data science

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Published By: Pure Storage     Published Date: Apr 18, 2018
Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data. Amazon CEO Jeff Bezos spoke about the potential of artificial intelligence and machine learning at the 2017 Internet Association‘s annual gala in Washington, D.C., “It is a renaissance, it is a golden age,” Bezos said. “We are solving problems with machine learning and artificial intelligence that were in the realm of science fiction for the last several decades. Natural language understanding, machine vision problems, it really is an amazing renaissance.” Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy
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Pure Storage
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
IBM Cloud Private for Data is an integrated data science, data engineering and app building platform built on top of IBM Cloud Private (ICP). The latter is intended to a) provide all the benefits of cloud computing but inside your firewall and b) provide a stepping-stone, should you want one, to broader (public) cloud deployments. Further, ICP has a micro-services architecture, which has additional benefits, which we will discuss. Going beyond this, ICP for Data itself is intended to provide an environment that will make it easier to implement datadriven processes and operations and, more particularly, to support both the development of AI and machine learning capabilities, and their deployment. This last point is important because there can easily be a disconnect Executive summary between data scientists (who often work for business departments) and the people (usually IT) who need to operationalise the work of those data scientists
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Group M_IBM Q2'19
Published By: Domino Data Lab     Published Date: Feb 08, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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Domino Data Lab
Published By: Domino Data Lab     Published Date: Feb 08, 2019
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform. This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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Domino Data Lab
Published By: Domino Data Lab     Published Date: Feb 08, 2019
As organizations increasingly strive to become model-driven, they recognize the necessity of a data science platform. According to a recent survey report “Key Factors on the Journey to Become Model-Driven”, 86% of model-driven companies differentiate themselves by using a data science platform. And yet the question of whether to build or buy still remains. This paper presents a framework to facilitate the decision process, and considers the four-year projection of total costs for both approaches in a sample scenario. Read this whitepaper to understand three major factors in your decision process: Total cost of ownership - Internal build costs often run into the tens of millions Opportunity costs - Distraction from your core competency Risk factors - Missed deadlines and delayed time to market
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Domino Data Lab
Published By: Uberflip     Published Date: Dec 20, 2018
In today’s world, marketers know that producing content isn’t enough. If they’re going to continue to make an investment in creating content, they need to do more to ensure it performs. We’ve long since known that combining content with a remarkable experience will allow it to reach its full potential, and allow marketers to see results. But as with any emerging category, content experience was not without its detractors. After all, what kind of results could you expect from an investment in the experience around that content? If you’ve ever wondered why you should care about content experience, and wanted something a little more concrete than a few anecdotes from marketers, or third-party stats, then look no further.
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Uberflip
Published By: AWS     Published Date: Dec 15, 2017
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time. AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance. In this webinar, you’ll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences. Join this webinar to learn: How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development. How AWS supports BI solutions f
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AWS
Published By: Adobe     Published Date: Nov 09, 2017
Marketing leaders are asking their analytics teams to provide better insights into customers, prospects and journeys, and a more accurate assessment of the impact of marketing tactics. Use this research to find a digital marketing analytics tool to support your needs. This Magic Quadrant is intended for chief marketing of?cers (CMOs), marketing analytics and data science practitioners, and other digital marketing leaders involved in the selection of systems to support marketing analytics requirements.
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Adobe
Published By: Dome9     Published Date: Apr 25, 2018
Last year at this time, we forecast a bumpy ride for infosec through 2017, as ransomware continued to wreak havoc and new threats emerged to target a burgeoning Internet of Things (IoT) landscape. ‘New IT’ concepts – from DevOps to various manifestations of the impact of cloud – seemed poised to both revolutionize and disrupt not only the implementation of security technology, but also the expertise required of security professionals as well. Our expectations for the coming year seem comparatively much more harmonious, as disruptive trends of prior years consolidate their gains. At center stage is the visibility wrought by advances in data science, which has given new life to threat detection and prevention – to the extent that we expect analytics to become a pervasive aspect of offerings throughout the security market in 2018. This visibility has unleashed the potential for automation to become more widely adopted, and not a moment too soon, given the scale and complexity of the thre
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Dome9
Published By: Oracle     Published Date: Dec 21, 2018
Join Oracle’s CX and Marketing Strategy Director, Wendy Hogan, and Senior Vice President Oracle Marketing, Shashi Seth, as they tell how AI, machine learning and data science can engage customers, automate tasks and build ROI. Reaching the right customers on the right channel at the right time, brings rewards for CMOs who embrace these innovations, including engaged customers and increased ROI. Be inspired by the new-generation AI, machine learning and data science and take your marketing to the next level.
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Oracle
Published By: SAS     Published Date: Nov 04, 2015
In a panel discussion at the 12th annual SAS Health Analytics Executive Forum in May 2015, leaders from Dignity Health, Horizon Blue Cross Blue Shield of New Jersey, Janssen Pharmaceuticals and SAS shared what they have done to prove the value of analytics to their business leaders – and what has worked for them as they developed an analytic culture in their organizations and put analytic insights to work.
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SAS
Published By: Sage People     Published Date: Jan 22, 2019
In our latest survey report, we explore the growth challenges facing businesses and HR leaders in a rapidly changing landscape. We surveyed over 500 HR leaders in leading organisations to explore their views on these challenges, and to find out how they are supporting people and leveraging people data to help them achieve their growth goals. The survey revealed that: • It’s the war for talent, again. The greatest challenges for growing companies are winning the war for talent, growing productivity and improving workforce visibility. • Fast-growth companies share common traits in the way they manage and engage their people—we call this being a People Company. • There’s a disconnect between managers and employees in terms of what being a People Company means. • Becoming a People Company is a journey, with many organisations some way from embracing all aspects. • People Science is a thing: there’s an appetite to leverage people data and analytics, but there are blockers in the way. Re
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Sage People
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