In Forrester's evaluation of the emerging market for conversational computing platforms, we identified the seven most significant providers ó Amazon, Google, IBM, Microsoft, Nuance Communications, Oracle, and Rulai ó in the category and evaluated them. This report details our findings about how each vendor scored against nine criteria and where they stand in relation to each other. Application developers should use this review to select the right partners for their conversational computing platform needs.
Delivering personalized customer experience remains the top business challenge for communications
service providers (CSPs). Ovum's recently published 2018 ICT Enterprise survey saw almost all CSP
IT executives interviewed identify delivering personalized customer experience as one of their three
most important business challenges for the next 18 months. This trend emphasizes the high priority
CSPs place on how customer relationships are managed. However, several factors have an impact on
CSPs' ability to identify and then deliver customers' core needs. These include understanding the data
sets they should focus on; collecting, cleansing, and consolidating these data sets; and having the
right expertise to mine the data sets.
The data maturity curve
As companies invest more and more in data access and
organization, business leaders seek ways to extract more
business value from their organizationís data.
92 percent of business leaders say that to compete in the future,
their organization must be able to exploit information much more
quickly than it can today.1
Chief Information Officers (CIO) need solutions that will allow
them to evolve their organizationís approach to data and drive real
value with strategic decisions. This journey can be depicted in
a data maturity curve.
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
between data scientists (who often work for
business departments) and the people (usually
IT) who need to operationalise the work of
those data scientists
A range of application security tools was developed to support the efforts to secure the enterprise from the threat posed by insecure applications. But in the ever-changing landscape of application security, how does an organization choose the right set of tools to mitigate the risks their applications pose to their environment? Equally important, how, when, and by whom are these tools used most effectively?
Today, when you make decisions about information technology (IT) security priorities, you must often strike a careful balance between business risk, impact, and likelihood of incidents, and the costs of prevention or cleanup. Historically, the most well-understood variable in this equation was the methods that hackers used to disrupt or invade the system.
Countless studies and analyst recommendations suggest the value of improving security during the software development life cycle rather than trying to address vulnerabilities in software discovered after widespread adoption and deployment. The justification is clear.For software vendors, costs are incurred both directly and indirectly from security flaws found in their products. Reassigning development resources to create and distribute patches can often cost software vendors millions of dollars, while successful exploits of a single vulnerability have in some cases caused billions of dollars in losses to businesses worldwide. Vendors blamed for vulnerabilities in their product's source code face losses in credibility, brand image, and competitive advantage.
The Business Case for Data Protection, conducted by Ponemon Institute and sponsored by Ounce Labs, is the first study to determine what senior executives think about the value proposition of corporate data protection efforts within their organizations. In times of shrinking budgets, it is important for those individuals charged with managing a data protection program to understand how key decision makers in organizations perceive the importance of safeguarding sensitive and confidential information.
This white paper will provide a road map to the most effective strategies and technologies to protect data and provide fast recovery should data be lost or corrupted due to accident or malicious action.
Journaling is a powerful feature, one that IBM has continued to develop and improve over the years. Yet, depending upon your business requirements, you probably still need more protection against downtime than journaling alone can provide. This white paper will cover what you need to know about journaling, what it can do and how it supports and cooperates with high availability software.
Achieving effective and efficient high availability protection for larger IBM i environments requires careful thought and clear understanding of the technology options. This white paper describes what you need to know in order to make an informed decision about IBM i high availability strategies so that your business requirements for Recovery Time Objective (RTO) and Recovery Point Objective (RPO) are not compromised
This white paper provides a road map to the most effective strategies and technologies to protect data in AIX environments and provide fast recovery should data be lost or corrupted due to accident or malicious action. The paper also outlines the benefits of continuous data protection (CDP) technologies for AIX.
Continuous member service is an important deliverable for credit unions, and. the continued growth in assets and members means that the impact of downtime is affecting a larger base and is therefore potentially much more costly. Learn how new data protection and recovery technologies are making a huge impact on downtime for credit unions that depend on AIX-hosted applications.
Published By: Cisco EMEA
Published Date: Nov 13, 2017
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.
Mention artificial intelligence (AI) to a person on the street, and you'll conjure up Hollywood visions ranging from the humanity-crushing Skynet of the Terminator series to the robot love interests in Her or Ex Machina. Perspectives on the tangible impact of AI similarly range from Elon Musk's declaration of AI as "the greatest risk we face as a civilization" to IBM Chief Science Officer Dr. Guruduth Banavar's belief that "we've never known technology that can have a greater benefit to all of society than artificial intelligence". The reality, especially in the short term, likely lies somewhere between these extremes.
The Cisco UCS solution provides all management and configuration services at the centrally located Fabric Interconnects, so you can manage large-scale deployments from a single location. This method lets you consolidate hardware and streamline management. The IBM Flex System solution uses a distributed management model with chassis-level control. This method adds to the complexity to the hardware configuration, which can increase management needs.