We changed our name from IT Central Station: Here's why

Top 8 Data Governance Tools

Collibra Governanceerwin Data Intelligence (DI) for Data GovernanceSAS Data ManagementAlation Data CatalogInformatica AxonMicrosoft Azure PurviewSAP Data HubBigID
  1. leader badge
    It's incredibly easy to use. I like Collibra's flexibility. I like to be able to modify things for our own use. For example, we've chosen to use Collibra also as a knowledge management tool, even though it is not designed to be a knowledge management tool. That's the beauty of it. It can serve as a knowledge management tool by creating some custom assets specifically for knowledge management.
  2. The metadata manager and the mapping manager are valuable. We use the metadata manager to document our tables and columns within our data stores, and we use the mapping manager for ETL specifications. These features are helpful because our major focus is on just documenting the movement of data. We don't focus on the other modules within the product, so we just never decided to use them.
  3. Find out what your peers are saying about Collibra, erwin, Inc., SAS and others in Data Governance. Updated: January 2022.
    564,322 professionals have used our research since 2012.
  4. The tool is reliable, quick, and powerful. The product offers very good flexibility.
  5. Given the relatively low level of maturity, Alation's most relevant feature at the moment is a user interface that's easy to navigate, which helps us find and understand the data. So while Alation has a lot more functionality, our pain point right now is being able to easily find, understand, and trust the information to use it.
  6. The solution is stable.The feature of auto-onboarding of the assets, enterprise assets via EDC is good.
  7. Has a good interface and is reasonably priced.
  8. report
    Use our free recommendation engine to learn which Data Governance solutions are best for your needs.
    564,322 professionals have used our research since 2012.
  9. The most valuable feature is the S/4HANA 1909 On-PremiseIts connection to on-premise products is the most valuable. We mostly use the on-premise connection, which is seamless. This is what we prefer in this solution over other solutions. We are using it the most for the orchestration where the data is coming from different categories. Its other features are very much similar to what they are giving us in open source. Their push-down approach is the most advantageous, where they push most of the processing on to the same data source. This means that they have a serverless kind of thing, and they don't process the data inside a product such as Data Hub. They process the data from where the data is coming out. If it is coming from HANA, to capture the data or process it for analytics, orchestration, or management, they go to the HANA database and give it out. They don't process it on Data Hub. This push-down approach increases the processing speed a little bit because the data is processed where it is sitt
  10. The features that I have found most valuable are the user experience, the credentialing, and that BigID is user friendly. Additionally, you can deploy to several other Microsoft platforms and you can use it for other things, like a bigger element or a report.

Advice From The Community

Read answers to top Data Governance questions. 564,322 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 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.

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 avatarBryn Davies

A brief informal answer is that Master Data Management is a very specific data architecture to sustain a high-quality system of record aka "golden records" enabled by specialized MDM hub technology. 

Data Governance covers primarily the people and process elements of data management through the implementation of associated organizational structures, roles, responsibilities, processes and standards in order to sustain well-managed and reliable data across the organization. 

MDM and DG are complementary and each supports the other. 

author avatarAntonio Carlos Murayama

MDM solutions are more related to the technical process about data model (customer, supplier, material, products) and process for capture data, enrich data, quality of data, matching capabilities to avoid duplication, golden rules for records surviving, parse/parsing, etc. 

Data Governance is more related to the Central Process - to create a specific workflow to request and process requests for the creation and update master data through workflow orchestration with approvals and enrichment under governance with visibility of the process and SLA´s Indicators. 

You need to define a model for central or federate governance and create specific teams (with a responsibility) like Custodians, Stewards, Owners for each type of master data, and so on. 

author avatarMohd Khairi

Data Governance (DG) is managing the data used in an organization for security, usability, availability and integrity. A sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute those procedures.

Master Data Management (MDM) provides new tools, techniques and governance practices to enable businesses to capture, control, verify and disseminate data in a disciplined fashion. Combined with tools for data quality management, this provides the trusted information foundation that companies base their analytics on.

Data Governance Articles

Subramanian R Iyer
Aug 19 2021
Organizations are in search of new ideas to remain relevant in today's data-driven world. Data is a key asset that can either make or break a go-to-market strategy and bring in the next big change. In this new normal e-commerce has benefitted the most and every business (big or small) is using pl...
Read More »
Thomas Dodds
Practice Director - Data Architecture & Governance at Agilarc LLC
All too often I hear talk of data culture and the conversation quickly encircles data technologies and tools. Technology and tools are not cultures. Culture is: “a way of life for a group of people--the behaviors, beliefs, values, and symbols that they accept, generally without thinking about the...
Read More »
Find out what your peers are saying about Collibra, erwin, Inc., SAS and others in Data Governance. Updated: January 2022.
564,322 professionals have used our research since 2012.