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data driven enterprise

Results 1 - 25 of 34Sort Results By: Published Date | Title | Company Name
Published By: MicroStrategy     Published Date: Aug 21, 2019
Ready or not, the future is here. For enterprise organizations, it must be a data-driven one. Whoever can use technology to transform the customer experience, and be the first to discover and deliver on new business models, will be the disruptor. Those who can’t, the disrupted in this period known as the “era of Digital Darwinism.” The future belongs to the Intelligent Enterprise which anticipates constantly evolving regulatory, technological, market, and competitive challenges and turns them into opportunity and profit. It delivers a single version of the truth and agility. It connects to any data and distributes reports to thousands. The Intelligent Enterprise goes beyond business intelligence, delivering transformative insight to every user, constituent and partner. Are most organizations there yet? As brands hone and focus their 2020 (and even 2030) vision, MicroStrategy has surveyed 500 enterprise analytics professionals on the state of their organization’s analytics initiatives.
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MicroStrategy
Published By: Simba by Magnitude     Published Date: Jul 12, 2019
Today’s C-level executives expect data and analytics to provide them with speed and agility to deliver competitive advantage and to disrupt new markets. But, in today’s complex data environment exists a near paradox between these expectations, that companies will be able to rapidly deliver value using data and analytics--and the complexities of the data landscape, making it more difficult to find, govern, connect to and access the data needed to deliver that value. Once thing is clear: if management expectation is to be met, simplifying connectivity is a must.In this white paper, veteran analyst Mike Ferguson, Managing Director of Intelligent Business Strategies explores how simplifying data access –connectivity –aligns expectations with data realities thus decreasing time to value.
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Simba by Magnitude
Published By: Datastax     Published Date: May 20, 2019
DataStax Enterprise and Apache Kafka are designed specifically to fit the needs of modern, next-generation businesses. With DataStax Enterprise (DSE) providing the blazing fast, highly-available hybrid cloud data layer and Apache Kafka™ detangling the web of complex architectures via its distributed streaming attributes, these two form a perfect match for event-driven enterprise architectures.
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Datastax
Published By: MicroStrategy     Published Date: Apr 11, 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: Pure Storage     Published Date: Apr 10, 2019
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: 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: 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: 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: 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: 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: 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: Apr 18, 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. Amazon CEO Jeff Bezos spoke about the potential of artificial intelligence and machine learning at the 2017 Internet Association‘s annual gala in Washington, D.C., “It is a renaissance, it is a golden age,” Bezos said. “We are solving problems with machine learning and artificial intelligence that were in the realm of science fiction for the last several decades. Natural language understanding, machine vision problems, it really is an amazing renaissance.” Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy
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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: SAS     Published Date: Mar 06, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS
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: 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: 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: 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: 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: Glint     Published Date: Sep 26, 2017
This white paper explores the data-driven strategies of innovative organizations who leverage employee engagement as a strategic weapon to improve productivity, retention, and corporate performance. It looks at different ways of measuring engagement, how common challenges in building engagement programs have been overcome by well-known enterprises, and offers real-life examples from HR executives.
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employee engagement, hr metrics, employee engagement survey, employee engagement strategies, employee engagement strategy
    
Glint
Published By: Lookout     Published Date: Aug 28, 2017
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.
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data protection, mobile risks, productivity tools, cyber attacks, device vulnerabilities
    
Lookout
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: 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: Workday     Published Date: Jun 05, 2017
In today’s mobile, connected and data-driven world, legacy financial management systems are failing to meet the needs of the modern enterprise.
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workday, finance management, mobile employees, mobile workers, mobile data, cloud finance, cloud hr, hr technology, finance technology
    
Workday
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