data projects

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Published By: BMC Software     Published Date: May 28, 2014
"In the paper, “Integrate Big Data into Your Business Processes and Enterprise Systems” you’ll learn how to drive maximum value with an enterprise approach to Big Data. Topics discussed include: • How to ensure that your Big Data projects will drive clearly defined business value • The operational challenges each Big Data initiative must address • The importance of using an enterprise approach for Hadoop batch processing
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it management
    
BMC Software
Published By: Oracle ODA     Published Date: Aug 15, 2016
Businesses understand more than ever that they depend on data for insight and competitive advantage. And when it comes to data, they have always wanted easy access and fast performance. But how is the situation different now? Today, organizations want those elements and more. They want IT to strip away the limitations of time with faster deployment of new databases and applications. They want IT to reduce the limitations of distance by giving remote and branch offices better and more reliable access. And in a global world where business never stops, they want IT to ensure data availability around the clock. If IT can deliver databases and applications faster, on a more automated and consistent basis, to more locations without having to commit onsite resources, IT will be free to focus on more strategic projects.
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Oracle ODA
Published By: Veritas     Published Date: Jan 04, 2019
The digital business continues to evolve. Investments in data analytics projects lead the way while traditional, proprietary infrastructures are being disrupted by cloud, open source and hyperconverged paradigms. These changes are forcing IT leaders to contend with greater workload diversity in the midst of tightening budgets. And while the workload [or] IT landscape is changing, the need for reliable data protection remains as crucial as ever to protect against, data corruption, human error, and malicious threats such as ransomware. Learn how Veritas can help you navigate through these obstacles. Join us to hear experts from ESG and Veritas discuss how the right data protection solution today can prepare you for tomorrow's business demands. You will learn: The key trends that are driving change in the digital business The most common causes of data loss in tomorrow’s multi-cloud data centers How to protect an increasingly diverse environment with minimal operational overhead
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Veritas
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: IBM     Published Date: May 17, 2016
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
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ibm, big data, inline analytics, business analytics, roi, business intelligence
    
IBM
Published By: IBM     Published Date: Jul 26, 2017
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base. High-priority big data and analytics projects often target customer-centric outcomes such as improving customer loyalty or improving up-selling. In fact, an IBM Institute for Business Value study found that nearly half of all organizations with active big data pilots or implementations identified customer-centric outcomes as a top objective (see Figure 1).1 However, big data and analytics can also help companies understand how changes to products or services will impact customers, as well as address aspects of security and intelligence, risk and financial management, and operational optimization.
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customer analytics, data matching, big data, competitive advantage, customer loyalty
    
IBM
Published By: IBM     Published Date: Jul 26, 2017
Business leaders are eager to harness the power of big data. However, as the opportunity increases, ensuring that source information is trustworthy and protected becomes exponentially more difficult. If not addressed directly, end users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organizations can boost confidence in big data. A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamle
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mdm, big data, automation, organization
    
IBM
Published By: Schneider Electric     Published Date: May 07, 2018
Modernizing your data center operations doesn't always mean expanding to a physically larger system
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modernization, data center lifecycle services, refresh-ups, mprs, assessment services, old ups, aging infrastructure
    
Schneider Electric
Published By: Delphix     Published Date: Feb 28, 2013
Agile Data Management eliminates the common barriers to successful agile development, reducing both the cost and the risk of short, parallel development projects. This paper discusses common challenges and the technical solutions for addressing them.
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database virtualization, roi, data management, data management software, agile development, application development
    
Delphix
Published By: CA Technologies EMEA     Published Date: Aug 03, 2017
The GDPR is set to have wide-ranging implications for the type of data which can be used in non-production environments. Organizations will need to understand exactly what data they have and who’s using it, and must be able to restrict its use to tasks for which consent has been given.
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organization scope, data controllers, technology, gdpr, application regulation, corporate rules, contractual clauses, anonymize data, privacy projects
    
CA Technologies EMEA
Published By: CA Technologies EMEA     Published Date: Aug 07, 2017
A big part of GDPR compliance will focus on how data is collected going forward. But a substantial emphasis will fall on the data businesses already hold. With many mainframes containing generations-old data, a manual data audit is completely unrealistic. That’s where CA comes in. CA Data Content Discovery enables organizations to find, classify and protect mission essential mainframe data—three valuable steps toward achieving GDPR compliance.
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gdpr, personal data, social information, ip addresses, ca technologies, mainframe security, anonymize data, privacy projects
    
