This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
But while state and local governments seek to improve policies, decision making, and the services constituents rely upon, data silos create accessibility and sharing challenges that hinder public sector agencies from transforming their data into a strategic asset and leveraging it for the common good. . Modern dataarchitectures.
They use data better. How does Spotify win against a competitor like Apple? Using machine learning and AI, Spotify creates value for their users by providing a more personalized experience.
Reading Time: 3 minutes While cleaning up our archive recently, I found an old article published in 1976 about data dictionary/directory systems (DD/DS). Nowadays, we no longer use the term DD/DS, but “data catalog” or simply “metadata system”. It was written by L.
Each of these trends claim to be complete models for their dataarchitectures to solve the “everything everywhere all at once” problem. Data teams are confused as to whether they should get on the bandwagon of just one of these trends or pick a combination. First, we describe how data mesh and data fabric could be related.
The main goal of creating an enterprise data fabric is not new. It is the ability to deliver the right data at the right time, in the right shape, and to the right data consumer, irrespective of how and where it is stored. Data fabric is the common “net” that stitches integrated data from multiple data […].
Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.
The initial dataset consisted of 13,202 journal articles relevant to novel coronavirus research. Ontotext’s knowledge graph technology is at the core of Cochrane’s dataarchitecture developed by our partners from Data Language. medications: 16,406 instances. procedures: 54,720 instances. To Sum It Up.
With Cloudera’s vision of hybrid data , enterprises adopting an open data lakehouse can easily get application interoperability and portability to and from on premises environments and any public cloud without worrying about data scaling. Why integrate Apache Iceberg with Cloudera Data Platform?
A recent VentureBeat article , “4 AI trends: It’s all about scale in 2022 (so far),” highlighted the importance of scalability. The article goes on to share insights from experts at Gartner, PwC, John Deere, and Cloudera that shine a light on the critical role that data plays in scaling AI. . Data science needs analytics.
I said I thought it affected all of them pretty profoundly, but perhaps the Metadata wedge the most. Recently, I was giving a presentation and someone asked me which segment of “the DAMA wheel” did I think semantics most affected. I thought I’d spend a bit of time to reflect on the question and answer […].
Standards exist for naming conventions, abbreviations and other pertinent metadata properties. Consistent business meaning is important because distinctions between business terms are not typically well defined or documented. What are the standards for writing […].
Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern dataarchitecture is critical in order to become a data-driven organization. Mike is the author of two books and numerous articles.
The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.
Reading Time: 2 minutes Data mesh is a modern, distributed dataarchitecture in which different domain based data products are owned by different groups within an organization. And data fabric is a self-service data layer that is supported in an orchestrated fashion to serve.
As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.
No this article has not escaped from my Maths & Science section , it is actually about data matters. The image at the start of this article is of an Ichthyosaur (top) and Dolphin. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation.
The post My Reflections on the Gartner Hype Cycle for Data Management, 2024 appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. Gartner Hype Cycle methodology provides a view of how.
The post Querying Minds Want to Know: Can a Data Fabric and RAG Clean up LLMs? – Part 4 : Intelligent Autonomous Agents appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
Reading Time: 2 minutes In recent years, there has been a growing interest in dataarchitecture. One of the key considerations is how best to handle data, and this is where data mesh and data fabric come into play. But what are the key.
In this article, we are bringing science fiction to the semantic technology (and data management) talk to shed some light on three common data challenges: the storage, retrieval and security of information. We will talk through these from the perspective of Linked Data (and cyberpunk).
They can provide the businessperson, analyst, architect, system engineer and data scientist alike the ability to look for data and understand what it contains. They require proper curation and a good understanding of data/metadata, privacy/security, and technical ecosystem to be effective. However, they are not magic.
This article endeavors to alleviate those confusions. Data fabric promotes data discoverability. Here, data assets can be published into categories, creating an enterprise-wide data marketplace. This marketplace provides a search mechanism, utilizing metadata and a knowledge graph to enable asset discovery.
