big data

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Published 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
Published By: IBM     Published Date: Jul 02, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does. From an IT perspective, there is a fairly straightforward sequence of applications that businesses can adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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IBM
Published By: Butler Technologies     Published Date: Jul 02, 2018
The Tenth Annual State of the Network Global Study focuses a lens on the network team’s role in security investigations. Results indicate that 88 percent of network teams are now spending time on security issues. In fact, out of 1,035 respondents, nearly 3 out of 4 spend up to 10 hours per week working exclusively on these types of problems - in addition to managing network upgrades, SDN, cloud, and big data initiatives. When it comes to technology adoption, both cloud and 100 GbE deployment continue to grow aggressively. VoIP adoption is closing in on 60 percent and software-defined networking (SDN) is projected to cross the halfway mark, indicating compounding network complexity amidst the ongoing struggle to ID security threats. With growth comes change and some trends identified in this year’s survey include a rise in email and browser-based malware attacks (63 percent) and an increase in sophistication (52 percent). Nearly 1 in 3 also report a surge in DDoS attacks, signaling a ne
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Butler Technologies
Published By: Infosys     Published Date: Jun 21, 2018
xxx
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Infosys
Published By: Amazon Web Services     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated q
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Amazon Web Services
Published 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
Published By: Adverity     Published Date: Jun 15, 2018
A Beginner's Guide to Marketing Data Analytics Marketing Data is big & highly fragmented Big data is messy. It’s scattered across platforms, it’s diverse, and in its raw form, it’s practically unusable. We know, it’s a painful truth. The fact of the matter is that having a lot of data doesn’t necessarily mean that you have the answers to your most pressing questions. Looking for the most relevant bits in your pile of big data is like looking for a needle in a haystack. But don't you worry - we are here to help. This handy e-book will give you a short overview what quality matters, why data is so important and what you need to pay attention to. Best thing is: getting this ebook is super easy. Just fill out the form to the right and voilá - your download is ready. Enjoy this read!
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marketing business intelligence, saas marketing optimization, measuring marketing performance, roi analytics, automated report generator, performance based marketing, online marketing data, roi metrics, marketing reports, roi reporting, reporting generator, automated reporting tool, marketing dashboard, marketing data, marketing intelligence, report software, marketing reporting, marketing metrics, performance marketing, marketing performance
    
Adverity
Published By: Adverity     Published Date: Jun 15, 2018
In this whitepaper, we take a closer look at some of the biggest challenges facing e-commerce businesses, namely understanding your data in general and, more precisely, your customer acquisition costs (CAC). It's full of inspiration, useful tips and actionable insights for you to step up your marketing game. In order to reap the fruits from your data seeds, you have to make sure you tackle these five challenges full-frontal: Knowing what data to capture Understanding customer behaviour Finding your technology solution Ensuring analysis is impartial Optimising website content - especially for your offline users
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marketing business intelligence, saas marketing optimization, measuring marketing performance, roi analytics, automated report generator, performance based marketing, online marketing data, roi metrics, marketing reports, roi reporting, reporting generator, automated reporting tool, marketing dashboard, marketing data, marketing intelligence, report software, marketing reporting, marketing metrics, performance marketing, marketing performance
    
Adverity
Published By: IBM     Published Date: Jun 13, 2018
The cloud offers elasticity and flexibility to meet a range of demands
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IBM
Published By: Infosys     Published Date: Jun 12, 2018
In the wake of data hacks and privacy concerns, enterprises are working extra hard to make sure they secure customer data from external threats. But what about securing data internally? Organizations unknowingly leave a big security hole in their own systems when they fail to have structured internal processes to handle access requests for employees, which could have disastrous implications for data security. A leading US bank sought to move its internal applications to a secure system for a standard and consistent access rights experience. See how Infosys helped and the five key takeaways from the project.
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internal, applications, data, hacks, privacy, enterprises
    
