The hottest Data Governance Substack posts right now

And their main takeaways
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Top Technology Topics

5 Key Predictions for the Data Industry in 2025

SeattleDataGuy’s Newsletter • 612 implied HN points • 07 Jan 25
🕹 Technology Data Analysis AI Data Tools Business Intelligence Data Governance
  1. Iceberg will become popular, but not every business will adopt it. Many companies want simpler solutions that fit their needs without needing lots of complicated tools.
  2. SQL isn't going anywhere; it still works well for managing and querying data. People have realized that a bit of order in data is important for getting meaningful insights.
  3. AI use will become more practical, focusing on real-world applications rather than just hype. Companies will find specific tasks to automate using AI, making their workflows more efficient.

Issue #16 - The Data Quality Conundrum (Part 2 – Solving)

The Data Ecosystem • 439 implied HN points • 28 Jul 24
🕹 Technology Data Quality Data Governance Data Management Data Tools Data Strategy Data Analytics
  1. Data quality isn't just a simple fix; it's a complex issue that requires a deep understanding of the entire data landscape. You can't just throw money at it and expect it to get better.
  2. It's crucial to identify and prioritize your most important data assets instead of trying to fix everything at once. Focusing on what truly matters will help you allocate resources effectively.
  3. Implementing tools for data quality is important but should come after you've set clear standards and strategies. Just using technology won’t solve problems if you don’t understand your data and its needs.

Issue #8 - Deliver on the Data Needs, not the Data Desires

The Data Ecosystem • 199 implied HN points • 02 Jun 24
💼 Business Data Strategy Stakeholder engagement Analytics Data Governance Business Intelligence
  1. It's important to focus on what the business truly needs from data, not just what they think they want. Conversations should help uncover real goals and challenges.
  2. Data projects often fail because teams don't ask the right questions or fully understand the business context. Engaging stakeholders regularly is key to success.
  3. A clear step-by-step process helps develop effective data solutions. Start with building a strong data foundation before moving on to more complex analytics.

Issue #9 - Clarifying Data Terminology

The Data Ecosystem • 159 implied HN points • 09 Jun 24
🕹 Technology Data Management Data Analysis Data Governance Data Literacy Data science
  1. Data can mean many things, from raw collections to curated evidence used in decisions. It's important to define what data means in each situation to avoid confusion.
  2. Poorly defined data terms can lead to problems in data literacy, collection, and management. This can create issues for organizations trying to use data effectively.
  3. Understanding different categories of data, like data types and processing stages, helps in managing and analyzing data better. Knowing these categories makes it easier to communicate and use data in an organization.

Issue #3 - Pulling Back the Data Ecosystem Curtain

The Data Ecosystem • 219 implied HN points • 28 Apr 24
💼 Business Data Strategy Business Models Decision-making Data Governance
  1. Data in a business starts with understanding its goals and needs. The success of data efforts relies on how well it aligns with what the business wants to achieve.
  2. The data lifecycle turns business needs into actionable insights. It involves sourcing data, organizing it, and finally consuming it to gain meaningful insights that support decision-making.
  3. Surrounding factors like market trends and organizational issues can impact how data is used. It's important to recognize these influences to address challenges and keep data initiatives on track.
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Issue #5 - Data’s Growth Problem: Losing the Audience

The Data Ecosystem • 99 implied HN points • 12 May 24
🕹 Technology AI Tools Data Strategy Business Intelligence Data Governance
  1. Data growth is huge but understanding it is lagging behind. Even though we generate tons of data daily, many people and businesses struggle to truly grasp what it means.
  2. Organizations often rely too much on consultants and vendors for quick fixes instead of addressing the core issues of their data practices. This can lead to overspending and not solving the deeper problems.
  3. To benefit from data, companies need to focus on building strong foundations like data governance and internal capabilities. It's important to think long-term instead of prioritizing quick solutions.

Issue #2 - The Data Ecosystem: Where do you even start?

The Data Ecosystem • 119 implied HN points • 21 Apr 24
🕹 Technology Data science Machine Learning Data Engineering Data Governance Analytics
  1. Data can be really complicated, and it's easy to miss how everything connects. People often focus on their own area and forget about the bigger picture of the data ecosystem.
  2. Chief Data Officers (CDOs) are important but can only do so much to fix data issues. They deal with many challenges, including limited power, lack of experience, and politics within the organization.
  3. To improve in the data field, we need to recognize the gaps in our knowledge, prioritize what to focus on, and continuously educate ourselves in both our own areas and related data domains.

