data driven enterprise

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Published By: DATAVERSITY     Published Date: Jun 14, 2013
This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. “Data Governance” is a term fraught with nuance, misunderstanding, myriad opinions, and fear. It is often enough to keep Data Stewards and senior executives awake late into the night. The modern enterprise needs reliable and sustainable control over its technological systems, business processes, and data assets. Such control is tantamount to competitive success in an ever-changing marketplace driven by the exponential growth of data, mobile computing, social networking, the need for real-time analytics and reporting mechanisms, and increasing regulatory compliance requirements. Data Governance can enhance and buttress (or resuscitate, if needed) the strategic and tactical business drivers every enterprise needs for market success. This paper is sponsored by: ASG, DGPO and DebTech International.
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data, data management, data governance, data steward, dataversity, research paper
    
DATAVERSITY
Published By: Semarchy     Published Date: Feb 12, 2018
This whitepaper covers a recently completed a ground-breaking industry wide survey of executives, architects, and business stakeholders from data-driven organizations by Enterprise Management Research in order to explore the growing role of the CDO, and to explore the various data management maturity levels of enterprise companies. This whitepaper explains how industry visionaries use data as an asset, and discusses the growing importance of data governance leadership. Additionally, it creates a data management maturity index to show how various companies match-up in their data management vision and capabilities. Finally, the whitepaper covers the top data-focused applications used and their average implementation timelines.
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Semarchy
Published By: DATAVERSITY     Published Date: Jun 17, 2013
This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. “Data Governance” is a term fraught with nuance, misunderstanding, myriad opinions, and fear. It is often enough to keep Data Stewards and senior executives awake late into the night. The modern enterprise needs reliable and sustainable control over its technological systems, business processes, and data assets. Such control is tantamount to competitive success in an ever-changing marketplace driven by the exponential growth of data, mobile computing, social networking, the need for real-time analytics and reporting mechanisms, and increasing regulatory compliance requirements. Data Governance can enhance and buttress (or resuscitate,if needed) the strategic and tactical business drivers every enterprise needs for market success.
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research paper, data, data management, data governance, data steward
    
DATAVERSITY
Published By: Sage EMEA     Published Date: Jan 29, 2019
Transform your finance operations into a strategic, data-driven engine Data inundation and information overload have burdened practically every largescale enterprise today, providing great amounts of detail but often very little context on which executives can act. According to the Harvard Business Review,1 less than half of an organisation’s structured data is actively used in making decisions. The burden is felt profoundly among finance executives, who increasingly require fast and easy access to real-time data in order to make smart, timely, strategic decisions. In fact, 80% of analysts’ time is spent simply discovering and preparing data, and the average CFO receives information too late to make decisions 24% of the time.2
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Sage EMEA
Published By: Domino Data Lab     Published Date: Feb 08, 2019
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform. This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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Domino Data Lab
Published By: Oracle     Published Date: Nov 28, 2017
Today’s leading-edge organizations differentiate themselves through analytics to further their competitive advantage by extracting value from all their data sources. Other companies are looking to become data-driven through the modernization of their data management deployments. These strategies do include challenges, such as the management of large growing volumes of data. Today’s digital world is already creating data at an explosive rate, and the next wave is on the horizon, driven by the emergence of IoT data sources. The physical data warehouses of the past were great for collecting data from across the enterprise for analysis, but the storage and compute resources needed to support them are not able to keep pace with the explosive growth. In addition, the manual cumbersome task of patch, update, upgrade poses risks to data due to human errors. To reduce risks, costs, complexity, and time to value, many organizations are taking their data warehouses to the cloud. Whether hosted lo
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Oracle
Published By: Cisco EMEA     Published Date: Nov 08, 2018
Digital transformation (DX) — a technology-driven business strategy — enables firms to gain or expand their competitive differentiation by embracing data-driven decision-making processes, whether for increasing operational efficiencies, developing new products and services, increasing customer satisfaction and retention, or getting a better intelligence on the market. Big Data and analytics (BDA) applications form the foundation for enterprisewide digital transformation initiatives. To find out more download this whitepaper today.
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Cisco EMEA
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 i
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Oracle
Published By: IBM APAC     Published Date: Jul 09, 2017
Organizations today collect a tremendous amount of data and are bolstering their analytics capabilities to generate new, data-driven insights from this expanding resource. To make the most of growing data volumes, they need to provide rapid access to data across the enterprise. At the same time, they need efficient and workable ways to store and manage data over the long term. A governed data lake approach offers an opportunity to manage these challenges. Download this white paper to find out more.
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data lake, big data, analytics
    