Infosys
Published By: SAS     Published Date: Jun 06, 2018
A multitude of “things” generate floods of big data – cars, wearables, machines and appliances. Wouldn’t you like to sift through that noise and become an organization that relies on data to make fact-based decisions? Learn about the three foundations of becoming data-driven – data management, analytics and visualization – and how they can increase profitability, boost performance, raise market share and improve operations. Read about hurdles to becoming a data-driven organization and learn best practices from others. Then get a glimpse of what the future holds with the Internet of Things (IoT), edge analytics, artificial intelligence (AI) and other technology innovations.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
This A-to-Z guide includes 101 common terms related to the Internet of Things. It’s not an exhaustive list, given that the IoT and big data are evolving so quickly, but rather a quick go-to resource for the technically savvy data professional who wants to get a handle on this vast IoT and big data ecosystem. This guide for the “non-geek” does include some technical terms, but they are explained sans technical “geek speak.” We also provide context to how a term may be connected to a larger discussion by grouping terms by category.
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SAS
Published By: ADP     Published Date: Jun 01, 2018
Too remedial. Too retrospective. Too static. Too irrelevant to the team leader. Too time-consuming. Such are the complaints heard repeatedly about Performance Management and Engagement systems. And these don’t even touch on the biggest problem of all: current systems produce a tidal wave of unreliable data. The ratings they yield are based on the notion that, with enough time and training, managers (and peers) can become reliable raters of other people’s skills and competencies. Sadly, that’s not true. A large body of research reveals: 61% of your manager’s rating of you is a reflection of the manager, not of you. Your organization, apparently unaware of this, then persists on paying you, training you, and promoting you—and everyone else in the organization—based on these flawed ratings.
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ADP
Published By: TIBCO Software APAC     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 APAC
Published By: TIBCO Software APAC     Published Date: May 31, 2018
With data the new competitive battleground, businesses that take advantage of their data will be the leaders; those that do not will fall behind. But gaining an advantage is a more difficult technical challenge than ever because your business requirements are ever-changing, your analytic workloads are exploding, and your data is now widely-distributed across on-premises, big data, the Internet of Things, and the Cloud. TIBCO® Data Virtualization is data virtualization software that lets you integrate data at big data scale with breakthrough speed and cost effectiveness. With TIBCO Data Virtualization, you can build and manage virtualized views / data services that access, transform, and deliver the data your business requires to accelerate revenue, reduce costs, lessen risk, improve compliance, and more.
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TIBCO Software APAC
Published By: TIBCO Software APAC     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 APAC
Published By: TIBCO Software APAC     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 APAC
Published By: Infosys     Published Date: May 30, 2018
In the wake of data hacks and privacy concerns, enterprises are working extra hard to make sure they secure customer data from external threats. But what about securing data internally? Organizations unknowingly leave a big security hole in their own systems when they fail to have structured internal processes to handle access requests for employees, which could have disastrous implications for data security. A leading US bank sought to move its internal applications to a secure system for a standard and consistent access rights experience. See how Infosys helped and the five key takeaways from the project.
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internal, applications, data, hacks, privacy, enterprises
    
Infosys
Published By: IBM     Published Date: May 22, 2018
An overview of testing in talent acquisition and four specific guidelines for assessment practitioners evaluating new selection methods.
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human resources, talent management
    
IBM
Published By: Infosys     Published Date: May 21, 2018
In HR, working purely on instinct is dangerous. HR Professionals are highly skilled, and their experienced opinion is extremely valuable when it comes to selecting candidates, assessing performance, and all the other important aspects of this function. But can you rely on their instinct alone? This was the big question that our client, a large CPG company was facing. When we realized this was the problem, the solution was obvious. Not necessarily easy, but obvious. There was plenty of data, but it wasn't being used to improve HR decision making. We designed an analytics solution that would improve HR Decision making. We designed an analytics solution that would improve the efficiency of data gathering, to make the HR function more effective. We then proposed an additional layer, which would use artificial intelligence (AI) to improve HR Decision-making further.
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company, employee
    
Infosys
Published By: Hewlett Packard Enterprise     Published Date: May 11, 2018
If your business is like most, you are grappling with data storage. In an annual Frost & Sullivan survey of IT decision-makers, storage growth has been listed among top data center challenges for the past five years.2 With businesses collecting, replicating, and storing exponentially more data than ever before, simply acquiring sufficient storage capacity is a problem. Even more challenging is that businesses expect more from their stored data. Data is now recognized as a precious corporate asset and competitive differentiator: spawning new business models, new revenue streams, greater intelligence, streamlined operations, and lower costs. Booming market trends such as Internet of Things and Big Data analytics are generating new opportunities faster than IT organizations can prepare for them.
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Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: May 11, 2018
Wie die meisten Unternehmen werden auch Sie vermutlich mit der Datenspeicherung zu kämpfen haben. In einer jährlichen Umfrage von Frost & Sullivan unter IT-Entscheidungsträgern wurde das Speicherwachstum als eine der größten Herausforderungen der letzten fünf Jahre für das Rechenzentrum genannt.2Da Unternehmen exponentiell mehr Daten als je zuvor sammeln, replizieren und speichern, ist schon der einfache Erwerb ausreichender Speicherkapazitäten ein Problem. Hinzu kommt nicht zuletzt, dass Unternehmen von ihren gespeicherten Daten mehr erwarten. Daten werden mittlerweile als kostbares Firmenkapital und Möglichkeit gesehen, sich von den Wettbewerbern zu differenzieren: Sie sollen neue Geschäftsmodelle, neue Umsatzchancen, bessere Informationen, optimierte Betriebsabläufe und niedrigere Kosten bringen. Angesagte Trends wie das Internet der Dinge und Big Data-Analysen generieren so schnell neue Marktchancen, dass IT-Organisationen kaum noch Schritt halten können.
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Hewlett Packard Enterprise
Published By: CrowdStrike     Published Date: May 10, 2018
One of the biggest challenges to effectively stopping breaches lies in sifting through vast amounts of data to find the proverbial “needle in the haystack” – 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. In this context, detecting attacks is often difficult, and sometimes impossible. This white paper describes how CrowdStrike solved this challenge by building its own graph data model – the CrowdStrike Threat Graph? – to collect and analyze extremely large volumes of security-related data, and ultimately, to stop breaches. This revolutionary approach applies massive graph-based technologies, similar to the ones developed by Facebook and Google, to detect k
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CrowdStrike
Published By: Google Cloud     Published Date: May 09, 2018
To the Cloud and Beyond: Big Data in the Age of Machine Learning
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Google Cloud
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