• Facing a myriad of challenges from digital transformation, business today are making big bets on the best collaboration tools they need on hand to meet those challenges. From employee buy-in, to machine-learning capabilities, to security, it's important to select a service with the right capabilities to further your business goals. The challenge, however, is that with so many services to choose from it can be difficult to figure out which one is the right fit for your business.
• This eBook, 5 Considerations in Choosing a Collaboration Platform in the Digital Age, will walk you through the ins and outs of what to keep in mind as you choose the best collaboration platform for you.
As organisations increasingly leverage data, sophisticated analytics, robotics and AI in their operations, we ask who should be responsible for trusted analytics and what good governance looks like.
Read this report to discover:
• the four key anchors underpinning trust in analytics – and how to measure them
• new risks emerging as the use of machine learning and AI increases
• how to build governance of AI into core business processes
• eight areas of essential controls for trusted data and analytics.
Data is the lifeblood of business. And in the era of digital business,
the organizations that utilize data most effectively are also the most
successful. Whether structured, unstructured or semi-structured,
rapidly increasing data quantities must be brought into organizations,
stored and put to work to enable business strategies. Data integration
tools play a critical role in extracting data from a variety of sources and
making it available for enterprise applications, business intelligence
(BI), machine learning (ML) and other purposes. Many organization
seek to enhance the value of data for line-of-business managers by
enabling self-service access. This is increasingly important as large
volumes of unstructured data from Internet-of-Things (IOT) devices
are presenting organizations with opportunities for game-changing
insights from big data analytics. A new survey of 369 IT professionals,
from managers to directors and VPs of IT, by BizTechInsights on
behalf of IBM reveals the challe
The combination of legislation, market dynamics, and increasingly sophisticated risk management strategies requires you to be proactive in detecting risks like fraud quicker and more effectively.
Dynamic detection systems need to adapt to evolving compliance regulations, scale to deal with growing transaction volumes, detect sophisticated risk specific patterns, and reduce false-positives. TIBCO's Risk Management Accelerator uses a combination of predictive analytics, streaming analytics, and business process management to deliver a powerful and cost-effective system for detecting anomalies.
Download this solution brief to learn more.
Humans excel at tasks that require creativity, the opportunity to respond to the unexpected, and general attentiveness to the surrounding environment. Technology and machines, on the other hand, are built to process a lot of information quickly without getting bored; technology reliably completes the task it is assigned without deviation. The most powerful approach to adding technology to a team takes the strengths of both humans and technology into account and from that, creates a superior, collaborative system. For example, reporting software such as Spreadsheet Server and Atlas for Dynamics AX/365 by Global Software, Inc. both serve as machine learning intelligence that uses automation to reduce errors, while at the same time preserving humans’ ability to create reports and outcomes from the data the way they need to see it.
Published By: CrowdTwist
Published Date: Apr 16, 2018
In order for brands to compete and provide the level of personalization consumers have already come to expect, marketers need to work quickly to develop competencies around their abilities to collect contextual and anticipatory insight and meet customers in the moments that matter most to them.
Now is the time for marketers to invest in technology that supports data capture, segmentation, predictive analytics, and machine learning.
With these capabilities in place, brands should be on track to build rich first party profiles of customers across all channels and maximize customer lifetime value by creating relevant experiences at all stages of the customer lifecycle.
As digital business evolves, however, we’re finding that the best form of security and enablement will likely remove any real responsibility from users. They will not be required to carry tokens, recall passwords or execute on any security routines. Leveraging machine learning, artificial intelligence, device identity and other technologies will make security stronger, yet far more transparent. From a security standpoint, this will lead to better outcomes for enterprises in terms of breach prevention and data protection. Just as important, however, it will enable authorized users in new ways. They will be able to access the networks, data and collaboration tools they need without friction, saving time and frustration. More time drives increased employee productivity and frictionless access to critical data leads to business agility. Leveraging cloud, mobile and Internet of Things (IoT) infrastructures, enterprises will be able to transform key metrics such as productivity, profitabilit
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
Business users expect immediate access to data, all the
time and without interruption. But reality does not always
meet expectations. IT leaders must constantly perform
intricate forensic work to unravel the maze of issues that
impact data delivery to applications. This performance
gap between the data and the application creates a
bottleneck that impacts productivity and ultimately
damages a business’ ability to operate effectively.
