big data governance

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Published By: Cambridge Semantics     Published Date: Mar 13, 2015
As the quantity and diversity of relevant data grows within and outside the enterprise, how can IT easily deploy secure governed solutions that allow business users to identify, extract, link together and derive value from the right data at the right time, at big data scale, while keeping up with ever changing business needs? Smart Enterprise Data Management (Smart EDM) is new, sensible paradigm for managing enterprise data. Anzo Smart Data solutions allow IT departments and their business users to quickly and flexibly access all of their diverse data. Based upon graph data models and Semantic data standards, Anzo enables users to easily perform advanced data management and analytics through the lens of their business at a fraction of the time and cost of traditional approaches, while adhering to the governance and security required by enterprise IT groups. Download this whitepaper to learn more.
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enterprise data management, data governance, data integration, cambridge semantics
    
Cambridge Semantics
Published By: AnalytixDS     Published Date: Feb 28, 2015
With future business intelligence solutions clearly evolving from data that comes from highly efficient and well behaved systems, to data that comes from the extended enterprise where data is not necessarily so well structured and behaved - Organizations are forced into a more collaborative mode of operation with their core infrastructure being adapted from the consumer space, and to the extent possible, conformed to their existing repositories. This whitepaper attempts to address various challenges consumers face while managing enormous data sets within the context of this complex scenario. Further, we’ll try to answer the question: Is Big Data Governance really that different from traditional data governance initiatives? Finally, we’ll see how AnalytiX™ Mapping Manager™ can help organizations accelerate the development and deployment of a successful Big Data/ Business Intelligence platform and accelerate delivery of all sorts of data – structured, semi-structured as well as unstruc
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big data, big data governance, data governance, analytixds
    
AnalytixDS
Published By: Infogix     Published Date: May 04, 2018
Over the last few years, the term “data governance” has evolved to more prominence, concurrently with big data. While organizations understand the need for governance around big data, the implementation of a successful data governance solution continues to remain elusive as organizations grapple with what exactly data governance is. This whitepaper provides a concise definition of data governance and offers some key considerations for a successful data governance solution.
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Infogix
Published By: NTT Data APAC     Published Date: Jan 16, 2019
NTT Data services has over 600+ BI experts and over 250+ implementations in India, receiving high accolades from Gartner and AMR research. NTT Data Services offers Industry focused offerings and pre-configured models. Some of our offerings include: - Guided Analytics Strategy - Proven Framework & Methodology for ‘C’ level executives - Solution lab for Co-Innovation and Proof of Concepts - Advisory Services - BI Transformations, Big Data Strategy, Information Governance, Health Checks - Proprietary Tools & Accelerators – System Optimization, Architect to Archive, User Adoption - Flexible Delivery - Rapid Development Factory, Shared Services Find more details in this deck.
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NTT Data APAC
Published By: SAS     Published Date: Jan 17, 2018
For organizations to succeed with the onslaught of devices, sensors and tools that innovation garners, data must no longer be treated as a byproduct but instead as an asset. And data-driven innovation must start at the top. That's what Michael Schrage, Fellow at MIT Sloan School's Initiative on the Digital Economy, said during the Harvard Business Review webinar, Leadership and Big Data Innovation. Find out why data experimentation, governance and culture are part of the next leadership challenge for organizations.
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SAS
Published By: Oracle Analytics     Published Date: Oct 06, 2017
Empowered with mobile and cloud-based access to a myriad of products and services, customers now have a variety of options at their fingertips with regards to partnerships. Enterprises that do not follow the ever-changing tastes and preferences of their customers, or that wait too long to react, will fall behind and fail. Across functions, business professionals readily require big data tools and insights to understand and serve these customers. It is no longer an option for business users to rely on IT to deliver customer and other relevant analytics. On the flipside, handing the analytics reins entirely to business users can make governance nearly impossible. Organizations must find balance in a new approach in which IT mostly governs and curates data while business users are empowered to derive insights from data mostly ontheir own without delay.
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Oracle Analytics
Published 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
Published By: Hitachi Vantara     Published Date: Aug 14, 2018
In this book, we are going to look at the key trends driving the modernization of data infrastructure. We’ll see how organizations are adapting and flourishing in a data-driven world. For some time, headlines have been around the internet of things (IoT), big data and data analytics. While these developments are important, the reality is that you cannot take full advantage of them without modernization. We’re going to look at these trends and priorities in detail, then look at the three key drivers of modernization: governance, mobilization and analytics. We’ll also consider the technologies that make up modern data infrastructure including artificial intelligence (AI), flash storage, converged and hyperconverged platforms and software-defined infrastructures. By making sense of data, we make sense of the world. With more data than ever before, we have the tools to turn all that information into intelligent innovation and change the way the world works.
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data infrastructure, big data, internet of things
    
