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Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful datagovernance. Recognize that artificial intelligence is a datagovernance accelerator and a process that must be governed to monitor ethical considerations and risk.
Summary Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Datagovernance is the binding force between these two parts of the organization. At what point does a lack of an explicit governance policy become a liability?
Disclaimer: Throughout this post, I discuss a variety of complex technologies but avoid trying to explain how these technologies work. The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. Then came Big Data and Hadoop!
Summary Datagovernance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. What is datagovernance?
The team at Satori has a background in cybersecurity and they are using the lessons that they learned in that field to address the challenge of access control and auditing for datagovernance. This is an interesting conversation about the intersection of data and security and the lessons that can be learned in each direction.
Summary Datagovernance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in datamanagement adds additional stress to an already complex endeavor. Closing Announcements Thank you for listening!
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement Dagster offers a new approach to building and running data platforms and data pipelines. Can you describe the operational/architectural aspects of building a full data engine on top of the FDAP stack?
Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and datagovernance are the top data integrity challenges, and priorities. However, they require a strong data foundation to be effective.
Summary Datagovernance is a phrase that means many different things to many different people. This is because it is actually a concept that encompasses the entire lifecycle of data, across all of the people in an organization who interact with it. data stewards, business glossaries, etc.)
I am pleased to announce that Cloudera was just named the Risk Data Repository and DataManagement Product of the Year in the Risk Markets Technology Awards 2021. . Supporting the industry’s risk data depository and datamanagement needs. End-to-end Data Lifecycle.
Summary Datagovernance is a complex endeavor, but scaling it to meet the needs of a complex or globally distributed organization requires a well considered and coherent strategy. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council.
In an effort to better understand where datagovernance is heading, we spoke with top executives from IT, healthcare, and finance to hear their thoughts on the biggest trends, key challenges, and what insights they would recommend. Get the Trendbook What is the Impact of DataGovernance on GenAI?
Key Takeaways: Data mesh is a decentralized approach to datamanagement, designed to shift creation and ownership of data products to domain-specific teams. Data fabric is a unified approach to datamanagement, creating a consistent way to manage, access, and share data across distributed environments.
In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. What do you have planned for the future of Trino/Starburst?
Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust datagovernance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications.
As this realization grows, businesses are shifting their investments from hardware to technologies that optimize data assets. Master DataManagement systems (MDM) play an important role in harmonizing data assets across large and midsize enterprises. How can they contribute their expertise?
How will my data stay secure and governed? A critical part of this decision is determining which foundational technology to build infrastructure on. Will it be easy to use for my entire team? What will costs look like?
Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement Introducing RudderStack Profiles. Can you describe your experiences with Kafka?
This is not surprising when you consider all the benefits, such as reducing complexity [and] costs and enabling zero-copy data access (ideal for centralizing datagovernance). Those requirements can be fulfilled by leveraging cloud infrastructure and services.
He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagementData lakes are notoriously complex. Can you describe what Synq is and the story behind it?
When most people think of master datamanagement, they first think of customers and products. But master data encompasses so much more than data about customers and products. Challenges of Master DataManagement A decade ago, master datamanagement (MDM) was a much simpler proposition than it is today.
Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and datagovernance are the top data integrity challenges, and priorities. However, they require a strong data foundation to be effective.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagementData lakes are notoriously complex. Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. Closing Announcements Thank you for listening!
But as I shared in a previous post, “Data Integrity for AI: Whats Old is New Again,” much of this advice surrounding AI will be hauntingly familiar to those of you who have journeyed in the datamanagement and analytics space through the last three decades. “ Prioritize data quality.
In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. Can you describe what role Trino and Iceberg play in Stripe's data architecture?
Summary Generative AI has rapidly transformed everything in the technology sector. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement Dagster offers a new approach to building and running data platforms and data pipelines.
In this episode CEO and co-founder of Isima Darshan Rawal explains how the biOS platform is architected to enable ease of use, the challenges that were involved in building an entirely new system from scratch, and how it can integrate with the rest of your data platform to allow for incremental adoption. What is the story behind the name?
Enterprise Challenges in 2024 and Beyond The big-picture process of building data that is accurate, consistent and contextual – or data integrity – calls for a systematic approach combining technology tools, internal change management, and a company-wide commitment to results.
According to IDC , 50% of enterprises in the United States say that using data and intelligence strategically to create competitive differentiation is critical to running a successful digital business. Datagovernance has emerged as a key success factor for companies aiming to innovate, improve efficiency, and drive competitive advantage.
In 2025, its more important than ever to make data-driven decisions, cut costs, and improve efficiency especially in the face of major challenges due to higher manufacturing costs, disruptive new technologies like artificial intelligence (AI), and tougher global competition. In fact, its second only to data quality.
Datagovernance refers to the set of policies, procedures, mix of people and standards that organisations put in place to manage their data assets. It involves establishing a framework for datamanagement that ensures data quality, privacy, security, and compliance with regulatory requirements.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement This episode is supported by Code Comments, an original podcast from Red Hat. Data observability has been gaining adoption for a number of years now, with a large focus on data warehouses.
Datagovernance is rapidly shifting from a leading-edge practice to a must-have framework for today’s enterprises. Although the term has been around for several decades, it is only now emerging as a widespread practice, as organizations experience the pain and compliance challenges associated with ungoverned data.
For some companies that do have a formal strategy, that strategy may be little more than a technical exercise, the primary purpose of which is to lay out the nuts and bolts of datamanagement, compliance, and similar baseline requirements. Datagovernance plays a critical role in any effective data strategy.
In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagementData lakes are notoriously complex.
In this episode Pete Hunt, CEO of Dagster labs, outlines these new capabilities, how they reduce the burden on data teams, and the increased collaboration that they enable across teams and business units. Can you describe what the focus of Dagster+ is and the story behind it? What problems are you trying to solve with Dagster+?
As we approach 2025, data teams find themselves at a pivotal juncture. The rapid evolution of technology and the increasing demand for data-driven insights have placed immense pressure on these teams. In this blog post, we’ll explore key strategies that data teams should adopt to prepare for the year ahead.
At Snowflake, we are dedicated to helping our customers effectively mobilize their data while upholding stringent standards for compliance and datagovernance. Today, we are thrilled to announce the general availability of the DataGovernance Interface in Snowsight.
Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. What do you have planned for the future of your academic research?
Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. What do you have planned for the future of Cube?
Data observability continuously monitors data pipelines and alerts you to errors and anomalies. Datagovernance ensures AI models have access to all necessary information and that the data is used responsibly in compliance with privacy, security, and other relevant policies. stored: where is it located?
Both traditional and AI data engineers should be fluent in SQL for managing structured data, but AI data engineers should be proficient in NoSQL databases as well for unstructured datamanagement. AI data engineers are the first line of defense against unreliable data pipelines that serve AI models.
My guest this week is Kulani Likotsi , the Head of DataManagement and DataGovernance at one of the four biggest banks in Africa. She’s had a rising career journey going from an analyst, to a Business Intelligence developer, to the data warehouse team, to the datagovernance team.
According to a recent report on data integrity trends from Drexel University’s LeBow College of Business , 41% reported that datagovernance was a top priority for their data programs. Automating functions in support of datagovernance provides a range of important benefits.
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