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data support

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Published By: SAP     Published Date: Jul 18, 2016
A long-time user of SAP software, SAP for Retail solutions are the IT backbone of ULTA's operations. From real-time data to analytics, to automated back-end processes, to comprehensive employee support. ULTA is using IT to drive better, more personalized service and consistent shopping experiences. Learn how SAP helps ULTA Beauty Run Simple.
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SAP
Published By: IBM     Published Date: Nov 30, 2016
You’ve taken the first step and already know that a document- oriented database is the right database for your application. From here, you still have to decide where and how you’ll deploy the software and its associated infrastructure. These decisions lead to additional considerations around administrative overhead, technical support, open-source options, data sovereignty and security, and more. This paper aims to outline the deployment options available when you select IBM® Cloudant® as your JSON store.
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ibm, cloud, cloudant managed service, cloudant local, apache couchdb, enterprise applications, cloud computing
    
IBM
Published By: OpenText     Published Date: Jun 28, 2019
Integration Technologies Should Reduce The Burden of Data Integration and Management Digital transformation has multiplied the number of packaged applications and the interfaces that support them. It also requires a greater agility from businesses to follow growing customer demands for value, innovation, and new and improved digital interactions. This means that interfaces must constantly evolve to support the continuous integration and continuous delivery (CI/CD) of systems of engagement. These applications are under pressure from customer experience, employee experience, and the required operational excellence of automation systems.
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OpenText
Published By: IBM     Published Date: Jan 18, 2017
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. But data scientists can only go so far without support.
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ibm, analytics, aps data, open data science, data science, data engineers, enterprise applications
    
IBM
Published By: IBM     Published Date: Jan 18, 2017
You’ve taken the first step and already know that a document- oriented database is the right database for your application. From here, you still have to decide where and how you’ll deploy the software and its associated infrastructure. These decisions lead to additional considerations around administrative overhead, technical support, open-source options, data sovereignty and security, and more. This paper aims to outline the deployment options available when you select IBM® Cloudant® as your JSON store.
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ibm, cloud, analytics, cloudant managed service, cloudant local, apache couchdb, databases, enterprise applications
    
IBM
Published By: IBM     Published Date: May 22, 2017
An investment firm is only as good as its data and its ability to leverage that data to make smart trades. A firm’s ability to differentiate itself in meeting market needs and bringing new products to market all hinge on its ability to squeeze maximum value out of all available data. But when it comes to implementing the right technology and processes to support a data-reliant business, those three factors present tremendous challenge
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cloud optimization, cloud efficiency, cloud management, cloud assurance, cloud visibility, enterprise management, data management
    
IBM
Published By: IBM     Published Date: May 23, 2017
This paper gives key considerations when making a strategic commitment to a database platform.
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security, operation management, cloud architectures, scale management, data support, ibm db2, cloud management
    
IBM
Published By: IBM     Published Date: Aug 23, 2017
Banks today are continuously challenged to meet rigorous regulatory requirements. They must implement strict governance programs that enable them to comply with a wide variety of regulations stemming from the financial crisis that began in 2007, including the DoddFrank Act, Basel Committee on Banking Supervision regulations, the General Data Protection Regulation (GDPR), the Revised Payment Services Directive (PSD2) and the revised Markets in Financial Instruments Directive To keep pace with regulatory changes, many banks will need to reapportion their budgets to support the development of new systems and processes. Regulators continually indicate that the banks must be able to provide, secure and deliver high-quality information that is consistent and mature.
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risk mitigation, data aggregation, risk reporting, banking
    
IBM
Published By: IBM     Published Date: Nov 08, 2017
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, ibm, deployment models
    
IBM
Published By: DataStax     Published Date: Nov 02, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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DataStax
Published By: Oracle     Published Date: Feb 20, 2019
Practice Supply Chain Best-in-Class What does it take to thrive in a digital world? A strong, strategic relationship with customers; data that supports confident decision-making; and an end-to-end SCM and ERP platform that brings it all together. See how industry leaders do it.
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Oracle
Published By: Datastax     Published Date: Aug 23, 2017
Part of the “new normal” where data and cloud applications are concerned is the ability to handle multiple types of data models that exist in the application and persist each in a single datastore. This data management capability is called a “multi-model” database. Chances are you are getting bogged down by various data models that require support — key-value, tabular, JSON/document and graph. This not only raises your operational expenses, but also slows down your time to market and ultimately revenue growth. Download this free white paper and explore the multi-model concept, its rationale, and how DataStax Enterprise (DSE) is the only database that can help accelerate building and powering distributed, responsive and intelligent cloud applications across multiple data models.
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cloud, data model, multi-model
    
