new ...•Data pipeline automation remains fractured. Data teams use a relatively equivalent mix of job schedulers, scripts, enterprise workload automation, and open-source schedulers to automate their data pipelines. No data team uses only one tool. Rather, most data teams employ all the different automation methods — indicating an opportunity to implement an orchestration layer for centralized management and observability. — Digitalisation World, 9h ago

new .... “However, many companies are still struggling to keep up as the volume and variety of workloads increase, and more data is highly fragmented and distributed between on-premises, edge, and cloud. In short, their needs have extended far beyond traditional data storage and management approaches, which is why performance, scalability, and data intelligence matter now more than ever.”... — TECHTELEGRAPH, 17h ago

new Meeting the Need for Item-Level Receipt Data: Why Data Infrastructure Is Key to a Better Customer Experience... — pymnts.com, 1d ago

new Getting identity right is core to a robust zero-trust framework. It takes endpoint resilience, improved sensing and telemetry data analysis techniques, and faster innovation at protecting identities. — VentureBeat, 1d ago

new ...3. Privacy aware flow – Privacy X-Ray can help businesses visualise privacy risk in data flow and Event Horizon can enable privacy preservation of data based on context of downstream flow of data. Thus data pipelines and data lakes can be made privacy compliant. — YourStory.com, 1d ago

new In these examples, ETL is a better choice over ELT as it allows organizations to efficiently transform and combine data from multiple sources before loading it into a target database. ETL enables efficient data processing, aggregation, transformation, and cleansing before loading, ensuring high-quality data and faster analytics processing times. On the other hand, ELT may lead to slower processing times and more complex data integration workflows, especially when dealing with multiple sources and complex data transformations. — dzone.com, 2d ago

new Perhaps most important, the Gen 2 app is based entirely on a cloud enabled architecture for data storage, management and visibility. Now – with credentialed protection that is GDPR compliant – the users’ data is securely maintained in the cloud providing a significant layer of access and usability that simply wasn’t there before. For example, think of a “dashboard” that will allow our users to view their data on a laptop or pad, or having the ability to share data with workout buddies, trainers, coaches, etc. And a lot more, coming soon!... — StartEngine, 1d ago

new ..."Traditional approaches to data management and individual tools that only perform one function will not be enough to solve the growing challenges users are facing. Frankly, they don't make life easier for the different users and business functions that rely on having access to trusted data. We're looking forward to sharing our unified data management platform and unique matching and replication software solutions with conference attendees."... — prnewswire.com, 1d ago

new A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, I predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems and Strava data with Machine Learning. My goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Therefore, also an interpretable analysis using SHAP will be discussed. — Hertie School, 1d ago

new As banks aim to gain a competitive advantage by delivering more value to customers and simultaneously managing the associated risks, they need a single view of consistent, correct, and real-time data that can be accessed on demand without any friction. However, as data grows both in terms of volume and complexity, most banks are struggling to get this single view of their enterprise data. This means transitioning from a traditional enterprise data warehouse or data lake–centered architecture toward a data-fabric model that utilizes a data architecture decentralized into independent, interoperable, and business-owned data products that operate as microservices. — IDC: The premier global market intelligence company, 1d ago

new Another open-source project — Delta Sharing — claims to be the first open protocol for secure data sharing. This lets Delta Lake users or other cloud adopters transfer and share files between platforms without jeopardizing security. — Datamation, 1d ago

new A data marketplace is an online platform that allows buyers and sellers to trade datasets, while a data silo is a repository for storing and managing data. — Cryptopolitan, 1d ago

new ...“The current cyber threat environment and heightened community expectation around data protection and retention highlights inherent tension between data sharing and data protection,” he said. — Australian Financial Review, 1d ago

new Existing certificates are not up to the task. They lack precision concerning time, place and source of production and cannot be used throughout the complete supply chain and lifecycle of products. The landscape of existing data interfaces is highly inhomogeneous which leads to massive inefficiencies and error rates in reporting processes. — riddleandcode.com, 1d ago

new This is one of the world’s richest baseline data sets for an LLM-based conversational system. Second, we already have a rich processing layer for understanding that data, which creates billions of derived data points, including sentiment, conversation quality, user intent, user problem resolution, customer satisfaction, et cetera, much of this using prior and current generations of large language model technology already. It’s part of why we have hundreds of millions of conversations handled today by our own first-party AI products. Third, we have, as Rob mentioned, over 300,000 human experts that log into the platform every day, providing feedback and helping models learn. Each of these assets is uniquely meaningful in this new LLM context. — Insider Monkey, 1d ago

