ibm big data

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Published By: SAS     Published Date: Apr 16, 2015
Former Intel CEO Andy Grove once coined the phrase, “Technology happens.” As true as Grove’s pat aphorism has become, it’s not always good news. Twenty years ago, no one ever got fired for buying IBM. In the heyday of customer relationship management (CRM), companies bought first and asked questions later. Nowadays, executives are being enlightened by the promise of big data technologies and the role data plays in the fact-based enterprise. Leaders in business and IT alike are waking up to the reality that – despite the hype around platforms and processing speeds – their companies have failed to established sustained processes and skills around data.
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SAS
Published By: IBM     Published Date: Oct 13, 2011
This whitepaper from IBM reveals three ways that the most successful companies are taking action when they deploy business analytics.
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ibm, big data, analytics, knowledge discovery, data mining, business analytics and optimization, bao, business analytics, insight
    
IBM
Published By: IBM     Published Date: Oct 19, 2015
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, infosphere, data profiling, business glossary, partition, pipeline parallelism, information server, data sources, hadoop
    
IBM
Published By: IBM     Published Date: Apr 05, 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, little data, ibm spss solutions
    
IBM
Published By: IBM     Published Date: Feb 02, 2016
Advanced analytics can provide extremelyvaluable insight into today’s media viewers. This must-read report details the top 10 best practices for successfully implementing data analytics for driving profit, attracting new viewers, and increasing viewer loyalty.
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ibm, big data, analytics, media, entertainment, data analytics
    
IBM
Published By: IBM     Published Date: May 17, 2016
Analyst Mike Ferguson of Intelligent Business Strategies writes about the enhanced role of transactional DBMS systems in today's world of Big Data. Learn more about how Big Data provides richer transactional data and how that data is captured and analyzed to meet tomorrow’s business needs. Access the report now.
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ibm, intelligent business solutions, big data, transaction data, business analytics
    
IBM
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
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
This white paper discusses the concept of shared data scale-out clusters, as well as how they deliver continuous availability and why they are important for delivering scalable transaction processing support.
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ibm, always on business, cloud, big data, oltp, ibm db2 purescale
    
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: Jan 27, 2016
Forrester Research evaluated 13 big data predictive analytics solution vendors and IBM emerged as one of the three leaders. Learn why.
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ibm, forrester wave, big data, predictive analytics
    
IBM
Published By: IBM     Published Date: Feb 22, 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.
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ibm, data, mdm, big data, insight, infosphere
    
IBM
Published By: IBM     Published Date: Jan 27, 2016
Learn how to uncover actionable insights through the IBM big data platform.
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ibm, machine data, insights, big data, real time
    
IBM
Published By: IBM     Published Date: Feb 02, 2016
In this white paper, IDC Energy Insights discusses the current and future challenges that utilities face in meeting their business objectives and the role analytics will play.
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analytics, energy, utilities, ibm, big data
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources.
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ibm, data, performance, scalability, information integration, big data
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
Survive the big data storm by getting ahead of integration and governance functional requirements
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ibm, data, big data, integration, governance
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
Apache Hadoop technology is transforming the economics and dynamics of big data initiatives by supporting new processes and architectures that can help cut costs, increase revenue and create competitive advantage.
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ibm, data, big data, integration, hadoop
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.
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ibm, data, big data, integration, governance, iig, infosphere
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
This e-book describes how a data refinery can make trusted data available quickly and easily to people and systems across your organization.
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ibm, data, big data, hybrid, refined data, data refinery
    
IBM
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
    
IBM
Published By: IBM     Published Date: Apr 11, 2016
This paper explores why your business needs the latest operational decision management (ODM) solutions to help turn data insights into action. Discover how IBM Operational Decision Manager software and the IBM Business Process Manager platform work together to: *Recognize patterns that suggest opportunity or risk *Create and shape business events by automating decisions *Bring more dimension and precision to decision making by applying analytics to big data *Help you implement the right business processes by understanding data in context.
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ibm, smarter process, operational decision management, odm
    
IBM
Published By: IBM     Published Date: Apr 18, 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 optim, ibm infosphere
    
IBM
Published By: IBM     Published Date: Apr 18, 2016
While the term 'big data' has only recently come into vogue, IBM has designed solutions capable of handling very large quantities of data for decades. IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.
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ibm, ibm infosphere, big data, data optim, data management
    
IBM
Published By: IBM     Published Date: Apr 18, 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 optim, big data, hybrid data environments
    
IBM
Published By: IBM     Published Date: Apr 19, 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
    
IBM
Published By: IBM     Published Date: Apr 19, 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
    
IBM
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