CA Technologies EMEA
Published By: Group M_IBM Q119     Published Date: Mar 08, 2019
The transformation imperative is now the imperative of the entire enterprise. The challenge to leaders of top financial services firms is to build operating models that are ready for anything. Join American Banker Editor-at-Large, Penny Crosman, and former IBM Global leader for strategy and design, Robert Schwartz, as they discuss this idea, pulling clips from a recent event for industry leaders, including: Bridget van Kalingen, IBM on redefining success with cloud, AI, quantum and blockchain Shari van Cleave, Wells Fargo on rethinking data strategies in the age of AI Bret King, Moven on rebuilding the bank from the ground up Rob Bauer, AIG on the ways to get started with transformative projects Marty Lippert, MetLife on creating space for innovation by migrating core operations off of legacy infrastructure and many more
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Group M_IBM Q119
Published By: Riverbed     Published Date: May 19, 2016
This white paper reveals how Hyper-converged Edge takes the value that Hyper-converged Infrastructure (HCI) provides in the Data Centre and projects it out to a “stateless” remote office.
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riverbed, hyper-converged infrastructure, data center, data, database development, data warehousing, virtualization, infrastructure
    
Riverbed
Published By: Teradata     Published Date: May 02, 2017
A Great Use of the Cloud: Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs. This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.
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data projects, data volume, business data, cloud security, data storage, data management, cloud privacy, encryption, security integration
    
Teradata
Published By: IBM     Published Date: Apr 01, 2016
Read the eBook to: 1) Expand what you know about Big Data; 2) Learn about the Big Data Zones Model that brings a new approach to managing data, faster to deploy, faster to insights and with less risk; 3) Gain confidence in your Big Data projects and learn about the importance of governance in a Big Data world
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ibm, ibm connect, big data, big data zones model, data management, business technology
    
IBM
Published By: IBM     Published Date: Jul 06, 2016
Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
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ibm, big data, trusted data, data management, data solutions, data center
    
IBM
Published By: IBM     Published Date: Jul 08, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, data management, data center
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Who's afraid of the big (data) bad wolf? Survive the big data storm by getting ahead of integration and governance functional requirements Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
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ibm, big data, trusted data, data management, data solutions, business technology, data center
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. And it must automatically discover, protect and monitor sensitive information as part of big data applications.
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IBM
Published By: IBM     Published Date: Jan 27, 2017
High-priority big data and analytics projects often target customer-centric outcomes such as improving customer loyalty or improving up-selling. In fact, an IBM Institute for Business Value study found that nearly half of all organizations with active big data pilots or implementations identified customer-c entric outcomes as a top objective (see Figure 1).1 However, big data and analytics can also help companies understand how changes to products or services will impact customers, as well as address aspects of security and intelligence, risk and financial management, and operational optimization.
Tags : 
    
IBM
Published By: IBM     Published Date: Jul 06, 2017
Effectively using and managing information has become critical to driving growth in areas such as pursuing new business opportunities, attracting and retaining customers, and streamlining operations. In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before. These needs create a daunting array of workload challenges and place tremendous demands on your underlying IT infrastructure and database systems. In many cases, these systems are no longer up to the task—so it’s time to make a decision. Do you use more staff to keep up with the fixes, patches, add-ons and continual tuning required to make your existing systems meet performance goals, or move to a new database solution so you can assign your staff to new, innovative projects that move your business forward?
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database, growth, big data, it infrastructure, information management
    
IBM
Published By: KPMG     Published Date: Jul 10, 2018
The real value of i4.0 comes from the integration of automation, data, analytics, manufacturing and products in a way that unlocks new business and operating models. Are you ready for the next industrial revolution? Read this report to find out: • why deep pockets alone won’t ensure i4.0 success • how to scale up projects and capabilities to drive enterprise-level value • what capabilities, controls and culture are required to support i4.0 success • how to unlock value by integrating smart processes and smart products • how to bring suppliers and value chain players into the i4.0 environment.
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KPMG
Published By: Delphix     Published Date: Sep 10, 2014
Most data masking products can create masked data copies but not distribute or update them, resulting in projects that fail to live up to expectations. Learn why Delphix’s Agile Masking solution is the only product that solves both masked data creation and delivery challenges.
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secure data, digitization, data breaches, privacy regulations, sensitive data, internal, customer information, application landscapes, database development, database security
    
Delphix
Published By: Delphix     Published Date: Mar 24, 2015
Data virtualization is becoming more important as industry-leading companies learn that it delivers accelerated IT projects at reduced cost. With such a dynamic space, one must make sure that vendors will deliver on their promises. This white paper outlines the top 10 qualification questions to ask before and during the proof of concept.
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Delphix
Published By: Delphix     Published Date: Mar 24, 2015
Many data centers are aging and organizations still use legacy applications, which drives up operational costs and limits the ability to upgrade software. Download this white paper to find out how Delphix Data as a Service can remove the biggest constraints and bottlenecks of your modernization projects.
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Delphix
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