big data projects

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By: Dell and Intel®     Published Date: Sep 17, 2014
According to the 2014 IDG Enterprise Big Data research report, companies are intensifying their efforts to derive value through big data initiatives with nearly half (49%) of respondents already implementing big data projects or in the process of doing so in the future. Further, organizations are seeing exponential growth in the amount of data managed with an expected increas of 76% within the next 12-18 months. With growth there are opportunities as well as challenges. Among those facing the big data challenge are finance executives, as this extraordinary growth presents a unique opportunity to leverage data assets like never before. * Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
Tags : dell and intel, big data for finance
     Dell and Intel®
By: Datastax     Published Date: Aug 07, 2018
The public sector has invested in big time in big data. But there’s one thing most public sector entities are dropping the ball on: real-time data, and how it can be combined with big data to increase citizen safety and make mission-critical digital transformation projects happen on-time and on budget. Read this white paper to learn why public sector entities need both big data and real-time data if they are going to deliver on their digital transformation promises.
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     Datastax
By: IBM     Published Date: Oct 19, 2015
This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
Tags : ibm, integration, data volume, business technology, information, data storage
     IBM
By: BMC Software     Published Date: May 28, 2014
"In the paper, “Integrate Big Data into Your Business Processes and Enterprise Systems” you’ll learn how to drive maximum value with an enterprise approach to Big Data. Topics discussed include: • How to ensure that your Big Data projects will drive clearly defined business value • The operational challenges each Big Data initiative must address • The importance of using an enterprise approach for Hadoop batch processing
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     BMC Software
By: TIBCO Software APAC     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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     TIBCO Software APAC
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.
Tags : ibm, big data, inline analytics, business analytics, roi
     IBM
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.
Tags : customer analytics, data matching, big data, competitive advantage, customer loyalty
     IBM
By: IBM     Published Date: Jul 26, 2017
Business leaders are eager to harness the power of big data. However, as the opportunity increases, ensuring that source information is trustworthy and protected becomes exponentially more difficult. If not addressed directly, end users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organizations can boost confidence in big data. 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 seamle
Tags : mdm, big data, automation, organization
     IBM
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
Tags : ibm, ibm connect, big data, big data zones model, data management, analytics, data science
     IBM
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 science, data storage
     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
     IBM
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, analytics, data science
     IBM
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
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: 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?
Tags : database, growth, big data, it infrastructure, information management
     IBM
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.
Tags : ibm, analytics, ecm, data, big data, information governance
     IBM
By: Datastax     Published Date: Aug 23, 2017
About 10 years ago big data was quickly becoming the next big thing. It surged in popularity, swooning into the tech world's collective consciousness and spawning endless start-ups, thought pieces, and investment funding, and big data's rise in the startup world does not seem to be slowing down. But something's been happening lately: big data projects have been failing, or have been sitting on a shelf somewhere and not delivering on their promises. Why? To answer this question, we need to look at big data's defining characteristic - or make that characteristics, plural - or what is commonly known as 'the 3Vs": volume, variety and velocity.
Tags : datastax, big data, funding
     Datastax
By: BlueData     Published Date: Aug 19, 2015
Over the past few years, “Big Data” has evolved from an interesting technology topic into a source of major competitive advantage, in which IDG conducted a survey and found out that 60% of enterprises are planning on spending an average of $8 million on Big Data initiatives. However, somewhere between intention/investment and executive/production, Big Data initiatives are falling into a gap. Download this white paper to find out how to change the equation on Big Data spending and learn what the successful companies are doing in order to achieve a success from your Big Data applications.
Tags : bigdata, hadoop, big data spending, big data projects, it commitments
     BlueData
By: Netezza IBM US     Published Date: Feb 29, 2012
This white paper details seven steps that are critical for delivering big data projects that can be undertaken to turn the flood of data into an advantage, and truly understand one's network and one's customers, while turning up profitability.
Tags : network, network analytics, data management, customer relation, data projects, customer satisfaction, data quality
     Netezza IBM US
By: Datastax     Published Date: Apr 04, 2017
As the big data ecosystem continues to expand, new technologies are addressing the requirements for managing, processing, analyzing, and storing data to help companies benefit from the rich sources of information flowing into their organizations. From NoSQL databases to open source projects to commercial products offered on-premises and in the cloud, the future of big data is being driven by innovative new approaches across the data management lifecycle. The most pressing areas include real-time data processing, interactive analysis, data integration, data governance, and security. Download this report for a better understanding of the current landscape, emerging best practices and real-world successes.
Tags : evolution, big data, technology, datastax, nosql
     Datastax
By: Enterprise Management Associates     Published Date: Jul 20, 2015
Research from leading IT analyst firm Enterprise Management Associates (EMA) has evidenced strong and growing interest in cloud deployment models. While public cloud has gotten the earliest attention, stronger adoption is happening within private and hybrid models. In the EMA April 2014 report “Managing Networks in the Age of Cloud, SDN, and Big Data Network Management Megatrends 2014,” over 50% of respondents reported public/hybrid cloud initiatives were driving network management priorities. Since 2012, cloud projects have moved from early adopter status to mainstream business initiatives, and their impact on network management grew from 36% in 2012 to over 50% in 2014.
Tags : network optimization, application delivery, public/hybrid cloud initiatives, network management
     Enterprise Management Associates
By: Oco, Inc.     Published Date: Apr 18, 2008
The business intelligence (BI) “boom” of the 1990s was something BIG: big projects designed to give big companies with big budgets a competitive advantage. And while BI delivered big returns for some companies, for others it was nothing but a big headache. An estimated 50% of all data warehousing projects failed to meet their goals. Why? Because many companies underestimated just how big an undertaking BI really was.
Tags : oco, business intelligence, software as a service, saas, business intelligence, bi applications, business intelligence applications, roi, tco, return on investment, total cost of ownership, small business, soho, scalability, oco
     Oco, Inc.
By: Cisco     Published Date: Sep 07, 2016
This white paper highlights the maturation of big data technologies and strategies, and how companies are transitioning from one-off pilot projects to powerful enterprise capabilities that can be leveraged on a daily basis.
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     Cisco
By: IBM     Published Date: May 28, 2014
Read the whitepaper to find out how one client improved business value of their data by implementing InfoSphere Optim processes and technologies.
Tags : ibm, data lifecycle management, infosphere optim, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence, virtualize data, lifecycle management
     IBM
By: IBM     Published Date: May 28, 2014
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
Tags : ibm, data retention, information governance, archiving, historical data, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence
     IBM
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