In the first article, I introduced and explained the approach to application development called Domain-Driven Development (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed data model can add value to a DDD project by helping to create the Ubiquitous Language that defines the Bounded Context (..)
Canonical Data Models and Overlapping Connections In the previous article, I introduced and explained the approach to application development called ‘Domain-Driven Development’ (or DDD), explained some of the Data Management concerns with this approach, and described how a well-constructed data model can add value to a DDD project by helping to create (..)
Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. We are in the era of graphs. Graphs are hot. Graphs deliver it in spades. Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say […].
The post Navigating the New Data Landscape: Trends and Opportunities appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. At TDWI, we see companies collecting traditional structured.
In a world of exponential data growth and ever-increasing silo-ization, the FAIR principles are needed more than ever. In this article, we will first summarize the FAIR principles and describe the […]. FAIR stands for: Findable, Accessible, Interoperable and Reusable.
It has been an incredible run. I hope it is just “see you soon” rather than “goodbye.” With this column, DAMA International’s streak of quarterly columns since mid-2001 is coming to an end. The columns have featured the activities and incredible work of DAMA International over the past two decades. Thank you, DAMA, and I […].
The Irish satirist Jonathan Swift wrote “Gulliver’s Travels” almost 300 years ago, but the story of Lemuel Gulliver’s journey to Lilliput and beyond has resonance for data leaders today. There are important lessons to learn from the little people of Lilliput and the challenges encountered by the eponymous Gulliver.
Bounded Contexts / Ubiquitous Language My new book, Data Model Storytelling,[i] contains a section describing some of the most significant challenges data modelers and other Data professionals face. Like most of its predecessors, including Agile development and […].
Data catalogs also seek to be the. The post Choosing a Data Catalog: Data Map or Data Delivery App? appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information.
Data Governance 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.
You would think that after knocking around in semantics and knowledge graphs for over two decades I’d have had a pretty good idea about Knowledge Management, but it turns out I didn’t. I think in the rare event the term came up I internally conflated it with Knowledge Graphs and moved on. The first tap […]
More specifically, it describes the process of creating, administering, and adapting a comprehensive plan for how an organization’s data will be managed. In this way, data governance has implications for a wide range of data management disciplines, including dataarchitecture, quality, security, metadata, and more.
There is no denying that database administration requires a bevy of technical know-how. The DBA is the information technician responsible for ensuring the ongoing operational functionality and efficiency of an organization’s databases and the applications that access those databases.
Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
Differences in Terminology and Capability Building on the terms and concepts introduced in Part I of this white paper, Part II digs deeper into the difference in the meaning of some key terms used in both Property Graphs and Knowledge Graphs, including LABELS, TYPES, and PROPERTIES. Key terms such as these actually mean very different […].
Executive Summary It seems obvious enough that companies, government agencies and non-profits would benefit from a common language. Without it, coordinating work is more difficult, computers “don’t talk,” and basic questions such as “how many customers do we have?” yield differing answers, making it more difficult to run the business.
In the world of data, automation plays a well-honed role in rapidly developing modern data estates. Before we proceed any further, let’s establish an understanding about the purpose of a corporate data estate. A data estate is the technical architecture and enterprise infrastructure that enables organizations to […].
Deep learning, as defined by MathWorks, is a system of artificial intelligence that is built around learning by example. Multiple industries have already understood the benefits that deep learning brings to their operational capabilities.
Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].
Wherever we go, we are overwhelmed by MORE: more sales, more discounts, more fun, more excitement, more features – the list goes on and on! What humans seem to be far less attuned to is reducing what we don’t need. Drive around any suburban neighborhood and see the many cars parked outside their garages! Believe […].
The third and final part of the Non-Invasive Data Governance Framework details the breakdown of components by level, providing considerations for what must be included at the intersections. The squares are completed with nouns and verbs that provide direction for meaningful discussions about how the program will be set up and operate.
More specifically, it describes the process of creating, administering, and adapting a comprehensive plan for how an organization’s data will be managed. In this way, data governance has implications for a wide range of data management disciplines, including dataarchitecture, quality, security, metadata, and more.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content