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By: GE Power     Published Date: Feb 02, 2017
From balancing asset life and performance to managing variations in market conditions and fuel costs, power leaders everywhere are using data and analytics as a strategic asset to drive efficiencies and increase profitability for their business.
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     GE Power
By: IBM     Published Date: Mar 30, 2017
In today’s competitive on-line world, the speed of change in customer behaviour is increasing. In addition, in industries such as retail banking, car insurance and to some extent retail, the Internet has become the dominant way in which customers interact with an organisation. Yet in many data warehouses today, being able to analyse customer on-line behaviour is often not possible because the clickstream web log data needed to do this is missing. It is a key point because customer access to the web has made loyalty cheap.
Tags : cyber attacks, data protection, it security, security solutions, system protector, web security, analytics
     IBM
By: IBM     Published Date: Apr 18, 2017
Companies in all industries can benefit from a master data management program, advises MDM expert Anne Marie Smith. Learn how to enhance yours.
Tags : data analytics, product refinement, business exploration, advanced prototyping, analytics, data preparation, customer support, sales relations
     IBM
By: IBM     Published Date: Oct 16, 2017
This white paper examines how some of the ways organizations use big data make their infrastructures vulnerable to attack. It presents recommended best practices organizations can adopt to help make their infrastructures and operations more secure. And it discusses how adding advanced security software solutions from IBM to their big-data environment can fill gaps that big-data platforms by themselves do not address. It describes how IBM® Security Guardium®, an end-to- end solution for regulatory compliance and comprehensive data security, supports entitlement reporting; user-access and activity monitoring; advanced risk analytics and real-time threat detection analytics; alerting, blocking, encryption and other data protection capabilities, as well as automated compliance workflows and reporting capabilities, to stop threats.
Tags : security, big data, ibm, data protection
     IBM
By: CrowdStrike     Published Date: Feb 01, 2017
One of the biggest challenges to effectively stopping breaches lies in sifting through vast amounts of data to find the subtle clues that indicate an attack is imminent or underway. As modern computer systems generate billions of events daily, the amount of data to analyze can reach petabytes. Compounding the problem, the data is often unstructured, discrete and disconnected. As a result, organizations struggle to determine how individual events may be connected to signal an impending attack. Download the white paper to learn: • How to detect known and unknown threats by applying high-volume graph-based technology, similar to the ones developed by Facebook and Google • How CrowdStrike solved this challenge by building its own proprietary graph data model • How CrowdStrike Threat Graph™ collects and analyzes massive volumes of security-related data to stop breaches
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     CrowdStrike
By: Dassault Systèmes     Published Date: Jun 19, 2018
This white paper outlines a framework that emphasizes digitization and business transformation and the new opportunities pull processes bring. The mechanism of “Pull” processes—those triggered by an actual event instead of a forecast—is nothing new. It is at the heart of many successful manufacturing strategies. Recent technological advances in digitization, including the harnessing of Big Data analytics, the use of the cloud, Business Process Management (BPM), social media, IIoT, and mobility, have extended the power of Pull beyond Lean manufacturing. In the wake of the current technological innovation wave, it is not uncommon for manufacturers to not know what next step to take. In light of these new developments, this white paper will focus on the mechanism of business transformation enabled by these technologies, which can be attributed to two major forces: the power of Pull and digitization. Nine practical applications are detailed, showing how innovative manufacturers can better
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     Dassault Systèmes
By: Kronos     Published Date: Sep 20, 2018
If you’ve tried managing your retail operation’s labor performance without reliable, real-time data, you know the costly shortcomings of this approach. Tap into clear, easy-to-access workforce data — a single view of all elements of your workforce that empowers you to make effective decisions and protect your bottom line. Workforce Analytics for Retail transforms labor and sales data into meaningful business intelligence. Learn how managers can make data-driven decisions that optimize workforce utilization, control labor costs, and drive continuous improvement.
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     Kronos
By: Pure Storage     Published Date: Jul 03, 2019
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
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     Pure Storage
By: Pure Storage     Published Date: Jul 03, 2019
Apache® Spark™ has become a vital technology for development teams looking to leverage an ultrafast in-memory data engine for big data analytics. Spark is a flexible open-source platform, letting developers write applications in Java, Scala, Python or R. With Spark, development teams can accelerate analytics applications by orders of magnitude.
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     Pure Storage
By: Pure Storage     Published Date: Jul 03, 2019
Deep learning opens up new worlds of possibility in artifi cial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifi cally to work with high-powered analytics.
