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Robert Seiner and Anthony Algmin faced off – in a virtual sense – at the DATAVERSITY® Enterprise Data World Conference to determine which is more important: DataGovernance, Data Leadership, or DataArchitecture. The post DataGovernance, Data Leadership or DataArchitecture: What Matters Most?
AI solutions have moved from experimental to mainstream, with all the major tech companies and cloud providers making significant investments in […] The post What to Expect in AI DataGovernance: 2025 Predictions appeared first on DATAVERSITY.
In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of datagovernance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks.
Part 1 of this article considered the key takeaways in datagovernance, discussed at Enterprise Data World 2024. Part […] The post Enterprise Data World 2024 Takeaways: Trending Topics in DataArchitecture and Modeling appeared first on DATAVERSITY.
Constantly evolving data privacy legislation and the impact of major cybersecurity breaches has led to the call for responsible data […]. The post Scaling Data Access Governance appeared first on DATAVERSITY.
Learn about data strategy pitfalls A few words about data strategy Elements of Strategy A solid strategy outlines how an organization collects, processes, analyzes, and uses data to achieve its goals. You will find my business analysis digest, my articles, and more! Is that your first visit to Passionate BA?
In the contemporary data-driven business landscape, the seamless integration of dataarchitecture with business operations has become critical for success.
As a result, data owners are highly motivated to explore technologies in 2024 that can protect data from the moment it begins its journey in the source systems. From […] The post Trends in DataGovernance and Security: What to Prepare for in 2024 appeared first on DATAVERSITY.
The post DataGovernance at the Edge of the Cloud appeared first on DATAVERSITY. With that, I’ve long believed that for most large cloud platform providers offering managed services, such as document editing and storage, email services and calendar […].
In a previous article , I compared the Nimble concept to the Business Agility model from the Business Agility Institute (BAI). In this article, I come back to this comparison, using another known concept: the enablers of business Agility. 5) Governance and Funding Agility is stifled by rigid governance models and funding mechanisms.
The hallmark of any successful DataGovernance implementation is awareness. The post Data Projects Should Start with DataGovernance appeared first on DATAVERSITY.
However, with data protection laws and positive awareness across the world, firms have extended the formalization to data collection management. The post Five DataGovernance Trends for Digital-Driven Business Outcomes in 2021 appeared first on DATAVERSITY. This, in fact, is the first […].
The deliverability of cloud governance models has improved as public cloud usage continues to grow and mature. The post Cloud Governance Models appeared first on DATAVERSITY. When we first started […].
There’s a fair amount of high-level advice on the internet about implementing datagovernance, which means the practices an organization uses to ensure its data is available, usable, complete, and secure.
Hence, there is a need to shift to cloud-based identity governance and administration (IGA) solutions. This type of cloud governance is more secure and reliable while providing […]. The post How Cloud Governance Allows Businesses to Become Compliant Superheroes appeared first on DATAVERSITY.
But we all know that cyberattacks are on the rise and evolving data privacy legislation has led to the […]. The post Why Data Access Governance Is Key to Going Faster appeared first on DATAVERSITY.
Analytics at the core is using data to derive insights for measuring and improving business performance [1]. To enable effective management, governance, and utilization of data and analytics, an increasing number of enterprises today are looking at deploying the data catalog, semantic layer, and data warehouse.
Editor’s note: This article originally appeared on CIO.com. If we asked you, “What does your organization need to help more employees be data-driven?” where would “better datagovernance” land on your list? A datagovernance framework. Identify root causes of datagovernance to drive impactful change.
As enterprises expand and grow business functions, theres corresponding linear growth in operational data. This encompasses both master data and transactional data.
Editor’s note: This article originally appeared on CIO.com. If we asked you, “What does your organization need to help more employees be data-driven?” where would “better datagovernance” land on your list? A datagovernance framework. Identify root causes of datagovernance to drive impactful change.
DataGovernance is defined as the execution and enforcement of authority over the management of data and data-related assets.1 1 The terms “Data Mesh” and “Data Fabric” are the most recent examples of names being given to something that describes techniques to help organizations manage their data.
This is where master data management (MDM) comes in, offering a solution to these widespread data management issues. MDM ensures data accuracy, governance, and accountability across an enterprise. Data spread across multiple sources led to inefficiencies in patient care and administrative processes.
Data Sips is a new video miniseries presented by Ippon Technologies and DATAVERSITY that showcases quick conversations with industry experts from last months DataGovernance & Information Quality (DGIQ) Conference in Washington, D.C.