Hitachi Vantara
Published 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
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mdm, big data, automation, organization
    
IBM
Published By: Oracle     Published Date: Apr 10, 2014
The very best CFOs are becoming “technology evangelists”, investing in the latest mobile, social, analytical and cloud technologies to modernize their finance organizations and become more strategic partners to the business. Find out more – download the report, “Empowering Modern Finance: The CFO as Technology Evangelist.”
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cloud, finance, saas, big data, social, mobile, analytics, modernization, planning, forecasting, governance, technology, accounting, business intelligence, epm, erp, grc
    
Oracle
Published By: IBM     Published Date: Feb 22, 2016
Survive the big data storm by getting ahead of integration and governance functional requirements
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ibm, data, big data, integration, governance, security, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Feb 22, 2016
IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.
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ibm, data, big data, integration, governance, iig, infosphere, enterprise applications, data management, business technology
    
IBM
Published 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
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ibm, ibm connect, big data, big data zones model, data management, business technology
    
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 08, 2016
This paper from Osterman Research, explores the origins of the "information problem" many organizations are now facing and presents a detailed discussion of how to calculate your current information costs as well as how to calculate the ROI of an information governance program.
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ibm, osterman, roi, big data, data, analytics, information governance, knowledge management, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 08, 2016
Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization’s risk and enhancing security.
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ibm, forrester, data discovery, big data, data, analytics, information governance, knowledge management, data management
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
IBM commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study and examine the potential return on investment (ROI) enterprises may realize by leveraging IBM InfoSphere Information Integration and Governance (IIG) solutions.
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ibm, total economic impact, tei study, roi, return on investment, big data, data governance, governance, ibm infosphere, data management, business technology, data center
    
IBM
Published By: IBM     Published Date: Oct 13, 2016
Cloud-based data and processing services present too much opportunity for organizations to ignore. How can organizations realize the obvious financial benefits of the cloud while ensuring information culled from cloud sources is secure and trustworthy? Good hybrid information governance implies several priorities for IT and the business which are based on these foundational pillars: - Broad agreement on what information means - Clear agreement on how owned information assets will be maintained and monitored - Standard practices for securing strategic information assets - Enterprise data integration strategy
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ibm, trusted data, big data, governance, data governance, data management, business technology, 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
IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by leveraging IBM InfoSphere Information Integration and Governance (IIG) solutions.
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ibm, forrester, data, analytics, big data, ibm information integration, governance, data management, business technology, data center
    
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: 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 19, 2017
With organizations keeping larger and larger quantities of data the question will come up that given dropping storage costs, does uncontrolled data growth even matter? It does matter, and in this IBM publication featuring Gartner research you will learn about this ever growing problem and ways for information managers to address solutions.
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ibm, analytics, data, ecm, big data, information governance, enterprise applications, data management, business technology
    
IBM
Published By: MarkLogic     Published Date: May 07, 2018
Learn how Life Sciences organizations can accelerate Real World Evidence by achieving faster time to insight with a metadata-driven, semantically enriched operational platform. Real World Evidence (RWE) is today’s big data challenge in Life Sciences. Medical records, registries, consultation reports, insurance claims, pharmacy data, social media, and patient surveys all contain valuable insights that Life Sciences organizations need to ascertain and prove the safety, efficacy, and value of their drugs and medical devices. Learn how Life Sciences organizations can accelerate RWE with a metadata-driven, semantically enriched operational platform that enables them to: • Unify, harmonize and ensure governance of information from diverse data sources • Transform information into evidence that proves product efficacy and safety • Identify data patterns, connections, and relationships for faster time to insight
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data, integration, drug, device, manufacture, science
    
MarkLogic
Published By: BMC Software     Published Date: Jul 22, 2015
In this white paper, you’ll discover an enterprise approach to Big Data that leverages workload automation to: - Integrate Hadoop workflows into your enterprise processes to deliver new applications faster - Resolve issues faster with predictive analytics, automated alerts, and early problem detection - Achieve compliance and governance adherence
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big data, business processes, enterprise systems, hadoop, compliance
    
BMC Software
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