IBM APAC
Published By: Oracle ODA     Published Date: Dec 06, 2016
IT leaders must consider new approaches to database administration, implementation, and security, including proactive management, to reduce time-consuming administrative tasks. This, in turn, will free them to develop strategies for capitalizing on the new data-driven initiatives that can help transform the enterprise. Learn more about these opportunities as well as how products, such as an integrated Database Appliance, can streamline the management of these tasks, while reducing costs and time.
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Oracle ODA
Published By: Oracle ODA     Published Date: Dec 06, 2016
It’s no secret that data-driven business strategies have resulted in unprecedented data growth and management of more complex database structures. Not only that, but IT leadership continues to play a strategic role in the direction of the business, especially as these disruptive business strategies come into play. This white paper addresses some of the key considerations IT management should have as they evaluate their IT roadmap and infrastructure to meet these dynamic enterprise needs.
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Oracle ODA
Published By: Dun & Bradstreet     Published Date: Apr 21, 2016
When the right approach is applied, analytics can drive more effective marketing strategies. While marketers understand the role analytics plays within the organization, most are not leveraging analytics to really drive enterprise performance. We surveyed 100+ business leaders to understand the state of analytics maturity across today’s leading organizations, uncovering common challenges teams are facing in their quest to use data and analytics to deliver a competitive advantage. What We Uncovered: - 73% of analytic professionals claim to work for an analytically-driven company - Only 42% of companies have a strategy for using analytics across the enterprise - Just 38% of companies share results of their analytic insights outside their department - 81% of organizations rely on 3rd parties for at least some portion of their analysis Download the report to learn how marketers, like yourself, view themselves in light of using analytics to drive their business.
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hoover's, marketing strategies, analytics, data, analytical applications, business intelligence
    
Dun & Bradstreet
Published By: IBM     Published Date: May 26, 2017
Securing your infrastructure, your customer interactions and protecting your data are critical to preserving your reputation and your bottom line. Many cyber attacks remain undetected for up to eight months1 and can cost an organization an average of 11 million USD.2 Today’s cyber actors are becoming more sophisticated, agile and capable of getting past any network security. Organizations must evolve, replacing traditional defensive security strategies with a proactive, intelligence-driven offense to prevent and disrupt these threats. IBM® i2® Enterprise Insight Analysis is a next generation intelligence solution that enables organizations to incorporate cyber threat hunting into their security strategy and turn their defense into a proactive offense.It helps organizations uncover critical insights about their threats and threat actors so they can mitigate and counter more threats with a combination of multi-dimensional visualte analysis capabilities
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ibm, ibm i2, cyber security, customer interaction, enterprise insight analysis
    
IBM
Published By: Lookout     Published Date: Dec 13, 2018
The world has changed. Yesterday everyone had a managed PC for work and all enterprise data was behind a firewall. Today, mobile devices are the control panel for our personal and professional lives. This change has contributed to the single largest technology-driven lifestyle change of the last 10 years. As productivity tools, mobile devices now access significantly more data than in years past. This has made mobile the new frontier for a wide spectrum of risk that includes cyber attacks, a range of malware families, non-compliant apps that leak data, and vulnerabilities in device operating systems or apps. A secure digital business ecosystem demands technologies that enable organizations to continuously monitor for threats and provide enterprise-wide visibility into threat intelligence. Watch the webinar to learn more about: What makes up the full spectrum of mobile risks Lookout's Mobile Risk Matrix covering the key components of risk How to evolve beyond mobile device management
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Lookout
Published By: Carbonite     Published Date: Oct 10, 2018
Overview Key Challenges Organizations still struggle with communication between data owners and those responsible for administering DLP systems, leading to technology-driven — rather than business-driven — implementations. Many clients who deploy enterprise DLP systems struggle to get out of the initial phases of discovering and monitoring data flows, never realizing the potential benefits of deeper data analytics or applying appropriate data protections. DLP as a technology has a reputation of being a high-maintenance control — incomplete deployments are common, tuning is a never-ending process, lack of organization buy-in is low, and calculations of ROI are complex.
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Carbonite
Published By: Pure Storage     Published Date: Oct 09, 2018
Massive amounts of data are being created driven by billions of sensors all around us such as cameras, smart phones, cars as well as the large amounts of data across enterprises, education systems and organizations. In the age of big data, artificial intelligence (AI), machine learning and deep learning deliver unprecedented insights in the massive amounts of data.
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Pure Storage
Published By: Pure Storage     Published Date: Mar 15, 2018
Managing technology refreshes is not a popular task among enterprise storage administrators, although it is a necessary task for successful businesses. As a business evolves, managing more data and adding new applications in the process, enterprise storage infrastructure inevitably needs to grow in performance and capacity. Enterprise storage solutions have traditionally imposed limitations in terms of their ability to easily accommodate technology refreshes that keep infrastructure current and operating reliably and most cost effectively. In 2015, Pure Storage introduced a new technology refresh model that has driven strong change in the enterprise storage industry by addressing the major pain points of legacy models and provided overall a much more cost-effective life-cycle management approach. In conjunction with other aspects of Pure Storage's enterprise storage product and services offerings, the company's "Evergreen Storage" technology refresh model has contributed to this all-f
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Pure Storage
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
Tags : 
    
