data science

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Published By: Dassault Systčmes     Published Date: Nov 12, 2019
The Life Sciences industry has changed significantly over the past 10 years. With a view to developing new, more effective treatments, industry leaders are exploring new therapeutic areas and approaches like biologics and precision medicine. To address this shift, pharmaceutical and medical devices manufacturers look to connect systems, people and data - characterized by more predictive and adoptive facilities that leverage machine learning, 3D modeling, Industrial Internet of Things (IIOT), digital twin, and augmented reality. This eBook explains the main challenges of manufacturing and explores 5 manufacturing experiences that disrupt the future of manufacturing in the Life Sciences industry.
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iiot, medical device, life sciences, machine learning, 3d modeling, digital twin
    
Dassault Systčmes
Published By: Dassault Systčmes     Published Date: Nov 12, 2019
The Life Sciences industry has changed significantly over the past 10 years. With a view to developing new, more effective treatments, industry leaders are exploring new therapeutic areas and approaches like biologics and precision medicine. To address this shift, pharmaceutical and medical devices manufacturers look to connect systems, people and data - characterized by more predictive and adoptive facilities that leverage machine learning, 3D modeling, Industrial Internet of Things (IIOT), digital twin, and augmented reality. This eBook explains the main challenges of manufacturing and explores 5 manufacturing experiences that disrupt the future of manufacturing in the Life Sciences industry.
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Dassault Systčmes
Published By: SAS     Published Date: Nov 08, 2019
Organizations realize they need analytics to run, grow, and differentiate their products and services. More than just driving strategic decision making, they need to incorporate analytics into their high-volume operational and transactional decisions every minute of every day. This means that analytics is no longer just the responsibility of the data science team. Organizations have to continuously deliver analytics into operational systems in a systemic, high-quality and dependable way.
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SAS
Published By: MicroStrategy     Published Date: Nov 08, 2019
Gartner’s annual Predicts report is out for 2019—and it includes recommended solutions to issues like incorporating analytics into corporate strategy, measuring the value of data assets, the rapidly increasing volume of data, the lack of data literacy, and more. Some of the valuable findings include: Today, fewer than 50% of documented corporate strategies mention data and analytics as key components for delivering enterprise value, per Gartner’s “How Infosavvy Are You? Study." Organizations that fail to develop and enforce such codes of conduct are at a greater risk of liability and misuse of data science and AI. Few organizations have implemented continuous intelligence capabilities, spanning multiple applications and business functions, because they lack the relevant skills.
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MicroStrategy
Published By: MicroStrategy     Published Date: Nov 08, 2019
Gartner’s annual Predicts report is out for 2019—and it includes recommended solutions to issues like incorporating analytics into corporate strategy, measuring the value of data assets, the rapidly increasing volume of data, the lack of data literacy, and more. Some of the valuable findings include: Today, fewer than 50% of documented corporate strategies mention data and analytics as key components for delivering enterprise value, per Gartner’s “How Infosavvy Are You? Study." Organizations that fail to develop and enforce such codes of conduct are at a greater risk of liability and misuse of data science and AI Few organizations have implemented continuous intelligence capabilities, spanning multiple applications and business functions, because they lack the relevant skills. Read the report for a full list of enterprise challenges and tips on how to address them.
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MicroStrategy
Published By: MicroStrategy     Published Date: Nov 08, 2019
Gartner’s annual Predicts report is out for 2019—and it includes recommended solutions to issues like incorporating analytics into corporate strategy, measuring the value of data assets, the rapidly increasing volume of data, the lack of data literacy, and more. Some of the valuable findings include: Today, fewer than 50% of documented corporate strategies mention data and analytics as key components for delivering enterprise value, per Gartner’s “How Infosavvy Are You? Study." Organizations that fail to develop and enforce such codes of conduct are at a greater risk of liability and misuse of data science and AI. Few organizations have implemented continuous intelligence capabilities, spanning multiple applications and business functions, because they lack the relevant skills.
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MicroStrategy
Published By: TIBCO Software     Published Date: Nov 07, 2019
Is your risk infrastructure showing signs of strain in the face of FRTB, Basel III, and BCBS 239? Imagine a risk function where discrepancies among business, risk, and finance views are eliminated, setting the stage for advanced technologies, robotic process automation, and machine learning. Take a step beyond first generation data governance towards unified data and analytics across the enterprise. In this whitepaper, we explore how technology can help financial institutions not just automate compliance, but demonstrate organizational commitment to the change management process and adherence to the principles of regulations and law. Get insights into: How you can master regulatory change as part of transforming the risk function, elevating knowledge and data resources through governance, MDM, data science, and analytics An overview of the key market challenges for delivering a unified data management and governance model Real-world case studies from G-SIBs focused on data governanc
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TIBCO Software
Published By: Corrigo     Published Date: Nov 01, 2019
Think about all the ways your life today is different than it was ten years ago. Think about how you shop, how you get around, how you plan travel, and how you stay in touch. So many things that used to be a hassle are now almost effortless. If your life feels different, it’s because you’re living in a new era – what experts are calling the 4th Industrial Revolution. You’ve probably heard some of the more catch-phrased components – big data, artificial intelligence, deep analytics. Some of these still seem like science fiction, but they are very real, very active, and crucial parts of what we at Corrigo call the Intelligence Economy. In the Intelligence Economy, data is collected, crunched, and activated to solve problems and create greater value for customers, partners, and employees. It’s the information, insights, and automations that enhance experiences, predict needs, strengthen connections, and deliver the right info or action at the right time, in the right way. And the Intellig
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Corrigo
Published By: Zilliant     Published Date: Oct 30, 2019
System price drift and an overabundance of price deviations are causing preventable margin leakage for MRO/industrial distributors
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mro, industrial distribution, maintenance, repair, operations, overhaul, distributor, b2b
    
