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Top 8 Data Governance Tools

Collibra Governanceerwin Data Intelligence (DI) for Data GovernanceSAS Data ManagementInformatica AxonMicrosoft Azure PurviewAlation Data CatalogSAP 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.
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  4. The product offers very good flexibility.In terms of which features I have found most valuable, I would say the importing and exporting features. Additionally, the data sorting, categorizing and summarizing features, especially how it can summarize based on categories. These are the key features.
  5. The solution is stable.The feature of auto-onboarding of the assets, enterprise assets via EDC is good.
  6. Has a good interface and is reasonably priced.
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  8. Its 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 sitting. That's the best part and an advantage. I have used an
  9. 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. 542,721 professionals have gotten help from our community of experts.
Rony_Sklar
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
User

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
User

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
User

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
User

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
User
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… (more)

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 platforms to get to the consumer with innovative products and services. What drives such behavior?  

1. Better products and customer service

2. Increased reach to the market 

3. Cost optimization 

4. Quick turn around in cash flow 

5. And many more.

All the above are data-driven for making informed decisions.  Managing data is a key driver for organizations and vendors are hard-pressed to provide secured solutions to feed this hunger.  There are various aspects of data from creation to deletion and identifying these aspects of Data Life Cycle Management is complex.

DLCM is nothing new and has been around for eons now.  With AI / ML coming into play, the dimension of data management changes.  The time required to churn huge data to make decisions has considerably shrunk, in line with the market requirements.  Product life cycles are shrinking as consumer needs have increased and organizations have limited time to roll out new products to tend to the market needs.  Products can be anything varying from FSI to Telco to Health Sciences to FMCG.

The industry is trending towards a Hybrid operation strategy when it comes to DLCM.  Budgets are shrinking and organizations are looking at Service-Based Solutions in a consumption model to meet their requirements whilst managing their cash flow.  Both On-Premise and Cloud-Based Deployments will co-exist as this move, towards a Service-Based Solution accelerates. Though the market is not ready for a full cloud-based adoption (excluding SME's), large organizations are approving this approach. 

As Data Privacy and Security remain a key concern, Hybrid cloud strategy provides a bridge between On-premise and Full Cloud adoption and helps to manage the journey of data in a more adaptive way.

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Thomas Dodds
Practice Director - Data Architecture & Governance at Agilarc LLC
May 11 2021
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 them… (more)

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 them, and that are passed along by communication and imitation from one generation to the next.” [1] A data culture, therefore, is just the application of that definition relating to data. Data culture is the way of life of the organization concerning data. The glowing word art in your lobby about “Innovation” is simply a vain symbol, if when met with organizational change the response is “this is the way we’ve always done it.”

‘Second nature’ behaviors, beliefs, and values of the organization on data

Culture can be likened to layers of an onion. On the surface, there are artifacts and symbols. Peel that layer back and find espoused values. At the core rests the underlying assumptions – those ‘second nature’ elements; it is the way we are. Data culture has the very same layers – artifacts, values, and basic assumptions. Artifacts can be expensive! Now, follow me closely – artifacts, when communicating the true culture, are indeed valuable. When not, they are mere points of criticism and frustration.

Your data culture

Grandiose claims of self-service data access can be a costly artifact and most certainly is when your data culture does not have at its core solid basic assumptions about data, its value, and proper use. This realization is dawning on organizations the world over and we are seeing a growth of the role of the Chief Data Officer (CDO) in response. CDOs arrived on the scene in the early 2000s, and the count shot up to 4000+ by 2017 with 63% of executives citing they had this role on staff [2]. Even today, there is still muddiness around who a CDO should be and what they should do. I say a CDO must be well versed in leadership, in addition to technical knowledge, having an intimate understanding of their culture, and a seasoned practitioner of how to effect organizational change centered on data.

Not so soft skills

Granted culture and leadership are often termed ‘soft skills’ by many in technology, but these are hard skills. There is solid science behind knowing your culture – both quantitative and qualitative measurements are used, and the scientific method applies. There is also the presence of solid science that underpins organizational change. The CDO must make good use of it all as a leader of people over a manager of things – they are an influencer. Influence comes with relationships.

McKinsey’s Khushpreet Kaur interviewed Scott Richardson, CDO of Fannie Mae, back in 2017 and supplies the following, “We’d go around the room, and people introduced themselves as human beings, not workers; it’s remarkable how everyone truly has a story to tell. I found this incredibly energizing, and it set the stage for us all to have a more trusting, human relationship. It has had broader, more positive benefits than I could have imagined.” [3] Notice the priority and import of trust-relationships between people – that’s leadership. He is referring to his first 100 days in the role – he is not handing out policy, strategies, and plans. Mr. Richardson understands that to change the culture he must know it – and he goes after the heart of the matter, the heart of people. It is not a soft skill; it is a heart skill.

Recurrently organizational culture seems to begin and finish with symbols; ‘hung on the wall’ and waiting for the people who make up that organization to mold themselves into. This approach assumes an end without first determining the means. The better approach consists of the evaluation and cultivation of culture through leadership. Give people the space to determine and connect with the shared values of the culture. The symbols will emerge, and people will naturally know how to align with them because they already identify with it – in fact, they will own it. There is already a culture around data in your organization - it is up to you to get to know it.

References:

  1. http://people.tamu.edu/~i-choudhury/culture.html
  2. https://medium.com/datapace/the-number-of-chief-data-officer-is-rising-but-this-role-is-still-unclear-be6add07315b
  3. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-first-100-days-as-a-chief-data-officer
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Find out what your peers are saying about Collibra, erwin, Inc., SAS and others in Data Governance. Updated: October 2021.
542,721 professionals have used our research since 2012.