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data model

Results 151 - 175 of 373Sort Results By: Published Date | Title | Company Name
Published By: Group M_IBM Q3'19     Published Date: Jul 01, 2019
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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Group M_IBM Q3'19
Published By: Group M_IBM Q3'19     Published Date: Sep 04, 2019
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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Group M_IBM Q3'19
Published By: Group M_IBM Q4'19     Published Date: Sep 27, 2019
Data quality tools are vital for digital business transformation, especially now that many have emerging features like automation, machine learning, business-centric workflows and cloud deployment models. This Magic Quadrant assesses 15 vendors to help you make the best choice for your organization.
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Group M_IBM Q4'19
Published By: Visier     Published Date: Jan 25, 2019
Complementing your investment in Workday, Visier People takes you beyond Workday’s operational reports to strategic excellence. How? Through actionable and proven people analytics that are available today—not someday. Chosen time and again by Global 2000 organizations, Visier’s dedicated people analytics and workforce planning solution, with its all-inclusive subscription model, brings together data from all your transactional HR and business systems— including Workday—so you can: • Answer strategic workforce questions on demand, with credibility • Connect the dots between workforce decisions and business outcomes • Model and forecast your future workforce and its costs
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Visier
Published By: Domino Data Lab     Published Date: Feb 08, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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Domino Data Lab
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: Domino Data Lab     Published Date: Feb 08, 2019
As organizations increasingly strive to become model-driven, they recognize the necessity of a data science platform. According to a recent survey report “Key Factors on the Journey to Become Model-Driven”, 86% of model-driven companies differentiate themselves by using a data science platform. And yet the question of whether to build or buy still remains. This paper presents a framework to facilitate the decision process, and considers the four-year projection of total costs for both approaches in a sample scenario. Read this whitepaper to understand three major factors in your decision process: Total cost of ownership - Internal build costs often run into the tens of millions Opportunity costs - Distraction from your core competency Risk factors - Missed deadlines and delayed time to market
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Domino Data Lab
Published By: Domino Data Lab     Published Date: May 23, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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Domino Data Lab
Published By: Domino Data Lab     Published Date: May 23, 2019
Lessons from the field on managing data science projects and portfolios The ability to manage, scale, and accelerate an entire data science discipline increasingly separates successful organizations from those falling victim to hype and disillusionment. Data science managers have the most important and least understood job of the 21st century. This paper demystifies and elevates the current state of data science management. It identifies best practices to address common struggles around stakeholder alignment, the pace of model delivery, and the measurement of impact. There are seven chapters and 25 pages of insights based on 4+ years of working with leaders in data science such as Allstate, Bayer, and Moody’s Analytics: Chapters: Introduction: Where we are today and where we came from Goals: What are the measures of a high-performing data science organization? Challenges: The symptoms leading to the dark art myth of data science Diagnosis: The true root-causes behind the dark art m
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Domino Data Lab
Published By: Domino Data Lab     Published Date: May 23, 2019
This paper introduces the practice of Model Management, an organizational capability to develop and deliver models that create a competitive advantage. Today, the best-run companies run their business on models, and those that don’t face existential threat. The paper explains why companies that fail to run on models are falling for the Model Myth—the assumption that models can be managed like software or data. Models are different and need a new organizational capability: Model Management. What’s inside: Defining a model Why models matter for businesses Why companies fall for the Model Myth A framework for Model Management Practical steps to get started The paper is intended for anyone in a data science organization, or anyone who hopes to use data science as a key source of competitive advantage for their business.
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Domino Data Lab
Published By: Virgin Media Business     Published Date: Aug 28, 2019
The world is now digital. From the explosive expansion in data-driven service delivery to digitally disruptive business models such as Uber and Netflix that have fundamentally changed the way we consume products, the digital evolution is unavoidable. As digital continues to advance, it’s crucial that UK businesses can be confident in their ability to keep up to date with the latest trends and technologies. But enhancing existing tools and models is just the beginning. Digital transformation is about taking advantage of new innovations that completely change the way businesses work, the experiences they offer their customers and the value they deliver within their market. To find out more download this whitepaper today.
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Virgin Media Business
Published By: MarkLogic     Published Date: Nov 07, 2017
Business demands a single view of data, and IT strains to cobble together data from multiple data stores to present that view. Multi-model databases, however, can help you integrate data from multiple sources and formats in a simplified way. This eBook explains how organizations use multi-model databases to reduce complexity, save money, lessen risk, and shorten time to value, and includes practical examples. Read this eBook to discover how to: Get unified views across disparate data models and formats within a single database Learn how multi-model databases leverage the inherent structure of data being stored Load as is and harmonize unstructured and semi-structured data Provide agility in data access and delivery through APIs, interfaces, and indexes Learn how to scale a multi-model database, and provide ACID capabilities and security Examine how a multi-model database would fit into your existing architecture
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MarkLogic
Published By: MarkLogic     Published Date: Nov 07, 2017
NoSQL means a release from the constraints imposed on database management systems by the relational database model. This quick, concise eBook provides an overview of NoSQL technology, when you should consider using a NoSQL database over a relational one (and when to use both). In addition, this book introduces Enterprise NoSQL and shows how it differs from other NoSQL systems. You’ll also learn the NoSQL lingo, which customers are already using it and why, and tips to find the right NoSQL database for you.
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MarkLogic
Published By: MarkLogic     Published Date: Nov 07, 2017
This eBook explains how databases that incorporate semantic technology make it possible to solve big data challenges that traditional databases aren’t equipped to solve. Semantics is a way to model data that focuses on relationships, adding contextual meaning around the data so it can be better understood, searched, and shared. Read this eBook, discover the 5 steps to getting smart about semantics, and learn how by using semantics, leading organizations are integrating disparate heterogeneous data faster and easier and building smarter applications with richer analytic capabilities.
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MarkLogic
Published By: Datarobot     Published Date: May 14, 2018
The DataRobot automated machine learning platform captures the knowledge, experience, and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods
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Datarobot
Published By: SAS     Published Date: May 24, 2018
This paper provides an introduction to deep learning, its applications and how SAS supports the creation of deep learning models. It is geared toward a data scientist and includes a step-by-step overview of how to build a deep learning model using deep learning methods developed by SAS. You’ll then be ready to experiment with these methods in SAS Visual Data Mining and Machine Learning. See page 12 for more information on how to access a free software trial. Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is used strategically in many industries.
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SAS
Published By: MicroStrategy     Published Date: Aug 28, 2019
The Constellation AstroChartTM supplies a visual guide of the trends impacting Data to Decisions. After assessing boardroom priorities, organizations should employ AstroCharts to inform portfolio management. This report contains two AstroCharts: one identifying Business Trends and one identifying Technology Trends. The AstroCharts’ vertical axes plot “Organizational Adoption” rates from Mainstream to Early Adopter to Bleeding Edge. Horizontal axes plot “Business Impact,” the impact of the trend on an organization’s business model, from Incremental to Transformational to Exponential. The Constellation AstroChart moves beyond both the hype and constraints of the standard 2 x 2 grid to identify the dynamics impacting the entire market. This report applies Constellation’s AstroChart to the Constellation’s business theme of Data to Decisions. This research domain examines the enablement of data-driven decisions across organizations. Holistic, data-informed decisions require a multidisciplin
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MicroStrategy
Published By: Epicor     Published Date: Aug 18, 2017
More than ever, businesses are considering a cloud solution for their enterprise resource planning (ERP) deployment over an on-premises system. Cloud technology appeals to these companies because updates and fixes occur automatically with little or no effort from internal IT staff, and because cloud-based solutions provide access to real-time data from anywhere. Employees want tools that make it easier for them to complete everyday tasks and make informed decisions that help the business grow. Aberdeen’s research report, “Top Performers Know It’s Time to Migrate to Cloud ERP: Here’s Why and How,” uncovers the reasons successful companies are choosing cloud over on-premises ERP models. Download this SmartBite for a quick look at the report’s highlights.
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erp software, enterprise resource planning software, saas, cloud erp, epicor erp
    