If you’re trying to determine whether you need a data lake, a data warehouse, or possibly even both, you’ll want to understand the functionality of each tool and their differences. This article will highlight the differences between each and how […].
Data fabric is redefining enterprise data management by connecting distributed data sources, offering speedy data access, and strengthening data quality and governance. This article gives an expert outlook on the key ingredients that go into building […].
The ways in which we store and manage data have grown exponentially over recent years – and continue to evolve into new paradigms. For much of IT history, though, enterprise dataarchitecture has existed as monolithic, centralized “data lakes.” The post Data Mesh or Data Mess?
Editor’s note: This article originally appeared in Forbes. Let’s look at a few focus areas of a people-centric strategy to help you achieve trusted data and successful AI projects: your dataarchitecture, the processes for managing governeddata, and balancing the roles of people and machines. Vidya Setlur.
Securities and Exchange Commission (SEC) implemented rules on Cybersecurity Risk Management, Strategy, Governance, and Incident Disclosure for Public Companies. As the December 15 compliance deadline […] The post Fast-Tracking SEC Compliance with AI for GRC and Cybersecurity Disclosure appeared first on DATAVERSITY.
Editor’s note: This article originally appeared in Forbes. Let’s look at a few focus areas of a people-centric strategy to help you achieve trusted data and successful AI projects: your dataarchitecture, the processes for managing governeddata, and balancing the roles of people and machines. Vidya Setlur.
Those of us in the field of enterprise data management are familiar with the many authors contributing their knowledge and expertise to the data management body of knowledge.[1] 1] We are also very familiar with the many, varied, and often conflicting ways in which data management terms are used.
In her groundbreaking article, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh, Zhamak Dehghani made the case for building data mesh as the next generation of enterprise data platform architecture.
The transition from hybrid to multi-cloud environments is more than just a buzzword: It’s a fundamental shift in how organizations manage and utilize their data. As these complex architectures evolve, the importance of robust multi-cloud datagovernance cannot be overstated.
Watching closely the evolution of metadata platforms (later rechristened as DataGovernance platforms due to their focus), as somebody who has implemented and built DataGovernance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.
Editor's note: This article originally appeared in Forbes , by Wendy Turner-Williams, Chief Data Officer, Tableau. In today’s fast-paced world of competing business priorities, the capacity to enable self-service data analytics with right-sized datagovernance is key. The problem data fabrics are designed to solve.
Biometrics was once a novelty reserved for spy movies and top-secret government facilities. While biometric data security technology offers advantages not available through conventional passwords, it also has unique privacy risks and limitations. What Is Biometric Data?
Editor's note: This article originally appeared in Forbes , by Wendy Turner-Williams, Chief Data Officer, Tableau. In today’s fast-paced world of competing business priorities, the capacity to enable self-service data analytics with right-sized datagovernance is key. The problem data fabrics are designed to solve.
Editor's note: This article originally appeared in Forbes. Establishing a Data Culture—one in which teams value, practice, and encourage using data to make decisions—is a key step toward building a data-driven organization that thrives in today’s dynamic environment. . The cloud strengthens Data Culture by.
In the fast-evolving data landscape, understanding emerging trends and embracing technological advancements are key to staying ahead. As we approach 2024, this article explores the data trends that will define the strategic landscape for the coming year.
that are responsible for the management, governance, and […]. We speak to enterprises large and small about cloud cost optimization, and one of the more dominant themes we have been hearing lately is: Who should manage app development costs? Cloud Operations teams (ITOps, DevOps, FinOps, Cloud Center of Excellence, etc.)
Being primarily transformation of the business, it is a response to digital strategy, thus connected with a wide range of topics – including DataGovernance and Enterprise Architecture. The topic of agile transformation is emerging, either driven “bottom-up” from product leaders or “top-down” by C-level executives.
As I’ve been working to challenge the status quo on DataGovernance – I get a lot of questions about how it will “really” work. The Business Dislikes Our Data Warehouse appeared first on DATAVERSITY. Click to learn more about author Laura Madsen. Welcome to the Dear Laura blog series! Last year I wrote […].
As I’ve been working to challenge the status quo on DataGovernance – I get a lot of questions about how it will “really” work. The Business Dislikes Our Data Warehouse appeared first on DATAVERSITY. Click to learn more about author Laura Madsen. Welcome to the Dear Laura blog series! Last year I wrote […].
Many business teams do not have a clear understanding of who is responsible for maintaining data quality. And should duplicate data or errors be found, many do not know where to report quality issues. Managing permissions, access, and governance at scale. Governance Toolkit. Big data is on the rise. What’s left?
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