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     Pure Storage
By: Pure Storage     Published Date: Jul 03, 2019
Deep learning opens up new worlds of possibility in artificial intelligence within the Financial Services industry, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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     Pure Storage
By: Q2 eBanking     Published Date: Jul 30, 2017
Many financial institutions see their customer data as one of their most valuable assets. Unlocking insights from that data helps FIs understand, anticipate and offer account holders the products and services they truly need. A major trend to unlocking customer insights is using machine learning to surface the behavioral intelligence buried in the large amount of account holder transactional data captured each and every day. In this paper, learn how a group of talented, enthusiastic analysts with an open approach to data can yield some very interesting and extremely valuable and actionable results. This approach, championed by Q2 Executive Vice President and CTO Adam Anderson, has led to a new platform, Q2 SMART, which provides powerful behavioral analytics for financial institutions, enabling growth while providing account holders with real value.
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     Q2 eBanking
By: Google     Published Date: Dec 03, 2018
The Internet of Things is growing fast: By 2025, IoT devices will transmit an estimated 90 zettabytes of data to their intended targets, according to IDC. Armed with information, businesses can revolutionise everything from fraud detection to customer service. But first, they need an architecture that supports real-time analytics so they can gain actionable insights from their IoT data. Read the complete report sponsored by Google Cloud, and learn how to mitigate key IoT-related challenges.
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     Google
By: Insight Software     Published Date: Mar 14, 2018
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Tags : kpi, business, data, analytics, metrics, hubble
     Insight Software
By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse.
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     Group M_IBM Q1'18
By: Group M_IBM Q1'18     Published Date: Jan 08, 2018
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements. To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
Tags : cloud, database, hybrid cloud, database platform
     Group M_IBM Q1'18
By: IBM     Published Date: Jul 05, 2018
Scalable data platforms such as Apache Hadoop offer unparalleled cost benefits and analytical opportunities. IBM helps fully leverage the scale and promise of Hadoop, enabling better results for critical projects and key analytics initiatives. The end-to- end information capabilities of IBM® Information Server let you better understand data and cleanse, monitor, transform and deliver it. IBM also helps bridge the gap between business and IT with improved collaboration. By using Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of- impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time. Since its inception, Information Server has been a massively parallel processing (MPP) platform able to support everything from small to very large data volumes to meet your requirements, regardless of complexity. Information Server can uniquely support th
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     IBM
By: IBM     Published Date: Jul 09, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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     IBM
By: Group M_IBM Q418     Published Date: Sep 10, 2018
There are three things that senior executives in the financial services industry want from their investments in computing systems. They are the same three things these institutions require for their very survival. First is unwavering security. The integrity of customer accounts and records is paramount to maintain trust across the financial ecosystem. Cybercrime is anathema to the core function of banking and cannot be tolerated. Next is captivating, personalized experiences based on real-time data analytics leading to instantaneous customer fulfillment. And finally, there is the essential delivery of these secure experiences while providing a cost and efficiency advantage over competing solutions
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     Group M_IBM Q418
By: Tricentis     Published Date: Jan 08, 2018
Data. It seems to be everywhere today and yet we can never get enough of it. But as it turns out, a lack of data isn’t our problem -- our problem is the difficulty piecing together, understanding and finding the story in all the data that’s in front of us. In software testing in particular, the need for consolidated, meaningful test metrics has never been higher. As both the pace of development and the cost of delivering poor quality software increase, we need these metrics to help us test smarter, better and faster. Fortunately, business intelligence now exists to make this goal a reality. The analytics these tools provide can help drive efficient and effective testing by providing teams with insight on everything from testing quality and coverage to velocity and more. And this knowledge can position the QA team as trusted experts to advise the entire software development team on steps that can ensure a better quality end result.
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     Tricentis
By: Hitachi Vantara     Published Date: Aug 02, 2018
In this inforgraphic, we are going to show you the possibilities with modernised, optimized data infrastructure. In doing so, we aim to bring you ideas about how you can bring your own data-driven business vision to life, focusing on the outcomes that matter most to you. In essence, we will be discussing five main industries and the possibilities for modernized data infrastructure. We will be displaying how Banking and Financial Services, Telecommunications, manufacturing, healthcare and retail can use data, analytics and automation to optimize their business, therefore bringing about a competitive edge which previous appeared unimaginable. Using data correctly, most importantly building proper data infrastructures, can be the driving force behind obtaining a competitive edge. Data is your story. And it’s our story, too.
Tags : digital transformation, data infrastructure, data optimisation
     Hitachi Vantara
By: Hitachi Vantara     Published Date: Aug 13, 2018
In this inforgraphic, we are going to show you the possibilities with modernised, optimized data infrastructure. In doing so, we aim to bring you ideas about how you can bring your own data-driven business vision to life, focusing on the outcomes that matter most to you. In essence, we will be discussing five main industries and the possibilities for modernized data infrastructure. We will be displaying how Banking and Financial Services, Telecommunications, manufacturing, healthcare and retail can use data, analytics and automation to optimize their business, therefore bringing about a competitive edge which previous appeared unimaginable. Using data correctly, most importantly building proper data infrastructures, can be the driving force behind obtaining a competitive edge. Data is your story. And it’s our story, too.
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     Hitachi Vantara
By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes.
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     TIBCO Software
By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes. Download now to read on.
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     TIBCO Software
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