You just bought Snowflake. What next? Your Top 5 Priorities

The Orchestra Data Leadership Newsletter • 59 implied HN points • 29 Apr 24
🕹 Technology Data Management Data Infrastructure Data Modeling Data Governance
  1. Ensure rock-solid infrastructure for your Snowflake implementation to prevent pipeline failures and maintain data quality.
  2. Set clear expectations and prioritize projects to manage scope and quality, fostering trust and collaboration.
  3. Start thinking of data as a product during the Snowflake implementation to minimize costs, stabilize usage, and accelerate trust in the data team.

Effective Data Governance Can Only Exist Within Data Orchestration

The Orchestra Data Leadership Newsletter • 99 implied HN points • 07 Feb 24
🕹 Technology Data Governance Data Orchestration Data Engineering Software Tools Security Measures
  1. Effective data governance requires incorporating preventive measures within data orchestration layers.
  2. Current data governance tools predominantly offer post-action analytics rather than proactive preventive measures.
  3. By integrating role-based access control and monitoring in the orchestration layer, organizations can shift to a more proactive data governance approach.

GroupBy #26: How GitHub uses merge queue to ship hundreds of changes every day, Data governance in the age of generative AI, "Good Enough" Data Models

VuTrinh. • 39 implied HN points • 12 Mar 24
🕹 Technology Data Engineering AI Software Development Machine Learning Data Governance
  1. GitHub uses a merge queue system that helps them quickly ship many code changes each day. This makes their deployment process faster and more efficient.
  2. Data governance is becoming really important, especially with the rise of generative AI. Companies need to ensure the data used by these systems is accurate and secure.
  3. The idea of 'Good Enough' data models suggests that it's okay to have models that meet basic needs instead of striving for perfection. This approach can save time and resources.

($) Do We Need Sovereign AI?

Interconnected • 77 implied HN points • 17 Mar 24
🕹 Technology AI Cloud Computing Data Governance
  1. Sovereign AI is a concept gaining attention, especially with Nvidia's involvement, and raises questions about AI infrastructure and global talent flow.
  2. The idea of sovereign AI has potential benefits in addressing issues like hallucination and data governance that plague generative AI.
  3. Global discussions are evolving around the necessity of sovereign AI to tackle complex AI challenges and leverage economies of scale.

Navigating the AI Transition? A good first step seems to be to distinguish the human from the machine

The Diary of a #DataCitizen • 1 HN point • 08 Sep 24
🕹 Technology Artificial Intelligence Data Governance Human-Machine Interaction Technology Ethics Emerging technologies
  1. It's important to clearly define what humans can do best, like being creative and making big decisions, and what AI can do well, like analyzing data and automating tasks. This helps us understand how to work together.
  2. AI should remain a tool for humans, not take over decision-making or replace human values. Keeping humans in control ensures that AI is used ethically and responsibly.
  3. Understanding how AI impacts our lives is crucial in today's world. Everyone should learn about AI so they can adapt and make informed choices in their personal and professional lives.

What is Data Orchestration and why is it misunderstood?

The Orchestra Data Leadership Newsletter • 39 implied HN points • 28 Jan 24
🕹 Technology Data Orchestration Workflow Orchestration Data Management Data Governance
  1. Data orchestration is often confused with workflow orchestration, but it involves more than just triggering and monitoring tasks; it includes reliably and efficiently moving data into production.
  2. Reliably and efficiently releasing data into production is complex and involves elements like data movement, transformation, environment management, role-based access control, and data observability.
  3. Implementing end-to-end and holistic data orchestration offers transformative benefits such as intelligent metadata gathering, data lineage, environment management, data product enablement, and cross-functional collaboration for scalable data operations.

Get a Microsoft 365 Copilot security teardown without hours of documentation review

Deploy Securely • 39 implied HN points • 24 Jan 24
🕹 Technology Security Privacy Data Governance AI Governance Data Management
  1. Microsoft 365 Copilot provides detailed data residency and retention controls favored by enterprises in the Microsoft 365 ecosystem.
  2. Be cautious of insider threats with Copilot as it allows access to considerable organizational data, potentially leading to inadvertent policy violations.
  3. Consider the complexities of Copilot's retention policies, especially in relation to existing settings and the use of Bing for web searches.

Databricks Unity Catalog: Enabling Data Democratization

Data Plumbers • 19 implied HN points • 08 Apr 24
🕹 Technology Data Management Data Governance
  1. Data democratization is vital for modern data strategies, making data more accessible and understandable within an organization for informed decision-making and better customer experiences.
  2. Databricks Unity Catalog supports data democratization by providing a centralized governance layer, simplifying access management, enabling unified data management, and fostering data discovery, collaboration, and sharing.
  3. Implementing data democratization requires robust data governance and security measures to mitigate risks of privacy violations and data leaks.