Datastax
Published By: Datastax     Published Date: May 14, 2018
"What’s In The Report? The Forrester Wave: Translytical Data Platforms, Q4 2017 report reviews companies that offer “translytical data platforms”, which Forrester defines as an “emerging technology” that delivers “faster access to business data to support various workloads and use cases.” Download the report if you want to learn: -Why Forrester named DataStax a leader among 12 companies it identifies as “the most significant translytical vendors. -What constitutes a “translytical data platform” and why translytical data platforms are so critical for enterprise data strategies today. Why performance, scale, security, and use-case support are the key differentiators."
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Datastax
Published By: Datastax     Published Date: Nov 02, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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Datastax
Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18
Published By: OracleSMB     Published Date: Jan 04, 2018
Customer loyalty matters. Loyal customers spend more, advocate more, and are the first to try new products or services. But how do you build a loyal customer base that drives profitable sales? The answer is a loyalty program that provides the data needed to support individualized experiences and show appreciation. And this is where technology can help. Move beyond paper punch cards and emails with personalized greetings. Quickly create an effective loyalty program that keeps your customers coming back for more.
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OracleSMB
Published By: Cohesity     Published Date: Nov 20, 2018
As one of the nation’s largest and most sophisticated controlled-temperature food distribution companies, Burris Logistics offers over 60 million cubic feet of freezer warehousing space in 15 strategic locations along the East Coast. Burris Logistics IT Team manages two data centers, one primary and another DR site that supports multiple remote locations up and down the East Coast. Download this case study to see how Burris Logistics acheived these benefits: - Over 80% reduction in backup windows - 25% CapEx savings and ongoing OpEx savings - Simplified operations with an easy-to-use and manage UI
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Cohesity
Published By: Group M_IBM Q119     Published Date: Feb 21, 2019
Mobile marketing platforms facilitate direct marketing on mobile devices by enabling mobile campaign management and activation. These platforms can operate as stand-alone solutions; however, they typically integrate with, or operate alongside, CRM, location/data management, and multichannel marketing hub (MMH) or email marketing platforms. Regardless of how a mobile marketing platform gets deployed, the native or third-party analytics supporting its audience targeting, campaign sequencing, personalization and performance measurement capabilities form the foundation of this technology.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
The life of an enterprise architect is becoming busy and difficult. Before the era of big data, the enterprise architect “only” had to worry about the data and systems within their own data center. However, over the past decade there were revolutionary changes to the way information is used by businesses and how data management platforms support the information available from modern data sources.
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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
IBM Cloud Private for Data is an integrated data science, data engineering and app building platform built on top of IBM Cloud Private (ICP). The latter is intended to a) provide all the benefits of cloud computing but inside your firewall and b) provide a stepping-stone, should you want one, to broader (public) cloud deployments. Further, ICP has a micro-services architecture, which has additional benefits, which we will discuss. Going beyond this, ICP for Data itself is intended to provide an environment that will make it easier to implement datadriven processes and operations and, more particularly, to support both the development of AI and machine learning capabilities, and their deployment. This last point is important because there can easily be a disconnect Executive summary between data scientists (who often work for business departments) and the people (usually IT) who need to operationalise the work of those data scientists
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Group M_IBM Q2'19
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: 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: Amazon Web Services     Published Date: Feb 01, 2018
Moving Beyond Traditional Decision Support Future-proofing a business has never been more challenging. Customer preferences turn on a dime, and their expectations for service and support continue to rise. At the same time, the data lifeblood that flows through a typical organization is more vast, diverse, and complex than ever before. More companies today are looking to expand beyond traditional means of decision support, and are exploring how AI can help them find and manage the “unknown unknowns” in our fast-paced business environment.
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predictive, analytics, data lake, infrastructure, natural language processing, amazon
    
Amazon Web Services
Published By: Commvault ABM Oct     Published Date: Jul 17, 2019
Your goal is high availability for the applications, databases, virtual machines (VMs), servers, and data that run your business. When access is lost or interrupted, recovery speed is critical, and must be measured in minutes and seconds, not hours of days. And if your backup and recovery strategy includes point solutions with limited coverage, legacy approaches that don't support today's modern technologies, or manual proc cesses that are time-consuming and complex, you may not be ready when disaster strikes.
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Commvault ABM Oct
Published By: Oracle     Published Date: Mar 22, 2019
Our research reports on the 2,000 executives from the marketing community, across 22 markets, who agreed that innovation drives growth. However, barriers to innovation remain significant, ranging from isolation within the business, to a lack of insight into the data, and absent support from leadership in 24% of cases, and data remains the second largest issue for marketing with a 10% gap. New technologies help harness data, demonstrate how innovation will benefit teams and businesses, and help CMOs create a successful innovation agenda. Read the report
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Oracle
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