Zilliant
Published By: Zilliant     Published Date: Oct 30, 2019
System price drift and an overabundance of price deviations are causing preventable margin leakage for electrical products distributors
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electrical products, distribution, distributor, b2b, deviations, system prices, margin, price optimization
    
Zilliant
Published By: Zilliant     Published Date: Oct 30, 2019
System price drift and an overabundance of price deviations are causing preventable margin leakage for building products and construction distributors
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building products, distribution, distributor, construction, b2b, deviations, system prices, margin
    
Zilliant
Published By: Akamai Technologies     Published Date: Sep 10, 2019
Contemporary internet threats are sophisticated and adaptable, they continuously change their complexion to evade security defenses. Traditional rigid, deterministic, rule-based security research are becoming less effective. Security research approaches employing data science methods to implement anomalies-based analysis across very large volumes of anonymized data are now essential. This paper will: • Briefly cover security research challenges in today’s threat landscape • Explain why DNS resolution data is a rich resource for security research • Describe how Akamai teams use DNS data and data science to create better threat intelligence • Discuss improvements in threat coverage, accuracy, and responsiveness to today’s agile threats
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Akamai Technologies
Published By: Group M_IBM Q3'19     Published Date: Sep 04, 2019
In the last few years we have seen a rapid evolution of data. The need to embrace the growing volume, velocity and variety of data from new technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) has been accelerated. The ability to explore, store, and manage your data and therefore drive new levels of analytics and decision-making can make the difference between being an industry leader and being left behind by the competition. The solution you choose must be able to: • Harness exponential data growth as well as semistructured and unstructured data • Aggregate disparate data across your organization, whether on-premises or in the cloud • Support the analytics needs of your data scientists, line of business owners and developers • Minimize difficulties in developing and deploying even the most advanced analytics workloads • Provide the flexibility and elasticity of a cloud option but be housed in your data center for optimal security and compliance
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Group M_IBM Q3'19
Published By: SAS     Published Date: Aug 19, 2019
With the combination of electronic health records, rich repositories of claims data, medical device outputs, laboratory and prescription systems, real-world data and the data mined from other information technology systems, the health and life sciences ecosystem can now gain new perspective. Download this complimentary paper to learn more about how health care data has the power to transform the sector, helping to address the industry’s biggest challenges surrounding costs and quality of patient care. By adopting solutions that allow them to both produce and consume data analytics insights in a way that better guides clinical and business strategies, innovative health care organizations can learn not only to survive but also thrive in the decades to come.
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SAS
Published By: SAS     Published Date: Aug 06, 2019
With the combination of electronic health records, rich repositories of claims data, medical device outputs, laboratory and prescription systems, real-world data and the data mined from other information technology systems, the health and life sciences ecosystem can now gain new perspective. Download this complimentary paper to learn more about how health care data has the power to transform the sector, helping to address the industry’s biggest challenges surrounding costs and quality of patient care. By adopting solutions that allow them to both produce and consume data analytics insights in a way that better guides clinical and business strategies, innovative health care organizations can learn not only to survive but also thrive in the decades to come.
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SAS
Published By: TIBCO Software     Published Date: Aug 02, 2019
Fraud is one of the biggest overheads for most financial firms. Detecting crime is hard as fraud constantly evolves and the tools have to be able to evolve with it. Also one of the key areas of focus for most firms is to address the cost of handling the false positives that all automated systems generate. Watch this short demonstration to learn how TIBCO’s advanced analytics and data science solutions can help you overcome these challenges.
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TIBCO Software
Published By: RMS     Published Date: Jul 25, 2019
U.S. Flood is a high-gradient, intricate peril incorporating various sources, and causing a variety of effects. It requires sophisticated models, data science, and analytics technology to properly understand and assess each risk.
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RMS
Published By: AWS     Published Date: Jul 24, 2019
