data science

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By: IBM     Published Date: Sep 02, 2014
Learn how GPFS accelerates data intensive work flows and lowers storage costs in Life Sciences, Energy Exploration, Government, Media & Entertainment and Financial Services by removing data related bottlenecks, simplifying data management at scale, empowering global collaboration, managing the full data life cycle cost effectively and ensuring end-to-end data availability, reliability, and integrity.
Tags : ibm, complete storage solution, gpfs
     IBM
By: IBM     Published Date: Sep 02, 2014
Whether engaged in genome sequencing, drug design, product analysis or risk management, life sciences research needs high-performance technical environments with the ability to process massive amounts of data and support increasingly sophisticated simulations and analyses. Learn how IBM solutions such as IBM® Platform Computing™ high-performance cluster, grid and high-performance computing (HPC) cloud management software can help life sciences organizations transform and integrate their compute environments to develop products better, faster and at less expense.
Tags : ibm, life sciences, platform computing
     IBM
By: Bull     Published Date: Dec 04, 2014
Bull, an Atos company, is a leader in Big Data, HPC and cyber-security with a worldwide market presence. Bull has extensive experience in implementing and running petaflopsscale supercomputers. The exascale program is a new step forward in Bull’s strategy to deliver exascale supercomputers capable of addressing the new challenges of science, industry and society.
Tags : bull, exascale, big data, hpc, cyber security, supercomputers
     Bull
By: General Atomics     Published Date: Jan 13, 2015
The term “Big Data” has become virtually synonymous with “schema on read” (where data is applied to a plan or schema as it is ingested or pulled out of a stored location) unstructured data analysis and handling techniques like Hadoop. These “schema on read” techniques have been most famously exploited on relatively ephemeral human-readable data like retail trends, twitter sentiment, social network mining, log files, etc. But what if you have unstructured data that, on its own, is hugely valuable, enduring, and created at great expense? Data that may not immediately be human readable or indexable on search? Exactly the kind of data most commonly created and analyzed in science and HPC. Research institutions are awash with such data from large-scale experiments and extreme-scale computing that is used for high-consequence
Tags : general atomics, big data, metadata, nirvana
     General Atomics
By: MarkLogic     Published Date: Mar 29, 2018
It’s your golden opportunity: Rapidly integrate and harmonize data silos. Enhance drug discovery. Achieve faster time to insight. Get to market faster — all with less cost than you think. Explore how Life Sciences organizations can accelerate Real World Evidence (RWE) in a comprehensive and cost efficient manner. Download this white paper to learn about challenges, solutions and most importantly — how to equip your organization for success.
Tags : manufacturers, organizations, integration, optimization, data, quality
     MarkLogic
By: MarkLogic     Published Date: Mar 29, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
Tags : enterprise, metadata, management, organizations, technology, tools, mark logic
     MarkLogic
By: MarkLogic     Published Date: Mar 29, 2018
Real World Evidence (RWE) requires the correlation of complex, frequently changing, unstructured data. To the enterprise architect, that means extracting value from data that doesn't neatly fit solutions. In this white paper, we dive into the details of why relational databases are ill-suited to handle the massive volumes of disparate, varied, and changing data that is required to be successful with RWE. It is for this reason that leading life science organizations are going beyond relational to embrace new kinds of databases. And when they do, the results can be dramatic.
Tags : data, integration, volume, optimization, architect, enterprise
     MarkLogic
By: Amazon Web Services     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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     Amazon Web Services
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
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
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.
Tags : sas, healthcare, healthcare models, episode analytics, analytics, data management
     SAS
By: Intel     Published Date: Jun 07, 2017
Using the Integrated Analytics Hub, data analytics projects have already accounted for an estimated quarterly savings on marketing digital-media expenditures of approximately USD 170,000. Download this white paper to find out more.
Tags : intel, analytics, data, data analytics, data science
     Intel
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