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Published By: SAS     Published Date: Aug 28, 2018
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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SAS
Published By: Intel     Published Date: Jun 07, 2017
Intel's Bob Rogers, chief data scientist for big data solutions, sat down with Dan Magestro, research director at the international Institute of Analytics (IIA), to discuss the power of asking questions when assessing an organisation's analytics maturity. Read on to find out more.
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intel, analytics, data, data analytics, data science
    
Intel
Published By: Teradata     Published Date: Oct 15, 2012
Does your organization struggle to get new business insights from all data types with rapid exploration?
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data scientists, analyst, statistician, quants, quantitative analyst, scientist, data science, business technology
    
Teradata
Published By: Virgin Pulse     Published Date: Jun 02, 2017
This report includes analysis from behavior and data scientists to help you understand why your employees may not be working at optimal levels and discover ways to address this growing problem.
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presenteeism, employee presenteeism, workforce presenteeism, job presenteeism, presenteeism in the workplace, worker productivity, workforce productivity
    
Virgin Pulse
Published By: TIBCO Software APAC     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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TIBCO Software APAC
Published By: HP Inc.     Published Date: Jun 12, 2018
HP scientists had a bright idea. If they could modify materials to absorb infrared light – the light we can’t see – they could successfully fuse color to that material. It’s a story of how HP “cracked the code” in 3D color printing by drawing upon deep experience in the science of ink. Read this innovation story to why HP Jet Fusion 300/500 3D printers make it possible to produce engineering-grade functional parts faster and more affordably. Download HP’s innovation story
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HP Inc.
Published By: SAS     Published Date: Apr 20, 2015
To create real business value with data scientists, top management must learn how to manage them effectively.
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SAS
Published By: EMC Corporation     Published Date: Jul 07, 2013
3TIER helps organizations understand and manage the risks associated with renewable energy projects. A pioneer in wind and solar generation risks analysis, 3TIER uses science and technology to frame the risk of weather-driven variability, anywhere on Earth. 3TIER's unique expertise is in combining the latest weather data with historical weather patterns, and using the expertise of 3TIER's meteorologists, engineers and data scientists to create a detailed independent assessment of the future renewable energy potential of any location.
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renewable energy, customer profile, emc, risk management, best practices, storage, technology, security, it management, data management, business technology
    
EMC Corporation
Published By: HP Inc.     Published Date: Apr 11, 2018
HP scientists had a bright idea. If they could modify materials to absorb infrared light – the light we can’t see – they could successfully fuse color to that material. It’s a story of how HP “cracked the code” in 3D color printing by drawing upon deep experience in the science of ink. Read this innovation story to why HP Jet Fusion 300/500 3D printers make it possible to produce engineering-grade functional parts faster and more affordably. Download HP’s innovation story
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HP Inc.
Published By: Veritas     Published Date: Oct 03, 2016
This benchmark report, the Data Genomics Index, encompasses a community of like-minded data scientists, industry experts, and thought leaders together with the purpose of better understanding the true nature of the unstructured data that we are creating, storing, and managing on a daily basis - a report on real storage environments’ composition.
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Veritas
Published By: IBM     Published Date: Jul 14, 2016
This video describes how data scientists, analysts and business users can save precious time by using a combination of SPSS and Spark to uncover and act on insights in big data.
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ibm, data, analytics, predictive business, ibm spss, apache spark, coding, data science, software development, enterprise applications, data management, business technology, data center
    
IBM
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. Download this white paper to find out how you can improve your data preparation for business analytics.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.
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Waterline Data & Research Partners
Published By: IBM     Published Date: Oct 21, 2016
Between the Internet of Things, customer experience and loyalty programs, social network monitoring, connected enterprise systems and other information sources, today's organizations have access to more data than they ever had before-and frankly, more than they may know what to do with. The challenge is to not just understand that data, but actualize it and use it to recognize real business value. This ebook will walk you through a sample scenario with Albert, a data scientist who wants to put text analytics to work by using the Word2vec algorithm and other data science tools.
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ibm, analytics, aps, aps data, open data science, data science, word2vec, enterprise applications, business technology, data center
    
