big data solution

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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.
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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.
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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, 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: 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, data management, business technology, data center
    
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 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: F5 Networks Inc     Published Date: Mar 30, 2018
It seems like every day there’s another article about IoT, big data analytics, and cloud architectures—and their unlimited potential for companies trying to gain a competitive edge in this digital world. If your business is in the midst of a digital transformation (and chances are, it is), you’re probably already enjoying some the benefits of the public cloud: economies of scale, preconfigured solutions that can be spun up with a few clicks, utility billing, and more. What you may not have heard, however, is how the shared security model of the public cloud affects your security responsibilities —and how it can be used to your advantage in these multicloud environments.
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roi, cloud, security, architectures, business, economies
    
F5 Networks Inc
Published By: RSA Security     Published Date: Oct 24, 2013
Big data security analytics is no longer a visionary idea -- leading enterprises recognize that their immediate security requirements demand this type of solution.
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rsa, emc, enterprise strategy, big data, security, security analytics
    
RSA Security
Published By: HP     Published Date: Feb 02, 2015
Customers across all industries are transitioning to the 3rd Platform, where IT organizations are seeking to leverage mobile, social, cloud, and big data solutions to drive business value. At the same time, the pace at which the business operates is continually accelerating.
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HP
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: HotSchedules     Published Date: Apr 25, 2018
Restaurant technologies have transformed drastically over the past decade, however there have been several misconceptions about the impact big data can have on restaurant operations. According to a Hospitality Technology study, only 23% of restaurants have confidence in overall tech innovation. When it comes to the state of technology many restaurants are still stuck with disconnected architecture, complex legacy systems, lack of visibility, and high maintenance costs. These challenges ultimately intimidate restaurant operators to outdated systems and processes that significantly limit their business growth. This ebook looks at the four pressing challenges and presents one unifying solution.
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HotSchedules
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: Group M_IBM Q119     Published Date: Mar 04, 2019
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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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
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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Group M_IBM Q119
Published By: Red Hat, Inc.     Published Date: Jul 10, 2012
Today, as IT departments struggle to design and implement solutions capable of managing exponential data growth with strict requirements for application scale and performance, many of them are turning to in-memory data grids (IMDGs).
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it departments, data growth, managing data growth, application scale, application performance, in-memory data grids, imdgs, big data, data volume, data velocity, data variability, data management, data collection, data processing, data scaling, data storage systems, data access, data solutions, data challenges, management tooling
    
Red Hat, Inc.
Published By: Red Hat, Inc.     Published Date: Jul 10, 2012
Is data changing the way you do business?Is it inventory sitting in your warehouse? The good news is data-driven applications enhance online customer experiences, leading to higher customer satisfaction and retention, and increased purchasing.
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it planning, data, data-driven applications, data challenges, data solutions, big data solutions, big data challenges, in-memory databases, web-abpplications, in-memory data grid, nosql, storage nodes, e-commerce applications, social applications, logisitcs applications, trading applications, data scaling, rest, memcached, hot rod
    
Red Hat, Inc.
Published By: Teradata     Published Date: May 01, 2015
Creating value in your enterprise undoubtedly creates competitive advantage. Making sense of the data that is pouring into the data lake, accelerating the value of the data, and being able to manage that data effectively is a game-changer. Michael Lang explores how to achieve this success in “Data Preparation in the Hadoop Data Lake.” Enterprises experiencing success with data preparation acknowledge its three essential competencies: structuring, exploring, and transforming. Teradata Loom offers a new approach by enabling enterprises to get value from the data lake with an interactive method for preparing big data incrementally and iteratively. As the first complete data management solution for Hadoop, Teradata Loom enables enterprises to benefit from better and faster insights from a continuous data science workflow, improving productivity and business value. To learn more about how Teradata Loom can help improve productivity in the Hadoop Data Lake, download this report now.
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data management, productivity, hadoop, interactive, enterprise
    
Teradata
Published By: Juniper Networks     Published Date: Aug 08, 2017
Facing the future requires enterprises to embark on a digital transformation, employing new technologies such as AI, big data, IoT, and the cloud. Enterprises need a long-term, trusted partner who will support them on their journey and understand their vision. Switch to Juniper Networks and align your enterprise with an innovation leader that places scalable solutions, high availability, productivity, security, and R&D at the forefront, all while keeping simplicity and savings in mind.
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Juniper Networks
Published By: Juniper Networks     Published Date: Aug 08, 2017
As enterprises embark on the digital transformation to take advantage of artificial intelligence, big data, machine learning, IoT, and cloud, they need a network infrastructure that gives them a solid foundation. Juniper Networks® Unite CloudEnabled Enterprise allows networking across your entire enterprise—campus, branch, and data center—ultimately helping you reduce risk, increase agility, lower costs, and enhance the customer experience. Here are the Top 6 reasons why enterprises embarking on the journey of digital transformation should switch to the Juniper Unite Cloud-Enabled Enterprise solution.
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Juniper Networks
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
Published By: AstuteIT_ABM_EMEA     Published Date: Feb 02, 2018
The demand for databases is on the rise as organizations build next-generation business applications. NoSQL offers enterprise architecture (EA) pros new choices to store, process, and access new data formats, deliver extreme web-scale, and lower data management costs. Forrester’s 26-criteria evaluation of 15 big data NoSQL solutions will help EA pros understand the choices available and recommend the best for their organization. This report details our findings about how each vendor fulfills our criteria and where they stand in relation to each other to help EA.
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nosql, market, industries, strategy, presence, vendor
    
AstuteIT_ABM_EMEA
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