data and analytics

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Published By: IBM     Published Date: Apr 22, 2016
The upside of disruption: Reinventing business processes, organizations and industries in the wake of the digital revolution
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ibm, ibm big data and analytics, ibm big data, trusted data, big data, data management
    
IBM
Published By: IBM     Published Date: Apr 22, 2016
This white paper explains the value of putting data and insight to work to accelerate an organization’s journey to becoming a Cognitive Business, by enhancing their investments in data and analytics and realizing new opportunities from cognitive systems that understand, reason and learn.
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ibm, ibm analytics, cognitive business, watson, think data, knowledge management, enterprise applications, data management, business technology
    
IBM
Published 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.
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ibm, datamart on demand, analytics, cloud, hybrid cloud, business insight, knowledge management, enterprise applications, data management, business technology, data center
    
IBM
Published 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.
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ibm, data warehouse, big data, analytics, data warehouse, business intelligence, knowledge management, data management, data center
    
IBM
Published 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.
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ibm, mdm, big data, data management, data matching, customer analytics, 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: 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.
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ibm, datamart on demand, data, analytics, big data, real-time data, cloud, cloud data analytics, knowledge management, enterprise applications, data center
    
IBM
Published 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.
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IBM
Published 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!
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scalability, hybrid environment, emerging applications, rapid reporting
    
IBM
Published 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.
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database, applications, data availability, cognitive applications
    
Group M_IBM Q1'18
Published 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.
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cloud, database, hybrid cloud, database platform
    
Group M_IBM Q1'18
Published By: KPMG     Published Date: Jul 10, 2018
As organisations increasingly leverage data, sophisticated analytics, robotics and AI in their operations, we ask who should be responsible for trusted analytics and what good governance looks like. Read this report to discover: • the four key anchors underpinning trust in analytics – and how to measure them • new risks emerging as the use of machine learning and AI increases • how to build governance of AI into core business processes • eight areas of essential controls for trusted data and analytics.
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KPMG
Published By: KPMG     Published Date: Jul 11, 2018
As organisations increasingly leverage data, sophisticated analytics, robotics and AI in their operations, we ask who should be responsible for trusted analytics and what good governance look like? Read this report to discover: • the four key anchors underpinning trust in analytics – and how to measure them • new risks emerging as the use of machine learning and AI increases • how to build governance of AI into core business processes • eight areas of essential controls for trusted data and analytics. Download the report now:
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KPMG
Published By: Teradata     Published Date: Jun 12, 2013
Download this paper to learn how Unified Data Architecture™ can bridge the gap between the business language of SQL, the extreme processing power of MapReduce, and the big data residing in Hadoop to provide a unified, high-performance big data analytics system for the enterprise.
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data, big data, unified data architecture
    
Teradata
Published By: Teradata     Published Date: Jun 12, 2013
This whitepaper details how BCBSNC leverages its Teradata environment to achieve key strategic goals.
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big data, analytics, healthcare
    
Teradata
Published By: Visier     Published Date: Nov 04, 2014
With workforce analytics, HR professionals can play a more pivotal role in their organizations to help direct senior management and hiring managers in connecting the dots between their company’s overall performance and their investment in their workforce. Workforce analytics empower HR professionals to shift from being an operational function to becoming more of a strategic role within their organization. Workforce analytics uncovers deep insights into workforce data by drilling down into the data and highlighting both patterns of success to be repeated and patterns of failure that could lead to risk and impact. Go from intuition- to fact-based workforce decision-making with this comprehensive guide to workforce analytics. This eBook includes advice on how to get started, tips to ensure successful implementation, and key recommendations for finding the right solution
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visier, workforce analytics, hr metrics, planning
    
Visier
Published By: Visier     Published Date: Jan 30, 2015
Until recently, ConAgra Foods struggled to collect accurate, analyzable data about its workforce. Information was spread across the organization in siloed systems within various departments and was often difficult to reconcile. In a few short years, however, ConAgra Foods has been able to leverage technology solutions to gain real-time insights into its employee data and can now analyze its workforce across a wide range of subject areas. Download this case study to see how ConAgra Foods transformed the role of HR through analytics, and to get guidance on how your organization can adopt a similar approach.
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visier, conagra, workforce, hr analytics, real-time insights
    
Visier
Published By: K2     Published Date: Aug 24, 2015
This complementary template focuses on the eight key areas for business process automation success to help you evaluate the quality of the platforms you are considering – and to ensure that the system you choose is agile and flexible enough to meet your business needs. WORKFLOW TOOLS AND FEATURES FORMS INTEGRATION AND DATA SECURITY MOBILE USER AND RUN-TIME EXPERIENCE REPORTING, ANALYTICS AND MONITORING MAINTENANCE/SUPPORT/HELP DOCUMENTATION
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K2
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Today, businesses pour Big Data into data lakes to help them answer the big questions: Which product to take to market? How to reduce fraud? How to retain more customers? People need to get these answers faster than ever before to reduce “time to answer” from months to minutes. The data is coming in fast and the answers must come just as fast. The answer is self-service data preparation and analytics tools, but with that comes an expectation that the right data is going to be there. Only by using a data catalog can you find the right data quickly to get the expected insight and business value. Download this white paper to learn more!
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. Download this white paper to find out how you can improve your data preparation for business analytics.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
For many years, traditional businesses have had a systematic set of processes and practices for deploying, operating and disposing of tangible assets and some forms of intangible asset. Through significant growth in our inquiry discussions with clients, and in observing increased attention from industry regulators, Gartner now sees the recognition that information is an asset becoming increasingly pervasive. At the same time, CDOs and other data and analytics leaders must take into account both internally generated datasets and exogenous sources, such as data from partners, open data and content from data brokers and analytics marketplaces, as they come to terms with the ever-increasing quantity and complexity of information assets. This task is clearly impossible if the organization lacks a clear view of what data is available, how to access it, its fitness for purpose in the contexts in which it is needed, and who is responsible for it.
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Waterline Data & Research Partners
Published By: Datawatch     Published Date: Feb 03, 2016
Some companies estimate that up to 80 percent of their analysts’ time is spent on data preparation. Thorough, automated data preparation, however, can quickly transform raw information into reliable, consistent data sets ready for analysis. This report from Gartner details why information management and business analytics leaders must introduce data preparation into their big data initiatives in order to improve both the understanding of the data, and the productivity of analysts. Read the report now to learn more.
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datawatch, data preparation, data analytics, data analysis, analyst productivity, information management, business analytics, big data
    
Datawatch
Published By: SAP     Published Date: Jul 18, 2016
Retail is a data-intensive industry. Knowing the customer better than competition and having the ability to orchestrate business decisions is the new retail competitive battlefield. Read the white paper to learn customer insights on the future of retail analytics.
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SAP
Published By: SAP     Published Date: Jul 18, 2016
Organizations are investing in new analytics technologies to improve agility and performance. The technology and data must come together to serve the business users under the guidance and management of IT. Read the white paper to learn key findings in the analytics market and learn how to deliver analytics capabilities suitable for a wide range of use cases.
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SAP
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