What are the key features of a modern data governance tool?

Modern data governance is about building context around an organizations data essentially covering but not necessarily limited to Business Glossaries/definitions, Data Gov Policies, Data Quality & security all linked to the actual live data rather than a standalone knowledge repository

To me a good governance tool begins with understanding the whole life cycle of data (Cradle to Grave) not just cataloging metadata from DB scrapes but actually connecting the dots from the UI to the Middle Layer/APIs/ ML Feature Stores all the way to analytics and reports. Most tools today address the last mile problem (ie) Scrapping Databases for extracting metadata and then relying on humans to stitch things together.

Data Governance 2.0 also sometimes called Modern Data Governance phase picked up in last few yrs. The main differentiator is inclusiveness of four concepts under one umbrella - Data Catalog (Metadata & Lineage), Data Quality, Data Security & Business Glossary Market Place The modern tools should have all these features and business friendly interface. It helps to improve trust worthiness of Data Asset. Ability for Businesses to have self-help interface to know end to end data (Producer to Consumer). Single tool for data auditability & Certified data set is big positive for Data Governance 2.0!

Adding sensitivity to the each data elements makes the catalog more valuable. This helps in identifying the sensitive data elements and assign relevant protection as per the business requirements.

Data Catalog is fundamental to data democratization. If you want business functions to be able to analyze the data on their own, they need to know what data is available, where it resides and what it means. In most basic form, it's all it does. But it can be much more. For data teams, a catalog with per attribute lineage and linkage is an invaluable source of information on dependency management. For business, knowing what each attribute means and how it was derived, who is the owner of that data - makes data usable and discoverable. Adding collaborative features, such as comments, requests and other social aspects, can help in destroying walls and silos across the organization. Asking the right questions to the right people made easy!

Modern data governance tools must prioritize data quality to ensure that the information feeding into your business processes is accurate and trustworthy. High-quality data reduces risk and cost by preventing errors and redundancies that can lead to poor decisions and inefficiencies. It also enhances regulatory compliance by ensuring that the data meets the standards required by various oversight bodies. Without a solid foundation of data quality, any insights or strategies developed are built on shaky ground.

Integrate data quality into the catalog for trustworthy analytics. Rate the accuracy, completeness, and consistency of data. Identify any quality issues at the attribute level. Automate validation, during the data ingestion process. Allow users to report any quality problems they encounter. Address quality concerns based on their impact downstream. Monitor the quality performance indicators (KPIs) over time. Establishing data involves ensuring transparency in its quality.

Any technology or product solution it is only as good as the data available at its foundation. Single source of truth of data allows for data accuracy and consistency across different systems and Applications.

Well-know fact: any data initiative can be stopped dead in its tracks and devalued, if there is no trust in the data it is built around. The best way to avoid that is to measure the quality of your data. Without measuring what you have right now, there is no path to improving. While the quality measurements usually done in a separate tool, or as a part of DE pipelines, it is important to make the results visible to the end-users. Better yet, those results are displayed in the Data Catalog itself, per attribute. This ensures that people working with data know beforehand if the data is trustworthy. It will also help prioritize what attributes need more love, based on how critical they are for downstream tasks, reports and models.

A modern data governance tool must enforce stringent security protocols to prevent unauthorized access and data breaches. Effective data security mitigates the risk of financial loss, intellectual property theft, and erosion of customer trust. It ensures that sensitive data is handled in compliance with legal and regulatory requirements, which is crucial for maintaining a company's integrity and operational continuity.

Besides the obvious role-based security configurations to disallow people accessing data freely, modern age is all about protecting PII data. Data Catalog should have the ability to mark attributes as such and apply more stringent policies around it. Bonus points if tool can utilize ML to identify PII elements automatically and notify responsible persons to validate it.

Safeguard sensitive data through least privilege access controls. Categorize data according to its sensitivity and enforce policies based on attributes. Utilize techniques, like data masking and encryption that align with these classifications. Safeguard identifiable information (PII) through automated discovery and rights management processes. Maintain a log of data access, for auditing purposes. Implement security models that can adapt to evolving regulations. Integrate security measures seamlessly into the data infrastructure.

Data collaboration isn't just nice to have; it's essential. It allows disparate teams to access, interpret, and act on data in a unified manner, reducing confusion and ensuring alignment. A modern data governance tool breaks down silos, fostering an environment where insights are shared as readily as the data itself. Ultimately, without data collaboration, you're not leveraging your most valuable asset to its full potential.

Based on my previous experiences, to implement an effective data collaboration platform specialy for big companies, there is a necessity of self-service solution that expose data as products and allows seamless connection between data owners and consumers using a simple and intuitive data oriented ux/ui. having such platform minimizes the need for OPS/Admin teams -normaly not part of such process - to grant data access. The key lies in integrating with a data catalog and automating access permissions, which aids in reducing human-related errors and subsequent delays. continuous monitoring of all changes is paramount to maintains data authenticity and to enhance security measures.

Data collaboration within Data Catalog should enable direct communication between data consumers and those, responsible for data: owners, custodians, stewards. It is also a good flow for requesting data from the Platform team or Engineering, complete with related approval steps from the owner. Ideally, the catalog is a one-stop shop for getting to know the data and to ask for access to it. Avoid separating the communication from the catalog, if at all possible. Tagging people in Slack or writing emails from outside the tool you use to check what data you need, makes it harder to follow the proper flow.

As an end user of Data, it is what I see when it comes to talk about data collaboration: ?? - Preventing data to be collected internally in each team, and make it more accessible. Sometimes we can face some cases where people write things in Excel sheets and keeps them in computers. Then sometimes they get lost when these employees leave the company; ?? - Centralized systems. Data may come from different sources and they may be valuable for different teams, to find a solution to join these sources in one center will make process more smooth; ?? - Preparing a Data Reports and Visuals that are dymanic and accessible for all helps share insights about current situation and available data.

Data Intelligence within data governance frameworks facilitates informed policy-making and regulatory compliance, enhancing the trustworthiness of data systems. The incorporation of this feature supports the mission critical business outcome of fostering an environment conducive to data-centric innovation and informed decision-making processes.

I have no idea what does Data Intelligence mean and no, I don't think that Data Governance tool should have the ability to analyze and visualize data from various sources. I do, however, think that a good Data Observability platform can be a valuable addition to the Data Catalog. That is not to say that it has to be a separate tool, or that there is no intersection in functionality. Observability helps users get to understand data better: seasonality, local trends, possible anomalies lurking in there. All of that without doing your own analysis, just by looking at pre-defined dashboards. Consider profiling your data and make it observable for everyone to see (within reason). If that can be achieved in a data catalog, even better.

Enable intuitive data understanding through embedded intelligence. Automatically create profiles and visually represent data as soon as it is ingested. Draw attention to any anomalies, outliers, and hidden insights. Offer suggestions, for datasets. Connect with domain experts. Enable analysis within the catalog fostering a sense of teamwork. Ensure that business users can comprehend and access data, with transparency. Remember true insight arises from empowering individuals than solely relying on technology.

A new Data jargon is "Data empathy" that is very important today. Data is a key to the story of customer, employee, business. It encovers the problems, opprtunities, and all other aspects. Just investigate it. Give rigth questions to get right answers.

Mastering is a key governance concept . There has to be single version of truth aka Golden source defined and redundant copies eliminated .

Data governance is a broad concept, not a one-size-fits-all solution. Modern tools have advanced significantly, so start by understanding your options. Select the right tools for your needs, and don't get swayed by marketing hype.