Tag: analytics

Feed: R-bloggers. Author: Mango Solutions. Mango Solutions are delighted to announce the full EARL Boston agenda for this coming November! As always, we received more abstracts than we could accept and all were of excellent quality. Because of this, the decision-making process was not easy; however, we believe we’ve put together a great agenda that showcases how a range of industries are solving business problems with R. We’re confident that R users of all levels and from all industries will find the EARL Boston Conference useful. On to the speakers! Joining David, Mara and Tareef, are speakers from companies such ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Friday, September 15, 2017 By Nick Mihailovski, Product Manager Editor’s note: Last week, the Google Data Studio team announced new features that make it even more useful for creating and sharing rich, visual reports that accelerate time-to-insight from data in a Google BigQuery-powered data warehouse. We’ve re-posted a version of the original announcement below for your convenience. Over the past six months we’ve launched over 25 new features in Google Data Studio including: ... Read More

Feed: Big Data Made Simple - One source. Many perspectives.. Author: Baiju NT. Big Data is amazing. It describes our everyday behavior, keeps track of the places we go, stores what we like to do and how much time we spend doing our favorite activities. Big Data is made of numbers, and I think we all agree when we say: Numbers are difficult to look at. Enter Big Data visualization. Google, Facebook, Amazon, Apple, Twitter and Netflix all ask better questions of their data – and make better business decisions – by using data visualization. It gets better: Data visualization lets you interact with data. It goes beyond analysis. Visualization brings a presentation ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Matt Spillar. Lloyds Banking Group has been awarded this year’s data accolade for its Digital Analytics System Delivery (DASD) project. The goal was to build a data environment that could be used to derive predictive models for fraud detection. Lloyds Banking Group partnered with Hortonworks to create a data analytics platform and integrated a data science toolkit suite. The DASD platform infrastructure and application components enable improved ingestion, transformation and processing of data via distribution and storage of multiple sources. By partnering with Hortonworks, the Group was able to put into place an integrated ... Read More

Feed: SAS Blogs. Author: Randy Guard. Have you heard the term “analytics economy” and wondered what it means? Or maybe you’ve wondered how your organization can use data and analytics to achieve economic gains. Now we have more than just data. We have accessible data, fueled by advances in compute power and connectivity, and interpreted by ever-more powerful analytics.At SAS, we’ve been talking about the analytics economy a lot lately. So naturally, people ask me what exactly we mean by that. After all, we’ve been talking about "data everywhere" since the big data craze a few years ago...so what’s new now? The ... Read More

Feed: Planet big data. Author: Joana van den Brink-Quintanilha. The CX leadership gapGet an exclusive look at the 2017 CX Index results and discover how to create breakaway CX. Register for the webinar    Why is journey analytics such a hot topic? Because it can help firms move the needle on customer obsession. That’s why after months of research — including in-depth briefings, demos, and customer references — we’re excited to announce the release of not one but two Forrester Wave’s focused on customer journey analytics platforms! Customer journey analytics is: An analytics practice that combines quantitative and qualitative data to ... Read More

Feed: SAS Blogs. Author: Kristoffer Nilsson. Creating an architecture to support AI is about creating a modern platform for advanced analytics, and means being able to support all steps of the analytics lifecycle. Requirements An architecture for advanced analytics/artificial intelligence need to cover three main domains, data, discovery and deployment. Data Data is the foundation of analytics: after all, we need something to analyse. Data can come from a multitude of places and include a multitude of types such as: Traditional data in data warehouses and operational systems, most commonly relational databases and files such as CSV and XML. Because ... Read More

Feed: All Oracle Press Releases. Oracle today announced its eighth generation SPARC platform, delivering new levels of security capabilities, performance, and availability for critical customer workloads. Powered by the new SPARC M8 microprocessor, new Oracle systems and IaaS deliver a modern enterprise platform, including proven Software in Silicon with new v2 advancements, enabling customers to cost-effectively deploy their most critical business applications and scale-out application environments with extreme performance both on-premises and in Oracle Cloud. SPARC M8 processor-based systems, including the Oracle SuperCluster M8 engineered systems and SPARC T8 and M8 servers, are designed to seamlessly integrate with existing infrastructures ... Read More

Feed: Microsoft Power BI Blog | Microsoft Power BI. Author: . This blog post covers the latest updates for Power BI Developers community. Don’t forget to check out the August blog post, if you haven’t done so already. Here’s the complete list of September updates for the Power BI Embedded APIs Clone tile & dashboard RLS for AS on-prem RLS additional properties to dataset Export/Clone PBIX (model and reports) Language configuration Clone Dashboard and Dashboard Tiles As an ISV, we recommend supporting multiple workspaces for your application’s embedded analytics topology. You can accomplish this by creating a main workspace which ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. Employee turnvover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques. First, we’ll use the h2o package’s new FREE automatic machine learning algorithm, h2o.automl(), to ... Read More