data and analytics

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Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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Group M_IBM Q2'19
Published By: Intel     Published Date: Apr 16, 2019
The data center is coming under immense pressure. The boom in connected devices means increasing volumes of data – and all that needs processing. One way for CSPs to accelerate customer workloads is by using FPGAs, which are easier to use than ever before. Download Intel's latest eGuide, ‘FPGA-as-a-Service: A Guide for CSPs' to discover: • How to add FPGAs to the data center • The structure of the Intel® Acceleration Stack for FPGAs • Adding off-the-shelf accelerator functions • How FPGAs can accelerate many cloud services, such as database as a service and analytics as a service
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Intel
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
Published By: SAP     Published Date: Jul 18, 2016
As your business transitions to a digital enterprise, you can start to give your employees, partners, and customers immediate, data-driven, and even predictive insight into what’s going on – in a way that’s relevant for them. Strategic use of on premise and cloud-based analytics accelerates this process. Read the solution brief to see how SAP is continuing to invest in on premise solutions as part of their effort to reimagine analytics.
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SAP
Published By: IBM     Published Date: May 03, 2016
In our 33-criteria evaluation of customer analytics vendors, we identified the 11 most significant software providers in this category - adobe, agilone, alteryx, angoss, FICo, IBM, Manthan, Pitney Bowes, saP, sas, and teradata - and researched, analyzed, and scored them. this evaluation details our findings about how well each vendor fulfills our criteria and where each vendor stands in relation to each other. this report will help customer insights (CI) professionals and marketers make the right choice for their customer analytics needs.
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ibm, forrester, customer analytics, customer insights, business technology
    
IBM
Published By: IBM     Published Date: Oct 21, 2016
Between the Internet of Things, customer experience and loyalty programs, social network monitoring, connected enterprise systems and other information sources, today's organizations have access to more data than they ever had before-and frankly, more than they may know what to do with. The challenge is to not just understand that data, but actualize it and use it to recognize real business value. This ebook will walk you through a sample scenario with Albert, a data scientist who wants to put text analytics to work by using the Word2vec algorithm and other data science tools.
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ibm, analytics, aps, aps data, open data science, data science, word2vec, enterprise applications, business technology, data center
    
IBM
Published By: Oracle     Published Date: Oct 20, 2017
What do these market-defining trends have in common? · Analytics for all · Analytics as competitive differentiator · Internet of Things · Artificial intelligence/Machine learning/Cognitive computing · Real-time analytics/event management They all rely on data – timely, accurate data delivered within an insightful context – to deliver value. The question is: who in the enterprise is most qualified and prepared to help deliver on the vision and values of the data-driven enterprise? It’s going to take a special type of professional to deliver that value to enterprises. Organizations are seeking professionals to step forward and take the lead, provide guidance and lend expertise to move into the brave new world of digital. The move to digital and all that it entails – sophisticated data analytics, online customer engagement and digital process efficiency – requires, above all, the skills and knowledge associated with handling data and turning it into insights. The move to digital is also a
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Oracle
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, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
Analytics relies on BI, Big Data, and data discovery to provide reporting, trend what-if analysis. Analytics is transforming data into insight.
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ibm, analytics, business intelligence, data, data management
    
IBM
Published By: IBM     Published Date: Jan 30, 2017
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
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ibm, talent analytics, cognitive computing, analytics, engagement
    
IBM
Published By: IBM     Published Date: Aug 24, 2017
Data governance is all about managing data, by revising that data to standardize it and bring consistency to the way it is used across numerous business initiatives. What’s more, data governance ensures that critical data is available at the right time to the right person, in a standardized and reliable form. A benefit that fuels better organization of business operations, resulting in improved productivity and efficiency of that organization. Thus, the importance of proper data governance cannot be understated. The concepts of data governance have evolved, where the first iteration of data governance, often referred to as version 1.0, focused on three simplistic elements: objectives, structure and processes; having a limited focus and scope due to its tactical usage. The opportunity from the growing value of data in the realm of analytics, business intelligence, and generating insights was left unrealized. Today, organizations are moving towards what can be called Data Governance 2.0,
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ibm, unified governance strategy, data management, data governance
    
IBM
Published By: IBM     Published Date: Oct 17, 2017
The data quality tools market continues to show strong revenue growth, driven by cost, process optimization and digital business initiatives. Applying data quality tools to existing and emerging business scenarios will enable data and analytics leaders to deliver greater business value.
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IBM
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