Epicor
Published By: Epicor     Published Date: Aug 15, 2018
More than ever, businesses are considering a cloud solution for their enterprise resource planning (ERP) deployment over an on-premises system. Cloud technology appeals to these companies because updates and fixes occur automatically with little or no effort from internal IT staff, and because cloud-based solutions provide access to real-time data from anywhere. Employees want tools that make it easier for them to complete everyday tasks and make informed decisions that help the business grow. Aberdeen’s research report, “Top Performers Know It’s Time to Migrate to Cloud ERP: Here’s Why and How,” uncovers the reasons successful companies are choosing cloud over on-premises ERP models. Download this report from Aberdeen Group and discover the compelling reasons more companies are choosing the cloud for their ERP platform.
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erp software, enterprise resource planning software, saas, cloud erp, epicor erp
    
Epicor
Published By: ADP     Published Date: Nov 16, 2017
Many businesses invest in analytics technology thinking it’s a silver bullet. But data doesn’t always tell the whole story. You get percentages but not insights. Trends but not necessarily relationships or patterns. Truly impactful workforce analytics has to do more. You have to turn data into insight, and then put it into action. This workbook is designed to help you progress through the workforce analytics maturity model. The first step in the process is to identify where you stand today.
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ADP
Published By: MarkLogic     Published Date: Mar 29, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
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enterprise, metadata, management, organizations, technology, tools, mark logic
    
MarkLogic
Published By: MarkLogic     Published Date: May 07, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
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agile, enterprise, metadata, management, organization
    
MarkLogic
Published By: NetApp     Published Date: Mar 05, 2018
Access to comprehensive, up-to-date information about your infrastructure is critical to meeting the challenges of a service-led IT organization. You need visibility into your entire IT infrastructure, including both multi-vendor and multi-cloud environments, so you can make data-driven decisions and improve IT services. Discover how you can manage, monitor, and report on IT services across your entire data infrastructure so you can drive intelligent business decisions and reduce costs.
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netapp, database performance, flash storage, data management, cost challenges
    
NetApp
Published By: Google Analytics 360 Suite     Published Date: Jul 27, 2017
Today’s marketing leaders need sophisticated tools to turn data into cross-channel insights that improve performance. In a new report, Gartner compares 11 digital marketing analytics solutions across five key areas: data integration, exploration, advanced models, platform integrations, and measurement. Selecting the best solution for your team requires thoughtful analysis. How will you determine the best fit? Gartner’s Magic Quadrant can help.
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Google Analytics 360 Suite
Published By: Juniper Networks     Published Date: Aug 08, 2017
Cloud, social, big data, and the Internet of Things (IoT) are increasingly central to business decisions as the pace of digitization accelerates. The impact of software-defined networking (SDN), virtualization, and converged and hyperconverged infrastructure within the datacenter is substantial. These technologies add complexity but offer enticing opportunities for new business models, revenue streams, operating efficiencies, and agility that organizations must pursue if they want to remain competitive and viable. This pursuit requires businesses to keep up with current and emerging technologies and applications and transform the ways in which they conduct business. At the core of "keeping up" is an organization's datacenter strategy — with an associated technology and services strategy that will either create industry laggards or accelerate innovators.
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Juniper Networks
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