big data

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Published By: IBM     Published Date: Jul 08, 2016
This paper from Osterman Research, explores the origins of the "information problem" many organizations are now facing and presents a detailed discussion of how to calculate your current information costs as well as how to calculate the ROI of an information governance program.
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ibm, osterman, roi, big data, data, analytics, information governance, knowledge management, data management, data center, data science
    
IBM
Published By: IBM     Published Date: Jul 08, 2016
Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization’s risk and enhancing security.
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ibm, forrester, data discovery, big data, data, analytics, information governance, knowledge management, data management, data science
    
IBM
Published By: IBM     Published Date: Jul 12, 2016
This video provides an overview as to how governments can provide better services to citizens with IBM Case & Capture. Watch to learn more.
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ibm, ecm, analytics, government, big data, ibm case and capture, knowledge management, enterprise applications, data center, data science
    
IBM
Published By: IBM     Published Date: Jul 12, 2016
Informative infographic featuring case management and data capture solutions to put the customer experience first.
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ibm, ecm, data analytics, analytics, big data, customer experience, knowledge management, enterprise applications, data management, data center, data science
    
IBM
Published By: IBM     Published Date: Jul 14, 2016
How do you keep 130,000 guests safely entertained, fed, watered and informed in a sustainable way? Roskilde Festival knew that the critical insights lay hidden in huge volumes of real-time data. The Copenhagen Business School used IBM technologies to build a cloud big data lab that correlates information from multiple sources, delivering valuable insight for planning and running the festival. Download to learn more.
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ibm, datamart on demand, data, analytics, big data, real-time data, cloud, cloud data analytics, knowledge management, enterprise applications, data center design and management, data center, data science
    
IBM
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: IBM     Published Date: Jul 14, 2016
IBM SPSS Solutions offer a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end.
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ibm, big data, data analytics, ibm spss, predictive business, enterprise applications, data management, business technology, data center, analytics, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
To help enterprises create trusted insight as the volume, velocity and variety of data continue to explode, IBM offers several solutions designed to help organizations uncover previously unavailable insights and use them to support and inform decisions across the business. Combining the power of IBM® InfoSphere® Master Data Management (MDM) with the IBM big data portfolio creates a valuable connection: big data technology can supply insights to MDM, and MDM can supply master data definitions to big data.
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ibm, mdm, big data, ibm infosphere, mdm advantagw, knowledge management, enterprise applications, business technology, analytics
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Use big data to build an integrated, more personalized approach to marketing, business strategy, customer service and more.
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ibm, analytics, data, mdm, ibm software, business strategy, customer service, knowledge management, enterprise applications, business technology
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Data quality and master data management in a hybrid environment
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ibm, mdm, big data, ibm infosphere, knowledge management, enterprise applications, business technology, analytics
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
IBM commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study and examine the potential return on investment (ROI) enterprises may realize by leveraging IBM InfoSphere Information Integration and Governance (IIG) solutions.
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ibm, total economic impact, tei study, roi, return on investment, big data, data governance, governance, ibm infosphere, data management, business technology, data center, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
This e-book describes how a data refinery can make trusted data available quickly and easily to people and systems across your organization. It includes simple steps you can take to start exploring - and implementing - this strategy for handling the challenges of hybrid data environments.
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ibm, trusted data, data governance, governance, big data, hybrid data environments, data management, business technology, data center, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Cloud-based data and processing services present too much opportunity for organizations to ignore. How can organizations realize the obvious financial benefits of the cloud while ensuring information culled from cloud sources is secure and trustworthy? Good hybrid information governance implies several priorities for IT and the business which are based on these foundational pillars: - Broad agreement on what information means - Clear agreement on how owned information assets will be maintained and monitored - Standard practices for securing strategic information assets - Enterprise data integration strategy
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ibm, trusted data, big data, governance, data governance, data management, business technology, data center, data science
    
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, analytics, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by leveraging IBM InfoSphere Information Integration and Governance (IIG) solutions.
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ibm, forrester, data, analytics, big data, ibm information integration, governance, data management, business technology, data center, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
IBM InfoSphere Information Server connects to many new ‘at rest’ and streaming big data sources, scales natively on Hadoop using partition and pipeline parallelism, automates data profiling, provides a business glossary, and an information catalog, plus also supports IT.
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ibm, data, analytics, big data, data integration, data management, business technology, data center, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
This white paper describes how IBM’s Pure Data System for Analytics delivers speed and simplicity to help organizations become more responsive and agile in today’s increasingly mobile and data-driven market.
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ibm, ibm pure data system, big data, data analytics, analytics architecture, enterprise applications, data management, business technology, data center, analytics, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse. In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
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ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, enterprise applications, data management, business technology, data center, analytics, data science
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
This e-book highlights the benefits of Hadoop across several industries and explores how IBM® Biglnsights for Apache™ Hadoop® combines open source Hadoop with enterprise-grade management and analytic capabilities.
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ibm, analytics, big data, hadoop, enterprise, ibm biginsights, apache, enterprise management, enterprise applications, data management, business technology, data science
    
IBM
Published By: IBM     Published Date: Oct 18, 2016
Informative infographic featuring case management and data capture solutions to put the customer experience first.
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ibm, ecm, data analytics, analytics, big data, customer experience, knowledge management, enterprise applications, data management, data center, data science
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business.
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IBM
Published By: IBM     Published Date: Jan 27, 2017
The bottom line is that those that have the most customer insight will win because they know what customers want. So the question is how will you get that insight? What is it that you don’t know about customers in the market(s) that you operate in? Do you have all the attributes about customers in your MDM system that could be of value to your business? Do you know about all the relationships that your customers have in your MDM system? In most cases, the answer to the above questions is no which inevitably means one thing. You need more data
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IBM
Published By: IBM     Published Date: Jan 27, 2017
A big data integration platform that is flexible and scalable is needed to keep up with today’s ever-increasing big data volume.
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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.
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IBM
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