new ..., mapping and classification work. It should at the same time eliminate data being siloed in multiple and proprietary data management systems. According to Craig Milroy, former chief data architect for TD Bank, “The data lake architecture on Hadoop was the modern data stack not too long ago; now it is data mesh and data lake houses. While I am all for the decommissioning of Hadoop, I think organizations should think through the business capability enablement in selecting the next data stack.”... — 7wData, 2d ago

new ...– some maritime technology vendors show data as-is, while others do basic clean-up. Windward heavily invests in cleaning the data, because if data is not clean, everything built on top of that flawed foundation will be wobbly. Maritime domain expertise is key for understanding and constantly evaluating data points. — Windward, 2d ago

new Sourcing and using human capital data and building talent superhighways is a journey—not something established overnight. However, most companies may be collecting this data without realizing it. Thanks to technological advances in cloud computing, AI, and more, the data sets companies can access in real-time have tremendously disrupted how we meet market demands and assist clients. We can and should also use this to align employee purpose with organizational purpose. — Quartz, 1d ago

new A great example of high-value digitalization is converting paper-based methods including inventory, production tracking and quality information. When digitalized, this information, which has typically been siloed and locked in disparate systems, is easily shared and used to broadly communicate, empowering every part of the organization to share and leverage data. The inefficiency of using paper forms contributes to low worker productivity and a significant probability of inaccurate information. Paper-based methods can now be digitized cost-effectively to increase productivity using no-code platforms without programmers. The new platforms using high performance and low cost of tablet computers and smartphones coupled with no-code application development software puts digital systems in frontline workers hands. This is analogous to how spreadsheets became a huge enabler providing nonprogrammers with the ability to leverage the power of computing. — automation.com, 2d ago

new ..., everyone is scrambling to figure out how to use collected factory data to feed these intelligent systems. Most electronics manufacturers are sitting on troves of data and are looking to maximize that untapped potential to better optimize manufacturing efficiency. As with all things related to connectivity and data in this industry, it isn’t as simple as feeding the AI tool data as-is, the structure and format of the data is paramount. — evertiq.com, 2d ago

new The big picture concept of supernets is that they are collections of interconnected networks that promote collaborative work while serving as a secure data-sharing hub. They can hold and organize large quantities of data and facilitate communication among users. — Bitcoin Insider, 2d ago

new As much as the success of data clean rooms are predicated on data shared by platforms, advertisers also need to pony up. And yet advertisers either don’t or don’t always want to share detailed transactional data, due to the privacy risk. That can make measurement rough at best. Those difficulties meant that Unilever resorted to a panel-based solution as the source of the first-party data it puts into its own data clean room. Doing so gives it an estimate of the true frequency and reach of its ads, but the panel isn’t active in the market for products and thus won’t be great for attribution purposes. — Digiday, 1d ago

new As graph technology grows in popularity, more and more database vendors offer “graph” capabilities alongside their existing data models (such as relational, document, wide column, key-value, or other NoSQL stores). But, the trouble with these graph add-on offerings is that they’re not optimized to store and query the connections between data entities. — dzone.com, 2d ago

new Efforts to address TikTok’s privacy and security threats must prioritize principles of data minimization, transparency, and accountability to protect the public and preserve democratic values. For decades, Congress has tiptoed around the issue and created a space for TikTok and the whole social media industry to thrive from damaging data harvesting practices. Rather than the surface-level solution of a ban on one problematic platform, the government must prioritize enacting a federal data protection law that tackles the real problem: how companies harvest and monetize our data. — Tech Policy Press, 2d ago

new As the frameworks accompanying cybersecurity mandates and compliance guidelines are also refined, many now encourage (and sometimes mandate) that businesses transition to a proactive, risk-based approach – one that establishes their liability based on the type of data they’re collecting and how it’s used. At the same time, many data-centric cybersecurity frameworks are pushing the industry towards full proactive prioritization and risk ranking gap analysis to enable an accurate measure of system risk while reducing the resources and time required for compliance. This collision of data privacy concerns and the associated regulations with cybersecurity frameworks is overwhelming for companies trying to strengthen their security and compliance posture. — CPO Magazine, 2d ago