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Data quality is no longer a back-office concern. In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries. I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. Complex orgs with mature data capabilities.
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.
Reading Time: 2 minutes As the volume, variety, and velocity of data continue to surge, organizations still struggle to gain meaningful insights. This is where active metadata comes in. Listen to “Why is Active Metadata Management Essential?” What is Active Metadata? ” on Spreaker.
Will the new creative, diverse and scalable data pipelines you are building also incorporate the AI governance guardrails needed to manage and limit your organizational risk? We will tackle all these burning questions and more in this article.
Not surprisingly, dataintegration and ETL were among the top responses, with 60% currently building or evaluating solutions in this area. In an age of data-hungry algorithms, everything really begins with collecting and aggregating data. Data results from a Twitter poll. Metadata and artifacts needed for audits.
While this is a technically demanding task, the advent of ‘Payload’ Data Journeys (DJs) offers a targeted approach to meet the increasingly specific demands of Data Consumers. Payload DJs facilitate capturing metadata, lineage, and test results at each phase, enhancing tracking efficiency and reducing the risk of data loss.
Reading Time: 2 minutes In today’s data-driven landscape, the integration of raw source data into usable business objects is a pivotal step in ensuring that organizations can make informed decisions and maximize the value of their data assets. To achieve these goals, a well-structured.
Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.
KGs bring the Semantic Web paradigm to the enterprises, by introducing semantic metadata to drive data management and content management to new levels of efficiency and breaking silos to let them synergize with various forms of knowledge management. Take this restaurant, for example. Enterprise Knowledge Graphs and the Semantic Web.
Dataintegrity constraints: Many databases don’t allow for strange or unrealistic combinations of input variables and this could potentially thwart watermarking attacks. Applying dataintegrity constraints on live, incoming data streams could have the same benefits. Disparate impact analysis: see section 1.
The post My Reflections on the Gartner Hype Cycle for Data Management, 2024 appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information. Gartner Hype Cycle methodology provides a view of how.
Lower cost data processes. This article is will help you understand the critical role of information stewardship as it relates to data and analytics. These stewards monitor the input and output of dataintegrations and workflows to ensure data quality. More effective business process execution.
In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Dataintegration and Democratization fabric. Introduction to the Data Mesh Architecture and its Required Capabilities. Introduction.
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 - DataIntegration and Modern Data Management Articles, Analysis and Information.
Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore as it stores data in RDF triples). It provides the core infrastructure for solutions where modeling agility, dataintegration, relationship exploration, cross-enterprise data publishing and consumption are critical.
The particular episode we recommend looks at how WeWork struggled with understanding their data lineage so they created a metadata repository to increase visibility. Agile Data. Another podcast we think is worth a listen is Agile Data. Techcopedia follows the latest trends in data and provides comprehensive tutorials.
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 […].
Ozone is also highly available — the Ozone metadata is replicated by Apache Ratis, an implementation of the Raft consensus algorithm for high-performance replication. Since Ozone supports both Hadoop FileSystem interface and Amazon S3 interface, frameworks like Apache Spark, YARN, Hive, and Impala can automatically use Ozone to store data.
If you do a general internet search for data catalogs, all sorts of possibilities emerge. If you look closely, and ask a lot of questions, you will find that some of these products are not actually fully functional data catalogs at all. Some software products start out life-solving a specific use case related to data, […].
As noted in the Gartner Hype Cycle for Finance Data and Analytics Governance, 2023, “Through. The post My Understanding of the Gartner® Hype Cycle™ for Finance Data and Analytics Governance, 2023 appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form. Those days are long gone if they ever existed.
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.
According to this article , it costs $54,500 for every kilogram you want into space. That means removing errors, filling in missing information and harmonizing the various data sources so that there is consistency. Once that is done, data can be transformed and enriched with metadata to facilitate analysis.
While transformations edit or restructure data to meet business objectives (such as aggregating sales data, enhancing customer information, or standardizing addresses), conversions typically deal with changing data formats, such as from CSV to JSON or string to integertypes.
And data fabric is a self-service data layer that is supported in an orchestrated fashion to serve. The post Data Governance in a Data Mesh or Data Fabric Architecture appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
The post Navigating the New Data Landscape: Trends and Opportunities appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information. At TDWI, we see companies collecting traditional structured.
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published as a special topic article in AI magazine, Volume 43, Issue 1 , Spring 2022. The paper introduces KnowWhereGraph (KWG) as a solution to the ever-growing challenge of integrating heterogeneous data and building services on top of already existing open data. The catalog stores the asset’s metadata in RDF.
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).
Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
The POC was for a data. The post Getting the Fundamentals Right for Gen AI appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
To be truly “data-driven,” an organization must view data as more than a byproduct. The post How to Shop for Data? appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
But what is a data lakehouse and why should we develop one? The post The Data Lakehouse: Blending Data Warehouses and Data Lakes appeared first on Data Virtualization blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
The idea seems, on the face of it, easy to understand: a data catalog is simply a centralized inventory of the data assets within an organization. Data catalogs also seek to be the. The post Choosing a Data Catalog: Data Map or Data Delivery App?
The Denodo Platform is a logical data management platform, powered by. The post Denodo Joins Forces with Presto appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
The post Welcome to the Era of Denodo Assistant appeared first on Data Management Blog - DataIntegration and Modern Data Management Articles, Analysis and Information. With the development of large language models (LLMs) and other generative AI (GenAI) technologies in recent years, we have doubled our efforts.
Instead, it creates a unified way, sometimes called a data fabric, of accessing an organization’s data as well as 3rd party or global data in a seamless manner. Data is represented in a holistic, human-friendly and meaningful way. For efficient drug discovery, linked data is key.
One of the key considerations is how best to handle data, and this is where data mesh and data fabric come into play. The post Data Mesh vs Data Fabric: Understanding the Key Differences appeared first on Data Virtualization blog - DataIntegration and Modern Data Management Articles, Analysis and Information.
The post The Secret Sauce of LeasePlan’s Award-Winning Logical Data Fabric appeared first on Data Virtualization blog - DataIntegration and Modern Data Management Articles, Analysis and Information. This is a testament to the maturity of.
When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” ” through a truly data literate organization. What is data democratization? Security Data security is a high priority.
From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), dataintegration, search and information discovery, built on top of the metaphactory platform.
Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore because it stores data in RDF triples). It provides the core infrastructure for solutions where modelling agility, dataintegration, relationship exploration, cross-enterprise data publishing and consumption are critical. .
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