Published By: Domo APAC
Published Date: Mar 04, 2019
Cisco’s marketing team has invested massively in technology over the past two years. This
$50 million investment in software has put Cisco at the leading edge of B2B technology
marketing. 40 new applications have been put in place, creating one of the most
sophisticated marketing technology stacks in the industry, resembling the more consumeroriented Amazon, Google, and Facebook. This investment has earned Cisco’s marketing team
plaudits and several industry awards
Published By: MuleSoft
Published Date: Apr 23, 2019
In today’s competitive landscape, businesses need to make decisions quickly in order to respond to rapidly changing customer preferences and nimble competitors; whether it’s a new business strategy, a new business process, or a new market offering, businesses are competing on speed and agility.
The overwhelming majority of today’s business and IT leaders understand digital transformation is necessary to maintain leverage amid constantly changing customer preferences. They also have a clear picture of their desired end state—exemplified by leaders like Amazon, Google, and Microsoft. However, only a small minority of have a clear understanding of the path they need to lead the market.
In this paper you’ll learn:
How to build digital transformation into the root of your company with a practical, natural, and tested blueprint.
Best practices from over 1,600 enterprises to transform your strategy, organization, and technology from the ground up.
Actionable next steps to start your journ
Respond to customer feedback faster
Organizations are demanding greater flexibility from the software they use. Yet, most line-of-business applications are built on top of rigid commercial databases that make them difficult and expensive to deploy. By building applications on top of Amazon Aurora, a cost-effective database solution from Amazon Web Services (AWS), independent software vendors (ISVs) can get away from commercial databases and the hassles they create for end customers.
In this webinar, AWS Partner Network (APN) Premier Consulting Partner Onica will show you how to leverage Amazon Aurora to make it easier for customers to operate your software, in turn increasing sales. You will also learn how to leverage the breadth and depth of AWS services to improve developer productivity and become more responsive to customer feedback as a result.
With more data to analyze than ever before, companies are finding new ways to quickly find meaning in their data with artificial intelligence (AI). Natural Language Generation (NLG) technologies deployed on Amazon Web Services (AWS) can enable organizations to free their employees from manual data analysis and interpretation tasks. NLG transforms data into easy-to-understand, data-driven narratives with context and relevance.
Reduce costs and scale to meet customer expectations
Software-as-a-service (SaaS) delivery models are being adopted faster than ever to meet business and consumer demand, prompting organizations to move their customer-facing applications to the cloud. As a result, Independent Software Vendors (ISVs) need to modernize their applications to deliver the flexibility and scale customers expect, without the restrictions and high costs of running applications on commercial databases. mLogica provides deep industry expertise, backed by powerful engineering, to help ISVs quickly modernize their applications on Amazon Aurora and drive greater business value with Amazon Web Services (AWS).
Register for our webinar to learn how mLogica and AWS can help you transform your business on the cloud, so you can deliver modern, high-performing solutions to your customers.
Many business leaders know that Artificial Intelligence (AI) and Machine Learning (ML) are critical to their future but don’t know where to start. Those who do have an AI/ML strategy struggle to find qualified data scientists; and once they find them, even advanced data scientists need a lot of time—even months—to build and deploy ML models. These challenges put significant limits on the range and number of problems a business can solve.
In this webinar, learn how H2O Driverless AI on Amazon Web Services (AWS) automates the best practices of leading data scientists to create advanced machine learning models automatically. With these production-ready models, relative newcomers to AI/ML can generate reliable results and scale-up AI programs that anticipate and capitalize on trends, optimize supply chains, understand customer demand, match consumers with goods and services, and much more.
Download our webinar to learn
Implement ML successfully with minimal data science expertise.
Common daily media broadcaster tasks such as ad verification are slow and costly. Done manually, they may also introduce inefficiencies that can interfere with transparency and payment accountability—and impact your bottom line. Meanwhile, recent and archived media lies idle when you could repurpose it to increase brand exposure and generate revenue.