The Ways Microsoft Copilot for Security Can Enhance Security Operations with Microsoft Purview

Rod’s Blog • 19 implied HN points • 08 Feb 24
🕹 Technology Security AI Data Governance Compliance Incident Management
  1. Microsoft Security Copilot enhances security by seamlessly integrating with Microsoft Purview, simplifying security policies and governance.
  2. The AI capabilities of Microsoft Security Copilot aid in proactive threat detection and response by analyzing data to identify potential risks before they escalate.
  3. Automated compliance and data governance processes are streamlined through the combination of Microsoft Purview's features and Security Copilot's automation, facilitating adherence to regulations.

Will active metadata eat the orchestrator?

Data People Etc. • 88 implied HN points • 27 Mar 23
🕹 Technology Data Management Metadata Data Governance Collaboration Innovation
  1. Active metadata is a dynamic way to manage and use metadata across different parts of the data stack.
  2. Active metadata can potentially replace triggering mechanism aspect of data orchestrators, but not the optimization intelligence.
  3. The true value of active metadata lies in empowering business users by acting as a personal data assistant.

Using Microsoft Purview for Data Classification and Labeling to Secure Generative AI

Rod’s Blog • 19 implied HN points • 20 Nov 23
🕹 Technology AI Data Governance Security Generative AI
  1. Data classification and labeling can enhance data quality by ensuring authenticity, reliability, and relevance, and help remove unnecessary or erroneous data for Generative AI systems.
  2. Data classification and labeling can safeguard data privacy and confidentiality, prevent unauthorized access, and aid in compliance with data protection regulations like GDPR and CCPA.
  3. Using Microsoft Purview for data classification and labeling can efficiently manage data access, apply sensitivity labels, and provide insights to improve data security and reliability for Generative AI.

☎️ Interview: Stijn Christiaens, CEO of Collibra on the State of Privacy-Enhancing Technologies #004

Let Us Face the Future • 19 implied HN points • 05 Apr 23
🕹 Technology Data Governance Data Sharing Data Monetization Technology Regulation
  1. Collaborative computing is shaping the future of data use and value maximization.
  2. Selling data products often means competing against non-consumption and overcoming organizational inertia.
  3. The rise of Chief Data Officers is simplifying the sales process and driving internal data sharing before external collaboration.

☎️ Interview: Dr Hyoduk Shin, Professor of Innovation at UC San Diego on the State of Privacy-Enhancing Technologies #003

Let Us Face the Future • 19 implied HN points • 05 Mar 23
🕹 Technology Privacy Data Sharing Data Governance Regulation
  1. Collaborative computing is becoming a trillion-dollar market reshaping how data is used in the economy.
  2. To promote data sharing, companies need to realign incentives, focus on building relationships, work on culture, and segment data by time.
  3. Financial services and healthcare are early adopters of data collaboration tools due to confidentiality and regulation around privacy and data security.

Meta v. Bright Data Perspectives: A Data Score Interview with Glacier Networks

The Data Score • 1 HN point • 20 Feb 24
💼 Business Data insights Legal Compliance Web scraping Data Governance Risk management
  1. The court ruling in the Meta v. Bright Data case may lead to more defenses against web scraping and offers clarity on accessing public data while underscoring the importance of adhering to individual website terms.
  2. Before starting a web mining project, individuals should carefully review each website's terms, assess intended usage of scraped data, and consider the legal implications of accessing specific content.
  3. Upcoming court cases, like those involving Meta and other companies, may set standards for web mining governance while Glacier Network emphasizes a standardized risk policy to simplify data exchange and compliance in a rapidly evolving data industry.

The Consumer-Defined Data Contract

Data Products • 3 implied HN points • 04 Dec 23
🕹 Technology Data Contracts Data Engineering Data Governance Data Quality Software Engineering
  1. Producers need to move towards consumer-defined data contracts to improve data quality and alignment with user needs.
  2. A phased approach of awareness, collaboration, and contract ownership helps in successful data contract adoption.
  3. Starting with consumer-defined contracts drives communication, awareness, and problem visibility, leading to long-term benefits.