Many business leaders know that Artificial Intelligence (AI) and Machine Learning (ML) are critical to their future but don’t know where to start. Those who do have an AI/ML strategy struggle to find qualified data scientists; and once they find them, even advanced data scientists need a lot of time—even months—to build and deploy ML models. These challenges put significant limits on the range and number of problems a business can solve. In this webinar, learn how H2O Driverless AI on Amazon Web Services (AWS) automates the best practices of leading data scientists to create advanced machine learning models automatically. With these production-ready models, relative newcomers to AI/ML can generate reliable results and scale-up AI programs that anticipate and capitalize on trends, optimize supply chains, understand customer demand, match consumers with goods and services, and much more. Download our webinar to learn Implement ML successfully with minimal data science expertise. Build
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AWS
Published By: AWS     Published Date: Jul 24, 2019
Trupanion, a Seattle-based medical insurance provider for cats and dogs, needed to find data insights quickly. With only 1% of pet owners insured, the process of evaluating a claim to approve or deny payment was manual and time-consuming. Building accurate predictive models for decision-making required manpower, time, and technology that the small company simply did not have. DataRobot Cloud, built on AWS, helped Trupanion create an automated method for building data models using machine learning that reduced the time required to process claims from minutes to seconds. Join our webinar to hear how Trupanion transformed itself into an AI-driven organization, with robust data analysis and data science project prototyping that empowered the company to make better decisions and optimize business processes in less time and at a reduced cost. Join our webinar to learn: Why you don’t need to be an expert in data science to create accurate predictive models. How you can build and deploy pr
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AWS
Published By: SAS     Published Date: Jul 22, 2019
Text is the largest human-generated data source. It grows every day as we post on social media, interact with chatbots and digital assistants, send emails, conduct business online, generate reports and essentially document our daily thoughts and activities using computers and mobile devices. Increasingly, organizations want to know how all of that data can be used to drive improvements. For many, unstructured text represents a massive untapped data source with great potential for producing valuable insights that could result in significant business transformations or spur incredible social innovation. This paper looks at how organizations in banking, health care and life sciences, manufacturing and government are using SAS text analytics to drive better customer experiences, reduce fraud and improve society.
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SAS
Published By: TIBCO Software     Published Date: Jul 22, 2019
Faster answers from unstructured data, improved accuracy of liability estimates, expanded service offerings
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TIBCO Software
Published By: TIBCO Software     Published Date: Jul 22, 2019
Global producer of polycrystalline silicon for semiconductors, Hemlock Semiconductor needed to accelerate process optimization and eliminate cost. With TIBCO® Connected Intelligence, Hemlock achieved centralized, self-service, governed analysis; revenue gains; cost savings; and more. Fueled by double-digit growth in the markets it serves, Hemlock Semiconductor is adapting to the increasing commoditization within the polysilicon industry and better positioning itself to compete. A key factor in this plan is to equip process-knowledgeable personnel with the skills and tools to accelerate delivery of process optimizations and associated cost elimination. Hemlock turned to a TIBCO® Connected Intelligence solution to address the challenges. By implementing TIBCO Spotfire® and TIBCO® Streaming analytics, TIBCO® Data Science, and TIBCO® Data Virtualization, the company created more self-service analytics. Adding TIBCO BusinessWorks™ integration let the company realize the vision of connect
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TIBCO Software
Published By: Expert System     Published Date: Jul 18, 2019
Although computers are better when it comes to processing and making calculations, they haven’t been able to accomplish some of the most basic human tasks, until now. Thanks to cognitive computing, machines are bringing human-like intelligence to many business applications, including big data. So, what is cognitive computing? According to Forbes, “cognitive computing comes from a mashup of cognitive science—the study of the human brain and how it functions—and computer science.” This definition is a good place to start. However, to really understand what cognitive computing is, we have to go a little deeper.
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Expert System
Published By: SAS     Published Date: Jun 27, 2019
Hear about real-world use cases that demonstrate how data, advanced analytics, AI and IoMT are helping life sciences companies increase efficiency.
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SAS
Published By: Domino Data Lab     Published Date: May 23, 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
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