Learn how Veritone, Inc. used its aiWARE Operating System, building on Amazon Web Services (AWS), to help Westwood One, Inc., a large audio broadcasting network in the United States, develop Artificial Intelligence (AI) and Machine Learning (ML) solutions designed for ad verification and monetizing archived media.
Download our webinar to learn how you can
Automate ad verification and reporting tasks.
Enhance archive content to make media searchable and reusable.
Use AI and ML in the cloud for near real-time media intelligence.
Start applying machine learning tools.
The goal of this review is to educate customers on the capabilities that Cisco’s SD-WAN solution provides when working with Amazon Web Services (AWS). ESG describes Cisco’s solution and highlights the business value it can deliver to customers via its integration with AWS. ESG completed this summary as part of an AWS-commissioned report to review nine SD-WAN vendors. Readers should use this review as a starting point when investigating how they can leverage the combination of AWS and Cisco for business advantage.
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Published By: Cisco EMEA
Published Date: Mar 05, 2018
Advanced Technology is having a growing impact on our everyday lives. Adoption of advanced technologies and virtual assistants such as Amazon Echo and Google Home are becoming more mainstream in homes. Meanwhile organizations worldwide are increasingly looking at how to implement similar technologies to improve productivity, speed workflows, and increase collaboration among employees, business partners, and even customers.
To date, little is known about perceptions of technologies such as artificial intelligence (AI) and virtual assistants in the workplace and how they will impact how we work in the future.
The Cloud, once a radical idea in IT, is now mainstream. Whether it’s email, backup or file sharing, most consumers probably use a cloud service or two. Similarly, most IT professionals are familiar with cloud service providers such as Amazon, Google and Microsoft Azure, and many companies have moved at least some of their information technology processes into the cloud. In fact, the cloud has become so popular it’s easy to assume that running IT applications on-premises is not cost competitive with a cloud based service. In this report Evaluator Group will test the validity of that assumption with a TCO (Total Cost of Ownership) model analyzing a hyperconverged appliance solution from HPE and a comparable cloud service from Amazon Web Services (AWS).
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making.
Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
AbeBooks, with Amazon Redshift, has been able to upgrade to a comprehensive data warehouse with the enlistment of Matillion ETL for Amazon Redshift. In this case study, we share AbeBooks’ data warehouse success story.
Published By: Dell EMC
Published Date: Aug 17, 2017
For many companies the appeal of the public cloud is very real. For tech startups, the cloud may be their
only option, since many don’t have the capital or expertise to build and operate the IT systems their
businesses need. Existing companies with established data centers are also looking at public clouds, to
increase IT agility while limiting risk. The idea of building-out their production capacity while possibly
reducing the costs attached to that infrastructure can be attractive. For most companies the cloud isn’t
an “either-or” decision, but an operating model to be evaluated along with on-site infrastructure. And
like most infrastructure decisions the question of cost is certainly a consideration.
In this report we’ll explore that question, comparing the cost of an on-site hyperconverged solution with
a comparable set up in the cloud. The on-site infrastructure is a Dell EMC VxRailTM hyperconverged
appliance cluster and the cloud solution is Amazon Web Services (AWS).
As more organizations consider a move to the cloud, security remains a top concern. Learn how Alert Logic’s suite of security solutions are designed to provide infrastructure and application security and compliance through a cloud-native model that takes advantage of the AWS business model and elastic scaling capabilities.
One of the value propositions of an Internet of Things (IoT) strategy is the ability to provide insight that was previously invisible to the business. But before a business can develop a strategy for IoT, it needs a platform that meets the foundational principles of an IoT solution. Amazon Web Services (AWS) believes in some basic freedoms that are driving organizational and economic benefits of the cloud into businesses. These freedoms are why more than a million customers already use the AWS platform to support virtually any cloud workload. These freedoms are also why AWS is proving itself as the primary catalyst to any Internet of Things strategy across commercial, consumer, and industrial solutions.
