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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.
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 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.
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.
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 hallmark of any successful DataGovernance implementation is awareness. The post Data Projects Should Start with DataGovernance appeared first on DATAVERSITY.
How can your company redesign its dataarchitecture without making the same mistakes all over again? The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all dataarchitecture, and […].
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.
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.
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.
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.
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 […].
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.
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.
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?
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 […].
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.
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.
Banks – and their data volumes – are at the epicenter of the world’s digital transformation. The pace of change mirrors the velocity, volume, and variety of data within the industry. It is where new products, new markets, and new touchpoints mean new – often cloud-based – ways to do business in financial services.
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.
Today’s data pipelines use transformations to convert raw data into meaningful insights. Yet, ensuring the accuracy and reliability of these transformations is no small feat – tools and methods to test the variety of data and transformation can be daunting.
Users can easily change data permissions—down to individual users—update permission policies, manage external data storage, and more. And with automated security protocols and processes, datagovernance is easier than ever. Big data is on the rise. What’s left? Nothing but opportunity. Ready, set … grow.
Unexpected (and unwanted) data transformation problems can result from 50 (or more) issues that can be seen in the table thats referenced in this blog post (see below). This post is an introduction to many causes of data transformation defects and how to avoid them.
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. June 13, 2022 - 7:04pm. June 13, 2022.
Instead of starting data protection strategies by planning backups, organizations should flip their mindset and start by planning recovery: What data needs to be recovered first? What systems […] The post World Backup Day Is So 2023 – How About World Data Resilience Day?
My company’s 2024 Data Protection Trends report revealed that 75% of organizations experience […] The post Understanding the Importance of Data Resilience appeared first on DATAVERSITY. In recent years, the frequency and sophistication of cyberattacks have surged, presenting a formidable challenge to organizations worldwide.
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. June 13, 2022 - 7:04pm. June 14, 2022.
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 […].
In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.
In todays digital age, managing and minimizing data collection is essential for maintaining business security. Prioritizing data privacy helps organizations ensure they only gather necessary information, reducing the risk of data breaches and misuse.
In todays rapidly evolving global landscape, data sovereignty has emerged as a critical challenge for enterprises. Businesses must adapt to an increasingly complex web of requirements as countries around the world tighten data regulations in an effort to ensure compliance and protect against cyberattacks.
Master data lays the foundation for your supplier and customer relationships. However, teams often fail to reap the full benefits […] The post How to Win the War Against Bad Master Data appeared first on DATAVERSITY.
Data archiving is an important aspect of datagovernance and data management. Not only does archiving help to reduce hardware and storage costs, but it is also an important aspect of long-term data retention and a key participant in regulatory compliance efforts.
In our increasingly digital world, organizations recognize the importance of securing their data. As cloud-based technologies proliferate, the need for a robust identity and access management (IAM) strategy is more critical than ever.
This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month, we’re talking about data mesh. Data mesh represents a federated model of running your data program. I’m Mark Horseman, and welcome to The Cool Kids Corner.
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