IBM
Published By: Veritas     Published Date: May 12, 2016
The Data Genomics Index is a first-of-its-kind benchmark analysis of data stored within a typical enterprise environment. This report reveals insights into data growth, data age, and data type thereby providing organizations with the comparison standard for beginning to take action on their data. In addition to the Index, Veritas has founded the Data Genomics Project. This community of likeminded data scientists, industry experts and thought leaders will come together to surface the true nature of enterprise environments, build the data-genome that matters for information management, and share the discussion with a world struggling to solve tremendous data growth challenges.
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Veritas
Published By: IBM     Published Date: Jan 18, 2017
It's all well enough for an organization to collect every slice of data it can reach, but having more data doesn't mean you'll automatically get better insights. First, you have to figure out what you want from your data you have to find its value.
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ibm, aps data, data science, open data science, analytics, enterprise applications
    
IBM
Published By: IBM     Published Date: Jan 18, 2017
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. But data scientists can only go so far without support.
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ibm, analytics, aps data, open data science, data science, data engineers, enterprise applications
    
IBM
Published By: Datastax     Published Date: May 14, 2018
"What’s In The Report? The Forrester Graph Database Vendor Landscape discusses the expanding graph uses cases, new and emerging graph solutions, the two approaches to graph, how graph databases are able to provide penetrating insights using deep data relationships, and the top 10 graph vendors in the market today Download The Report If You: -Want to know how graph databases work to provide quick, deep, actionable insights that help with everything from fraud to personalization to go-to-market acceleration, without having to write code or spend operating budget on data scientists. -Learn the new graph uses cases, including 360-degree views, fraud detection, recommendation engines, and social networking. -Learn about the top 10 graph databases and why DSE Graph continues to gain momentum with customers who like its ability to scale out in multi-data-center, multi-cloud, and hybrid environments, as well as visual operations, search, and advanced security."
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Datastax
Published By: Alteryx, Inc.     Published Date: Apr 21, 2017
The traditional multiple-step, multi-tool legacy approach is a slow, time-consuming, and in most cases, a costly process that prevents organizations from making faster decisions with confidence. Data analysts today need an agile solution that empowers them to take charge of the entire analytics process. Download The Definitive Guide to Self-Service Data Analytics to: Understand why traditional analytic tools designed for data scientists are not ideal for data analysts like you Learn how self-service data analytics delivers the ease of use, speed, flexibility, and scalability you require See how Alteryx stacks up against traditional data prep and analytics tools
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Alteryx, Inc.
Published By: Alteryx, Inc.     Published Date: Apr 21, 2017
Data Analytics has become critical for many business decision makers. However, many of these managers and data analysts still rely on spreadsheets and other legacy-era tools that fall far short of current needs. As a result, they also rely heavily on a virtual army of data specialists and scientists, working under the auspices of a centralized analytics group, to prepare, blend, analyze, and even report on the critical data they need for decision making. Download this new paper to get the details behind self-service data analytics, and how it lets business analysts: Take charge of the entire analytical process, instead of relying on other departments Overcome limitations of legacy tools to save time and prevent errors Make more comprehensive and insightful business decisions at speed
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Alteryx, Inc.
Published By: IBM     Published Date: Jul 02, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy. IDC believes that this emerging environment is to date still highly undefined, even as businesses must make critical decisions. Should businesses develop in-house or use VARs, systems integrators, or consultants? Should they deploy on-premise, in the cloud, or in some hybrid form? Can they use existing infrastructure, or do AI applications and deep learning require new servers with new capabilities? We believe that many of these questions can be
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IBM
Published By: Amazon Web Services     Published Date: Feb 01, 2018
At Amazon, we’ve been investing deeply in AI for more than 20 years. Machine learning (ML) algorithms drive many of our internal systems, and have formed the core of our customers' experience —from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, and our new retail experience, Amazon Go. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every executive, developer, and data scientist.
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machine learning, algorithms, interal systems, amazon
    
Amazon Web Services
Published By: Datarobot     Published Date: May 14, 2018
The DataRobot automated machine learning platform captures the knowledge, experience, and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods
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Datarobot
Published By: SAS     Published Date: May 24, 2018
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. You’ll then be ready to experiment with these methods in SAS Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
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SAS
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