This paper outlines core tenets that should be considered when developing an IoT strategy, the benefits of AWS in that strategy and how the AWS cloud platform can be the critical component supporting those core tenets.
Les technologies d'intelligence artificielle telles que l'apprentissage machine et le Deep Learning permettent d'obtenir des informations et de la précision à deux marques majeures dans des secteurs très différents : la santé et les assurances.
Des théories sur les futures incidences de l'intelligence artificielle (IA) sur les entreprises et la société vont florissantes. Mais la réalité du terrain aujourd'hui pour les entreprises et les dirigeants appliquant des technologies comme l'apprentissage machine et l'apprentissage profond à leurs enjeux majeurs est déjà très enthousiasmante. Les modèles de fonctionnement sont refondés en se basant sur les informations obtenues de puissantes capacités cognitives. De nouveaux produits et services améliorent l'expérience client, voire la condition humaine. D'une façon très concrète et significative, l'IA change le monde pour le meilleur.
AI-Technologien wie Machine Learning und Deep Learning liefern zwei großen Marken in zwei sehr unterschiedlichen Branchen – Gesundheit und Versicherungen – Insights und Genauigkeit.
Theorien zu den zukünftigen Auswirkungen künstlicher Intelligenz (AI) auf die Geschäftswelt und die Gesellschaft sind allgegenwärtig. Aber die Realität, wie Unternehmen sie kennen, die Technologien wie Machine Learning und Deep Learning anwenden, ist schon aufregend genug. Geschäftsmodelle werden anhand der Insights umgestaltet, die durch die leistungsstarken kognitiven Funktionen generiert werden. Neue Produkte und Dienstleistungen verbessern das Benutzererlebnis – um nicht zu sagen die menschliche Existenz. Auf sehr reale und bedeutsame Weise verbessert AI die Welt.
What is a Data Lake?
Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems.
Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
Download to find out more now.
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes.
This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
As easy as it is to get swept up by the hype surrounding big data, it’s just as easy for organizations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt.
However, given big data’s power to transform business, it’s critical that organizations overcome these challenges and realize the value of big data.
Download now to find out more.
IDC’s research has shown the movement of most IT workloads to the cloud in the coming years. Yet, with all the talk about enterprises moving to the cloud, some of them still wonder if such a move is really cost effective and what business benefits may result. While the answers to such questions vary from workload to workload, one area attracting particular attention is that of the data warehouse.
Many enterprises have substantial investments in data warehousing, with an ongoing cost to managing that resource in terms of software licensing, maintenance fees, operational costs, and hardware. Can it make sense to move to a cloud-based alternative? What are the costs and benefits? How soon can such a move pay itself off?
Download now to find out more.
Defining the Data Lake
“Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
Die Recherchen von IDC haben ergeben, dass in den nächsten Jahren die meisten IT-Workloads in die Cloud verschoben werden. Doch neben all den positiven Berichten über Unternehmen, die in die Cloud umziehen, gibt es auch Unternehmen, die sich noch immer fragen, ob ein solcher Wechsel wirklich kosteneffizient ist und welche Vorteile sich aus einem solchen ergeben. Während die Antworten auf solche Fragen von Workload zu Workload variieren, gibt es ein Element, das besondere Aufmerksamkeit auf sich zieht: das Data-Warehouse.
Il est tout aussi facile d'être submergé par l'omniprésent Big Data qu'il l'est pour les organisations d'être découragées par les défis qu'elles rencontrent lorsqu'elles implémentent une initiative en matière de Big Data. Les préoccupations liées aux ensembles de compétences associées au Big Data (et à leur absence), à la sécurité, à l'imprévisibilité des données, aux coûts non viables et à la nécessité d'effectuer une analyse de rentabilité peuvent mettre brutalement fin à une initiative en matière de Big Data.