Oracle
Published By: IBM     Published Date: Nov 03, 2017
Massive shifts within the digital business landscape are sparking immense opportunities and reshaping every sector. In some cases, complete upheaval is happening at lightning-fast speed. In other instances, digital undercurrents are stirring beneath the surface as organizations scramble to monetize vast volumes and variety of data in an effort to sharpen their competitive edge and not be blindsided by unforeseen events that completely upend existing business models. While long-standing industry leadership might be no match for the next cool app, agility, speed and the ability to harness more data than was ever imagined is fueling powerful possibilities for reinvention among companies of every size. Data is following rapidly from mobile devices and social networks, as well as from every connected product, machine and infrastructure. This data holds the potential for deep insights that can replace guesswork and approximations as to locations, behaviors, patterns and preferences. As the w
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digital business, data, data-driven enterprise, innovation, ibm
    
IBM
Published By: Datastax     Published Date: Aug 23, 2017
Kenzan, a software engineering firm that specializes in building scalable, data-driven solutions, often leverages DataStax Enterprise (DSE) for customers that have high expectations for both performance and value. That means making careful, informed choices when designing the cloud infrastructure that applications run on. Controlling costs is always a priority for businesses that rely on AWS for running DSE. With AWS, it can be tempting to solve performance issues simply by adding more resources until the problem goes away. However, this “solution” results in increased costs which quickly add up over time. In this white paper, Kenzan and DataStax identify some of the key factors to consider when building out infrastructure on AWS, outlining practical steps you can take to optimize performance with DSE on AWS while keeping costs in check.
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enterprise, benchmarking, data
    
Datastax
Published By: MicroStrategy     Published Date: Jan 23, 2019
By 2025, the total amount of data produced will grow to 175 zettabytes, according to IDC’s Data Age 2025 Report. How will enterprises evolve over the next year to manage and make the most of this unprecedented growth? Download 10 Enterprise Analytics Trends to Watch in 2019 to learn how leading organizations will win with embedded and augmented analytics, HyperIntelligence, collaboration, enterprise AI strategies, and more. This eBook details the transformational technologies data-driven organizations will need to leverage to get and stay ahead in 2019 and beyond, with contributions from thought leaders, including: Mike Gualtieri of Forrester Research Ray Wang and Doug Henschen of Constellation Research Mark Smith and David Menninger of Ventana Research Chandana Gopal of IDC Ronald van Loon, and more. Is your organization ready for the data-driven future? Read 10 Enterprise Analytics Trends to Watch in 2019 today to find out.
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MicroStrategy
Published By: Juniper Networks     Published Date: Feb 05, 2018
Innovative data-driven strategies are enabling organizations to connect with customers and increase operational efficiency as never before. These new initiatives are built on a multitude of applications, such as big-data analytics, supply chain, and factory automation. On average, organizations are now 53% digital as they create new ways of operating and growing their businesses, according to the Computerworld 2017 Forecast Study. As part of this transformation, enterprises rely increasingly on multivendor, multicloud environments that mix on-premise, private, and public cloud services and workloads. This shift is causing enterprises to increase network capacity; 55% of enterprises in the Computerworld study expect to add network bandwidth in the next 12 months.
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security, automation, savings, technology, cloud
    
Juniper Networks
Published By: Oracle     Published Date: Apr 22, 2015
This white paper distills six rules that modern CFOs worldwide are adopting to attract, retain and nurture finance talent with the skills needed to put finance at the heart of data driven business decision making across the enterprise.
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digital, finance, modern finance, finance departments, attract finance talent, big data, cfos, modern business challenges, technology, data integration, business technology
    
Oracle
Published By: MuleSoft     Published Date: Oct 13, 2015
Improving data management is critical for streamlining regulatory reporting, monetizing customer analytics, and democratizing data-driven decision-making. Financial firms must transform their data management approach to unlock data from its silos, incorporate unstructured information from non-traditional sources, and integrate information whether on-premises, in the cloud, batch-based, or real-time. As this evolution continues, technology leaders must consider integration holistically across the enterprise and create an agility layer that is key to future-proofing their data management architecture. Read this whitepaper to learn: -Data management challenges in financial services -Best practices to manage data in financial institutions -Integration strategies for optimal data management
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back office systems, operational effectiveness, unlocking data, business imperatives, modernizing legacy systems, enterprise applications
    
MuleSoft
Published By: NetApp     Published Date: Mar 11, 2015
To enable the data-driven enterprise, IT organizations are moving rapidly to a services-based cloud model. This white paper highlights the challenges of controlling data in hybrid as-a-service IT environments and suggests that data control in hybrid IT should be looked at as a framework rather than as a single effort.
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NetApp
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