We term this the “app-data gap.”
"This research by Nimble Storage, a Hewlett Packard Enterprise Company, outlines the top five causes of application delays. The report analyzes more than 12,000 anonymized cases of downtime and slow performance. Read this report and find out:
Top 5 causes of downtime and poor performance across the infrastructure stack
How machine learning and predictive analytics can prevent issues
Steps you can take to boost performance and availability"
Published By: Dell EMC
Published Date: Oct 13, 2016
Flexibility is important, since many future initiatives—big data, machine learning, emerging technologies, and new business directions—will be built on this cloud structure.
No matter what shape your cloud infrastructure takes, Dell EMC converged and hyper-converged platforms and innovations like Dell EMC VscaleTM Architecture, powered by Intel® Xeon® processors, deliver the pathways to scale-up and scale-out, today and tomorrow.
Published By: Genesys
Published Date: Jun 06, 2017
In this ebook, learn:
- Five trends will have the biggest impact on customer experience
- How to use machine learning to detect patterns and trends to deliver the next great customer experiences
- How to future-proof your contact center and adapt to changing customer needs
Every week InfoSight analyzes more than a trillion data points from
more than 9,000 customers. How does this translate into true
business value? By reducing your business risk with over Six-Nines
of measured availability. By providing you with an infrastructure
that gets “smarter” every single day. By empowering IT staff to
focus on business priorities instead of mundane maintenance.
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally.
Learn more about key findings, including:
Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics.
Return on investment for analytics stems from the governing and sharing of data throughout the organization.
Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
Published By: Pentaho
Published Date: Nov 04, 2015
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
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
While many organizations are guarding the front door with yesterday’s signature-based antivirus (AV) solutions, today’s unknown malware walks out the back door with all their data. What’s the answer? A new white paper, “The Rise of Machine Learning in Cybersecurity,” explains machine learning (ML) technology —what it is, how it works and why it offers better protection against the sophisticated attacks that bypass standard security measures. You’ll also learn about CrowdStrike’s exclusive ML technology and how, as part of the Falcon platform’s next-gen AV solution,it dramatically increases your ability to detect attacks that use unknown malware.
Download this white paper to learn:?How different types of ML are applied in various industries and why it’s such an effective tool against unknown malware?Why ML technologies differ and what factors can increase the accuracy and effectiveness of ML ?How CrowdStrike’s ML-based technology works as part of the Falcon platform’s next-generation AV
Marketing leaders will find a host of new vendors in this year's Magic
Quadrant for multichannel campaign management. Vendors are focused on integrating machine learning, personalization and ad tech capabilities into big data foundations for deeper customer engagement.
Published By: Oracle CX
Published Date: Oct 19, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions
of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by
the aggressive build-out for cloud computing. Big data and machine learning applications that perform
tasks such as fraud and intrusion detection, trend detection, and click-stream and social media
analysis all require forward-thinking solutions and enough compute power to deliver the performance
required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of
business up, and organizations need to support their customers with real-time data. The task of
managing sensitive information while capturing, analyzing, and acting upon massive volumes of data
every hour of every day has become critical.
These challenges have dramatically changed the way that IT systems are architected, provisioned,
and run compared to the past few decades. Most companies
A fundamental people-process-technology transformation enables businesses to remain
competitive in today’s innovation economy. Initiatives such as advanced security, fraud detection
services, connected consumer Internet of Things (IoT) devices, augmented or virtual reality
experience, machine and deep learning, and cognitively enabled applications drive superior
business outcomes such as predictive marketing and maintenance.
Superior business outcomes require businesses to consider IT a core competency. For IT, an
agile, elastic, and scalable IT infrastructure forms the crucial underpinning for a superior service
delivery model. The more up to date the infrastructure, the more capable it is of supporting the
scale and complexity of a changing application landscape. Current-generation applications must
be supplemented and eventually supplanted with next-generation (also known as cloud-native)
applications — each with very different infrastructure requirements. Keeping infrastructure up
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.