, standardization of data etc.. identifying the domains for which MDM needs to be implemented. Business should drive this initiative and not IT. Choosing the right tool and its implementation partner. Clear understanding of the attributes for which the MDM will be done. Realistic expectation and a program to run data quality & data governance with right stewardship

author avatarreviewer1175463 (User)
Real User

There are multiple steps to address the challenges.

Key challenges include:

Funding - understanding how to calculate the dollars required to adequately plan, design, build, test, and implement an MDM solution.

Tool selection: understanding which toolset is right for your organization is crucial.

Communication: communication the benefits of MDM are difficult as it's back end capability that doesn’t offer hard tangible benefits to the user as such. And those that benefit directly from it are a very narrow cross-section of the user community.

Endorsement: securing an endorsement from the business and convincing the business to support the investment of multi-million dollars for a toolset that has little visibility and plays a silent role in the background is a tough proposition

Industry alignment: ensuring your industry will align with other industries You partner with so the data can be easily and readily be available and in a better format than it was prior to the implementation. Ensuring clear dialogue with all parties to make the right decisions upfront or it will be costly to retrofit and re-engineer a solution post-implementation.

Data classifications: having a clear understanding of your data estate is crucial when the selection of an MDM solution is being undertaken.

Skills and experience: it’s very important to assess the current skills and experience in the team that have MDM skills, this is a major consideration

Infrastructure: select the toolset that will integrate with your current infrastructure to avoid introducing layers of complexity into your enterprise environment.

author avatarBrian Dandeneau

One of the best things that you can do is identify the stakeholders for Master Data Management. Once that group is identified you have that group assign a leads to be on the governance committee. Assign one person on this committee to be the overall lead so there is one voice to communicate to the stakeholders. Once full organized you can then setup monthly meetings to go over changes and gathering of approvals. There are a lot of tools out there that can help in this process to put restrictions and rules around the creation and use of Master Data. Be careful on which one you choose and it can create unneeded complexities in some cases. hardest part of it all is getting people to believe that there is a need for governance. Be safe, wash your hands, and wear a mask.

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