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data quality

Talend Winter '20 Adds AI, Unified Features To Better Reveal Intelligence in Data


Talend has released the latest update to its Talend Data Fabric platform is adding several new features, including AI/ML, to more quickly reveal latent intelligence held inside dispersed enterprise data. The Talend Winter '20 release delivers trusted data quickly, reliably and at first sight for faster business outcomes, according to Talend execs. "The innovations introduced in Talend Data Fabric will provide our customers with dramatically improved efficiency, optimized productivity and scale, and accelerated path to revealing value from data," said Talend's Ciaran Dynes senior vice president products in a statement. Here's a list of notable features in Talend's Winter '20 release, and how they deliver value. Data Inventory: This new cloud-based app automatically inventories and quality checks data to reveal trusted data quickly and easily.

ML Infrastructure Tools for Data Preparation


Artificial Intelligence (AI) and Machine Learning (ML) are being adopted by businesses in almost every industry. Many businesses are looking towards ML Infrastructure platforms to propel their movement of leveraging AI in their business. Understanding the various platforms and offerings can be a challenge. The ML Infrastructure space is crowded, confusing, and complex. There are a number of platforms and tools spanning a variety of functions across the model building workflow.

Talend Accelerates Path to Revealing the Intelligence in Data - Talend Real-Time Open Source Data Integration Software


Establish data intelligence at first sight: Data Inventory is a new cloud-based app designed for self-service that automatically inventories and quality checks data to establish data intelligence quickly and easily. It unlocks data silos and fosters collaboration and reuse so that data professionals don't need to repeatedly build the same datasets. Boost data intelligence at speed: With new intelligent data preparation capabilities, Talend Pipeline Designer, a modern, cloud-based solution for building and deploying data pipelines, now provides an in-flight data quality feature that eliminates quality problems before the data is consumed or replicated. Additionally, no coding or complex transformations are required, which will help citizen integrators increase development and maintain productivity. Empower everyone with data intelligence at scale: Intelligent data quality with augmented intelligence uses machine learning to compare data matching so customers can empower everyone in the organization with data intelligence.

Machine Learning Playing an Important Role in Data Management


AI (ML) has been utilized for a long time in different industries to drive new business, increase productivity, reduce risk and improve consumer satisfaction. However, within data management, widespread adoption still can't seem to progress. One issue is that use cases and capacities of ML related to data management are not constantly comprehended by operational teams. Another is that the undeniable use cases require high levels of accuracy, while the accuracy of ML methods is as of now observed as hard to anticipate. Above all, there is a strong everyday spotlight on delivering cleansed data to downstream applications, for example, risk, trade support, and compliance engines, leaving little time to improve or set out on apparent, large undertakings.

Artificial Intelligence and the Integrated Review: The Need for Strategic Prioritisation


The government's Integrated Review comes at a time of considerable technological change. The UK has entered a'Fourth Industrial Revolution' (4IR), which promises to'fundamentally alter the way we live, work, and relate to one another'. This new era will be characterised by scientific breakthroughs in fields such as the Internet of Things, Blockchain, quantum computing, fifth-generation wireless technologies (5G), robotics, and artificial intelligence (AI), which together are expected to deliver transformational changes across almost every sector of the economy. Of particular note are recent developments in AI, specifically advances in the sub-field of machine learning. Progress over the last decade has been driven by an exponential growth in computing power, coupled with increased availability of vast datasets with which to train machine learning algorithms.

Harnessing the Transformative Power of Data Science


The power of Data Analytics for Business is a well-known practice that has been proving its mettle since the 1980s. Data Science has recently been defined as an evolution of Analytics and is considered to be an umbrella term that groups several tactical components. A few examples of these tactics include Artificial Intelligence (AI), Machine Learning, ETLs data cleanup, Data Visualisation, Algorithm and Infrastructure. So how is rebirth of this analytical evaluation set to transform the business world as we know it today? Digital transformation can be a tricky beast and developing new digital products and services fast enough to stay abreast of this rapidly developing industry is challenging.

A Semi-Dynamic Bus Routing Infrastructure based on MBTA Bus Data Artificial Intelligence

As traffic congestion continues growing in urban areas, more and more cities have realized that investment priority should be given to public transport modes, such as bus transit systems (BRT) instead of personal vehicles. Simply put, in congested cities, public transport modes are more efficient than personal vehicles in terms of carrying and moving people around. As city populations grow and as their economic bases shift and evolve, their housing sector adjusts, even more vehicles are expected to enter the roads each day, creating more traffic congestion. The 2012 Urban Mobility Report states that, the lack of public transportation services would have cost commuters an additional 865 million hours of delay. With growing urban population numbers, this number undoubtedly stands higher today (National Express). On average, expanding and optimizing transit services produced an economic benefit of roughly $45 million a year by connecting urban areas in the US. There is no doubt that expanding public transportation use is key to reducing traffic congestion.

Text and Data Quality Mining in CRIS


Different research institutions use research information for different purposes. Data analyses and reports based on current research information systems (CRIS) provide information about the research activities and their results. As a rule, management and controlling utilize the research information from the CRIS for reporting. For example, trend analysis helps with business strategy decisions or rapid ad-hoc analysis to respond effectively to short-term moves. Ultimately, the analysis results and the resulting interpretations and decisions depend directly on the quality of the data.

Revealing the Intelligence in your Data with Talend Winter'20 (part 1) - AnalyticsWeek


One of my favorite Talend customer success stories is the International Consortium of Investigative Journalists (ICIJ). I love this story not only because they transformed investigation journalism with data, won the Pulitzer prize for the Panama papers, and helped the public to recover billions of dollars lost to illegal tax evasion. The story is also fascinating because they managed to decipher intelligence out of highly disparate and unknown data that they got from some of the history's largest data leaks. By retro-engineering massive amounts of raw data using Talend and other innovative data management tools, they revealed some of the most important stories in the world. This is the power of data intelligence.

How High-Performing Companies Develop and Scale AI


In the latest McKinsey Global Survey on AI we noted a significant year-over-year jump in companies using AI across multiple areas of the business. And while most survey respondents said their companies have gained value from AI, some are attaining greater scale, revenue increases, and cost savings than the rest. Based on our research and experience, this is no accident; how companies build their business strategy, what foundations they put in place, and how they tackle AI adoption in the workplace can all impact their potential for transformation. Many companies that have spent years developing AI technologies are facing the stark reality that successfully scaling AI requires more than just deploying AI technology. We find that those companies finding more success in scaling efforts are more likely than others to apply a core set of practices.