Top 8 Data Governance Tools

Collibra GovernanceSAS Data Managementerwin Data Intelligence (DI) for Data GovernanceInformatica AxonSAP Data HubAlation Data CatalogBigIDInfogix Data360 Govern
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    As far as the functionality of the tool is concerned, it's pretty slick.Collibra is very good at talking to modern database systems like a normal RDBMS, a DB2, or a SQL server or an Oracle.
  2. 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.
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  4. erwin has tremendous capabilities to map right from the business technologies to the endpoint, such as physical entities and physical attributes, from a lineage standpoint.
  5. The solution allows the complete governance process, starting from the data quality, those definitions, and it can get the data quality in the EDC.The features that I have found most valuable are, first of all, that it comes as part of this whole bundle of Informatica tools. So if you've been implementing Informatica MDM across a business, you'd often find it comes with it. It's been thrown in as a sweetener. The adoption of it is often the challenge because as part of your MDM project, governance is seen as something beyond the MDM and is often restrictive. So this tool actually, for the first time, when you talk to data governance teams, gives a holistic view of what a data governance team can do with this tool.
  6. 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
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  8. 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.
  9. Infogix support stands out in terms of quality and response time, and it is really easy to speak with them by phone or by email.It is an integrated solution that provides end-to-end capabilities for managing the data. It is very useful for cleaning the data. It also allows us to combine data from on-premises and cloud sources. On this unified data, we can perform some advanced analytics to predict customer behavior and create new products. Its management and governance capabilities are valuable. It provides full tracking of the data flow. It provides customers the full control over the data and the ability to protect the data. It enables us to provide the best service to the customers. We also get great support from Infogix.

Advice From The Community

Read answers to top Data Governance questions. 501,818 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 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 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.

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Data Governance Articles

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, 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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