data analytics

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By: IBM     Published Date: Jul 05, 2016
Today's data-driven organization is faced with magnified urgency around data volume, user needs and compressed decision time frames. In order to address these challenges, while maintaining an effective analytical culture, many organizations are exploring cloud-based environments coupled with powerful business intelligence (BI) and analytical technology to accelerate decisions and enhance performance.
Tags : ibm, datamart on demand, analytics, cloud, hybrid cloud, business insight, knowledge management, enterprise applications, data management, business technology, data center
     IBM
By: IBM     Published Date: Jul 05, 2016
Cloud-based data warehousing as-a-service, built for analytics
Tags : ibm, dashdb, data, analytics, data warehouse, cloud, analytics, business insights, knowledge management, enterprise applications, data management, business technology, data center
     IBM
By: IBM     Published Date: Jul 05, 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.
Tags : ibm, ibm pure data system, big data, data analytics, analytics architecture, knowledge management, enterprise applications, data management, business technology, data center
     IBM
By: IBM     Published Date: Jul 05, 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?
Tags : ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, knowledge management, data management, data center
     IBM
By: IBM     Published Date: Jul 05, 2016
In an environment where data is the most critical natural resource, speed-of-thought insights from information and analytics are a critical competitive imperative.
Tags : ibm, data warehouse, big data, analytics, data warehouse, business intelligence, knowledge management, data management, data center
     IBM
By: IBM     Published Date: Jul 06, 2016
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.
Tags : ibm, mdm, big data, data management, data matching, customer analytics, data center
     IBM
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.
Tags : ibm, idc, big data, data, analytics, information governance, knowledge management, data management, data center
     IBM
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.
Tags : ibm, osterman, roi, big data, data, analytics, information governance, knowledge management, data management, data center
     IBM
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.
Tags : ibm, forrester, data discovery, big data, data, analytics, information governance, knowledge management, data management
     IBM
By: IBM     Published Date: Jul 12, 2016
Informative infographic featuring case management and data capture solutions to put the customer experience first.
Tags : ibm, ecm, data analytics, analytics, big data, customer experience, knowledge management, enterprise applications, data management, data center
     IBM
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.
Tags : ibm, datamart on demand, data, analytics, big data, real-time data, cloud, cloud data analytics, knowledge management, enterprise applications, data center
     IBM
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.
Tags : ibm, data, analytics, predictive business, ibm spss, apache spark, coding, data science, software development, enterprise applications, data management, business technology, data center
     IBM
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.
Tags : ibm, big data, data analytics, ibm spss, predictive business, enterprise applications, data management, business technology, data center
     IBM
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.
Tags : ibm, forrester, data, analytics, big data, ibm information integration, governance, data management, business technology, data center
     IBM
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.
Tags : ibm, data, analytics, big data, data integration, data management, business technology, data center
     IBM
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.
Tags : ibm, ibm pure data system, big data, data analytics, analytics architecture, enterprise applications, data management, business technology, data center
     IBM
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?
Tags : ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, enterprise applications, data management, business technology, data center
     IBM
By: IBM     Published Date: Oct 18, 2016
Informative infographic featuring case management and data capture solutions to put the customer experience first.
Tags : ibm, ecm, data analytics, analytics, big data, customer experience, knowledge management, enterprise applications, data management, data center
     IBM
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
By: IBM     Published Date: Jan 27, 2017
While any number of reasons can prompt a change in data warehouse solutions, there are four key facts you need to know to help you make the right choice: 1. Complexity stifles business 2. Speed is business-friendly 3. Cost reduction is crucial 4. Analytics: The key to current and future success This e-book will explore those facts and explain why they are essential when evaluating your data warehouse options.
Tags : 
     IBM
By: IBM     Published Date: Mar 29, 2017
Not just some data—all of it. Internal, external, structured, unstructured, historical, real-time. And what if you could do it without a huge infrastructure project? You can. Take a closer look at how three companies capitalized on more data—almost instantly—with IBM® BigInsights® on Cloud.
Tags : analyze, data, cloud, ibm, analytics
     IBM
By: IBM     Published Date: Apr 18, 2017
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes. The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
Tags : data integration, data security, data optimization, data virtualization, database security, data analytics, data innovation
     IBM
By: IBM     Published Date: Sep 28, 2017
Here are the 6 reasons to change your database: Lower total cost of ownership Increased scalability and availability Flexibility for hybrid environments A platform for rapid reporting and analytics Support for new and emerging applications Greater simplicity Download now to learn more!
Tags : scalability, hybrid environment, emerging applications, rapid reporting
     IBM
By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
As organizations develop next-generation applications for the digital era, many are using cognitive computing ushered in by IBM Watson® technology. Cognitive applications can learn and react to customer preferences, and then use that information to support capabilities such as confidence-weighted outcomes with data transparency, systematic learning and natural language processing. To make the most of these next-generation applications, you need a next-generation database. It must handle a massive volume of data while delivering high performance to support real-time analytics. At the same time, it must provide data availability for demanding applications, scalability for growth and flexibility for responding to changes.
Tags : database, applications, data availability, cognitive applications
     Group M_IBM Q1'18
By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
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
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