big data solution

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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, knowledge management, data management, business technology
    
IBM
Published By: IBM     Published Date: Apr 18, 2016
"Five high-value uses for big data: IBM has conducted surveys, studied analysts’ findings, talked with more than 300 customers and prospects and implemented hundreds of big data solutions. As a result, it has identified five high-value use cases that enable organizations to gain new value from big data."
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ibm, mdm, big data, trusted data, data solutions, data management
    
IBM
Published By: IBM     Published Date: Apr 18, 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
    
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 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, data management
    
IBM
Published 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
    
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.
Tags : 
ibm, big data, trusted data, data management, data solutions, data center
    
IBM
Published By: IBM     Published Date: Jul 06, 2016
How can your organization realize the financial benefits of the cloud while ensuring information culled from cloud sources is secure and trustworthy? The answer is governance. Download to read more.
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ibm, big data, trusted data, data management, data solutions, data center
    
IBM
Published By: IBM     Published Date: Jul 06, 2016
Five high-value uses for big data: IBM has conducted surveys, studied analysts’ findings, talked with more than 300 customers and prospects and implemented hundreds of big data solutions. As a result, it has identified five high-value use cases that enable organizations to gain new value from big data.
Tags : 
ibm, mdm, big data, trusted data, data solutions, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 06, 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.
Tags : 
ibm, ibm infosphere, big data, data optim, data management, 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
    
IBM
Published By: IBM     Published Date: Jul 12, 2016
Join us for a complimentary webinar with Mark Simmonds, IBM big data IT Architect who will talk with leading analyst Mike Ferguson of Intelligent Business Strategies about the current fraud landscape. They will discuss the business impact of fraud, how to develop a fraud-protection strategy and how IBM z Systems analytics solutions and predictive models can dramatically reduce your risk exposure and loss from fraud.
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ibm, z systems, fraud loss reduction, fraud management, fraud prevention, fraud analytics, roi
    
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.
Tags : 
ibm, big data, trusted data, data management, data solutions, business technology, data center
    
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?
Tags : 
ibm, ibm pure data system, big data, data analytics, analytics architecture, data warehouse, data management, business technology
    
IBM
Published By: IBM     Published Date: Oct 18, 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
    
IBM
Published By: IBM     Published Date: Oct 18, 2016
The worldwide growth rate of digital data is staggering. If you're a CIO or a data center administrator, data growth statistics aren't just big numbers, they are a big problem -- for your company. Email messages, social media and blog posts, text and instant messages, photos, video and audio, machine-generated data, and transactional detail are on track to overwhelm your storage capacity. Read this paper for practical advice and smarter solutions for managing the information in your organization and getting back to a position of mastery over your data. Get this valuable resource now.
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ibm, analytics, big data, information governance, ecm, information lifecycle governance, knowledge management, data management
    
IBM
Published By: IBM     Published Date: Jan 19, 2017
The outcome of any big data analytics project, however, is only as good as the quality of the data being used. As big data analytics solutions have matured and as organizations have developed greater expertise with big data technologies he quality and trustworthiness of the data sources themselves are emerging as key concerns. 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, analytics, ecm, data, big data, information governance, data management, business technology
    
IBM
Published By: IBM     Published Date: Mar 29, 2017
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.
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cloud, analytics, data, organization, ibm
    
IBM
Published 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.
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data integration, data security, data optimization, data virtualization, database security, data analytics, data innovation
    
IBM
Published By: IBM     Published Date: Apr 18, 2017
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. An effective big data integration solution delivers simplicity, speed, scalability, functionality and governance to produce consumable data. To cut through this misinformation and develop an adoption plan for your Hadoop big data project, you must follow a best practices approach that takes into account emerging technologies, scalability requirements, and current resources and skill levels.
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data integration, data security, data optimization, data virtualization, database security, data migration, data assets, data delivery
    
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: IBM     Published Date: Oct 16, 2017
This white paper examines how some of the ways organizations use big data make their infrastructures vulnerable to attack. It presents recommended best practices organizations can adopt to help make their infrastructures and operations more secure. And it discusses how adding advanced security software solutions from IBM to their big-data environment can fill gaps that big-data platforms by themselves do not address. It describes how IBM® Security Guardium®, an end-to- end solution for regulatory compliance and comprehensive data security, supports entitlement reporting; user-access and activity monitoring; advanced risk analytics and real-time threat detection analytics; alerting, blocking, encryption and other data protection capabilities, as well as automated compliance workflows and reporting capabilities, to stop threats.
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security, big data, ibm, data protection
    
IBM
Published By: IBM     Published Date: Aug 08, 2018
An IBM Cloud configuration completed a big data analytics workload in less time and with greater throughput than an AWS solution
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IBM
Published By: IBM APAC     Published Date: Aug 25, 2017
Transitioning from traditional IT to cloud IT is not an all-at-once, big bang effort. Rather, the cloud adoption process should be an agile, incremental process. And the first part of that process is understanding the different cloud models. Contrary to popular belief, cloud isn’t necessarily only public cloud, multi-tenant, and hosted in a vendor’s data center. It can also be private cloud, single-tenant, and/or hosted in a corporate data center. Often the best solution is a hybrid combination of these options. This paper will show you the advantages of hybrid cloud applications and explore the considerations you should make to find an optimal solution for your organization.
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cost reduction, cost optimization, business agility, innovation, business models
    
IBM APAC
Published By: IBM Watson Health     Published Date: Nov 10, 2017
To address the volume, velocity, and variety of data necessary for population health management, healthcare organizations need a big data solution that can integrate with other technologies to optimize care management, care coordination, risk identification and stratification and patient engagement. Read this whitepaper and discover how to build a data infrastructure using the right combination of data sources, a “data lake” framework with massively parallel computing that expedites the answering of queries and the generation of reports to support care teams, analytic tools that identify care gaps and rising risk, predictive modeling, and effective screening mechanisms that quickly find relevant data. In addition to learning about these crucial tools for making your organization’s data infrastructure robust, scalable, and flexible, get valuable information about big data developments such as natural language processing and geographical information systems. Such tools can provide insig
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population health management, big data, data, data analytics, big data solution, data infrastructure, analytic tools, predictive modeling
    
IBM Watson Health
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