Data Contracts Book - Early Release

Data Products • 2 implied HN points • 27 Feb 24
🕹 Technology Data products Data Contracts Data Engineering Data Quality Data Governance
  1. Chad Sanderson announced an upcoming book on Data Contracts with O'Reilly, covering topics like what data contracts are, how they work, implementation, examples, and the future implications. The book will delve into Data Quality and Governance.
  2. The first two chapters of the book are available for free on the O'Reilly website. They cover the importance of data contracts and the real goals of data quality initiatives, totaling about 45 pages of content.
  3. Chad Sanderson is currently selecting technical reviewers for the book. Interested individuals can reach out to him to share their thoughts on an advance copy.

Gradient Flow: ML in Finance, Disinformation, AI Superpowers

Gradient Flow • 19 implied HN points • 04 Jun 20
🕹 Technology Machine Learning Finance AI Remote work Data Governance
  1. Collaboration between lawyers and technologists is crucial for identifying and mitigating risks associated with AI deployment in various industries.
  2. Responsible ML tools from Microsoft focus on explainability, privacy & security, and governance & reproducibility, providing comprehensive support for ethical AI development.
  3. China and the US are considered AI superpowers, with strong research interest in Data and AI, along with vibrant startup ecosystems focused on applying these technologies.

Snowflake Summit 2023

Magis • 2 HN points • 02 Jul 23
🕹 Technology Cloud Computing Software Development Cybersecurity Data Governance
  1. Snowflake Summit 2023 introduced key features including a partnership with Nvidia, Snowpark Container Services for machine learning, and updates to the Native Application Framework.
  2. Snowflake announced new options for paying Marketplace Listings using Snowflake capacity contracts, custom billing events for native applications, and data governance features like Aggregation Constraints.
  3. Additional announcements at Snowflake Summit 2023 included updates in Snowflake SQL, a new Snowflake Performance Index, and the ability to set spending alerts and calculate cost run-rates.

The IT Director's Guide to Modern Data Management with Sort

Database Engineering by Sort • 0 implied HN points • 23 Jan 25
🕹 Technology Data Management Database Systems Digital Transformation Data Governance
  1. Managing data is crucial for IT success today, and having good data management practices can help organizations thrive.
  2. Data silos, lack of change visibility, and compliance challenges are common problems for IT departments, making it harder to manage information effectively.
  3. Sort is a tool that helps break down data silos, improves tracking of data changes, and enhances security and compliance, making data management easier for IT teams.

Historage Tech completed a tens of millions Series A round

CyberSecurityMew • 0 implied HN points • 10 Jul 23
🕹 Technology Data security Funding AI Data Governance Digital economy
  1. Historage Tech completed a Series A funding round worth tens of millions of RMB. This is their second round of funding this year after the April investment from Hefei Gaotou.
  2. Historage Tech innovatively combines data governance with data security through their Seastone Data Platform. They offer AI-driven data classification and categorization for enhanced security.
  3. The funding from Qi-An-Chuanfa Fund will help Historage Tech accelerate market expansion and continue developing their data platform, aligning with the future digital economy's focus on business-driven data governance.

Why Data Needs Global Governance

The Digital Anthropologist • 0 implied HN points • 10 Mar 23
🕹 Technology Data Governance AI Tech Companies Regulations
  1. Our personal data is being used in various ways by known and unknown companies, which highlights the need for effective governance over data usage.
  2. Data is a crucial resource in the digital age, powering advancements in technologies like AI, robotics, and genetic engineering, but inadequate regulation poses risks in balancing innovation and privacy rights.
  3. The lack of global governance over data flow between nations and industries, coupled with the increasing influence of AI, emphasizes the importance of collaborative efforts involving citizens, non-profits, governments, and industries to establish effective data laws and regulations.

What is 'The Diary of a #DataCitizen?'

The Diary of a #DataCitizen • 0 implied HN points • 28 Aug 24
🕹 Technology Data Governance AI Ethics Digital Rights Data Literacy
  1. Being a data citizen means using data to make smart business choices. It's about knowing your rights and responsibilities regarding data.
  2. Data literacy and good governance are super important with the rise of AI. Understanding data helps us navigate its challenges and benefits.
  3. There is a 'Data Citizens Bill of Rights' that outlines the rights and expectations for those involved in data decision-making.

Embedded Analytics - Under the Hood

astrodata • 0 implied HN points • 30 Jan 24
🕹 Technology Data Governance Performance Data Monetization
  1. Embedded analytics bring data to where customers are, sparking curiosity and increasing engagement by providing data in easily interpretable ways.
  2. Themes of modern embedded analytics include leveraging headless BI tools with semantic layers for defining business logic, and ensuring data governance for reliable data access.
  3. Building embedded analytics solutions not only drives product engagement by integrating data analysis seamlessly, but also opens avenues for data monetization and fosters internal data-driven cultures within businesses.