With the advent of big data, organizations worldwide are
attempting to use data and analytics to solve problems previously
out of their reach. Many are applying big data and analytics
to create competitive advantage within their markets, often
focusing on building a thorough understanding of their
High-priority big data and analytics projects often target
customer-centric outcomes such as improving customer loyalty
or improving up-selling. In fact, an IBM Institute for Business
Value study found that nearly half of all organizations with active
big data pilots or implementations identified customer-centric
outcomes as a top objective (see Figure 1).1 However, big data
and analytics can also help companies understand how changes
to products or services will impact customers, as well as address
aspects of security and intelligence, risk and financial management,
and operational optimization.
A rewarding customer experience is the central aim for luxury gift company 1-800-Flowers.com: a fast, intuitive shopping process helps keep consumers loyal to its brands. Working with IBM, the company has built a master data management (MDM) system that helps deliver a more seamless experience to shoppers across multiple brands and channels.
Read this report from IDG to see how Juniper Networks can help in several ways:
• Simplify at the data center, campus, and branch levels
• Protect the edge via the cloud
• Centralize functions like configuration, provision, management, and more
C’est l’un des changements majeurs de ces 20 dernières années au niveau de la protection de la vie privée dans le domaine numérique. Le Règlement général de l’UE sur la protection des données (RGPD) introduira, en mai 2018, des amendes d’un montant pouvant atteindre jusqu’à 20 millions d’euros en cas de non-conformité.
Depuis plus de vingt ans, les entreprises doivent se conformer à différentes directives et réglementations en matière de protection des données. Le Règlement général sur la protection des données (RGPD ou GDPR en anglais), qui reprend l’ensemble des législations existantes de la Commission européenne en matière de protection des données, a toutefois pour but de renforcer et d’harmoniser ces différentes réglementations pour les citoyens européens. Les principaux objectifs du RGPD sont de redonner aux citoyens un contrôle sur leurs données personnelles et de simplifier le cadre réglementaire pour les entreprises internationales. Pour les organisations déjà conformes à la Directive 95/46/CE, quels sont les critères technologiques à remplir pour garantir la conformité au RGPD ?
Ce document présente les résultats d’une enquête commandée par CA Technologies en vue de comprendre la situation des entreprises face aux exigences imposées par le RGPD. Ce dernier ayant de vastes implications concernant le type de données pouvant être utilisées dans les environnements autres que de production, CA Technologies souhaitait avant tout comprendre comment les entreprises envisageaient de se mettre en conformité avec le RGPD et quels sont les processus et technologies nécessaires pour y parvenir.
Cloud-based data presents a wealth of potential information for organizations seeking to build and maintain competitive advantage in their industries.
However, as discussed in “The truth about information governance and the cloud,” most organizations will be challenged to reconcile their legacy on-premises data with new third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.
The growing need for organizations to treat information as an asset is making metadata management strategic, driving significant growth for metadata management solutions. We evaluate nine vendors to help data and analytics leaders find the solution that best suits the needs of their organization.
Today, all consumers can obtain any
piece of data at any point in time. This
experience represents a significant
cultural shift: the beginning of the
democratization of data.
However, the data landscape is increasing
in complexity, with diverse data types
from myriad sources residing in a mix of
environments: on-premises, in the cloud or
both. How can you avoid data chaos?
The perimeter continues to dissolve, and the definition of endpoint is evolving, according to results of the SANS 2016 Endpoint Security Survey, now in its third year.
As we might expect, 90% or more consider desktops, servers, routers, firewalls and printers to be endpoints that need to be protected. After that, respondents include other less-typical devices in their definition of endpoints that warrant protection: 71% include building security (access/ surveillance), 59% include employee-owned mobile devices and 40% consider industrial control systems as endpoints that need to be protected. Some respondents also consider POS devices, smart cars, emulated endpoints in the cloud and wearables as endpoints needing protection, highlighting the diversity of thinking among respondents.
The growth of virtualization has fundamentally changed the data center and raised numerous questions about data security and privacy. In fact, security concerns are the largest barrier to cloud adoption. Read this e-Book and learn how to protect sensitive data and demonstrate compliance.
Virtualization is the creation of a logical rather than an actual physical version of something. such as a storage device, hardware platform, operating system, database or network resource. The usual goal of virtualization is to centralize administrative tasks while improving resilience, scalability and performance and lowering costs. Virtualization is part of an overall trend in enterprise IT towards autonomic computing, a scenario in which the IT environment will be able to manage itself based on an activity or set of activities. This means organizations use or pay for computing resources only as they need them.
An interactive white paper describing how to get smart about insider threat prevention - including how to guard against privileged user breaches, stop data breaches before they take hold, and take advantage of global threat intelligence and third-party collaboration.
Security breaches are all over the news, and it can be easy to think that all the enemies are outside your organization. But the harsh reality is that more than half of all attacks are caused by either malicious insiders or inadvertent actors.1 In other words, the attacks are instigated by people you’d be likely to trust. And the threats can result in significant financial or reputational losses.
There's an old saying in information security: "We want our network to be like an M&M, with a hard crunchy outside and a soft chewy center." For today's digital business, this perimeter-based security model is ineffective against malicious insiders and targeted attacks. Security and risk (S&R) pros must eliminate the soft chewy center and make security ubiquitous throughout the digital business ecosystem — not just at the perimeter. In 2009, we developed a new information security model, called the Zero Trust Model, which has gained widespread acceptance and adoption.
This report explains the vision and key concepts of the model. This is an update of a previously published report; Forrester reviews and updates it periodically for continued relevance and accuracy.
Firms face loss of Intellectual property (IP) and breaches of sensitive data as a result of account takeover (ATO). Risk-based authentication RBA plays an important role in the identity and access management (IAM) and risk mitigation of ATO across a variety of user populations (employee-facing [B2E] users, partners, clients, and consumer/citizen-facing users).
New headlines provide ongoing evidence that IT Security teams are losing the battle against attackers, reinforcing the need to address the security of enterprise applications.This Analyst Insight reviews several practical steps you can take to get started now.
The framework presented here is a way to avoid data dysfunction via a coordinated and well-planned governance initiative. These initiatives require two elements related to the creation and management of data:
• The business inputs to data strategy decisions via a policy
• The technology levers needed to monitor production data
based on the policies.
Collectively, data governance artifacts (policies, guiding principles and operating procedures) give notice to all stakeholders and let them know, “We value our data as an asset in this organization, and this is how we manage it.”
While the concept of big data is nothing new, the tools and technology and now in place for companies of all types and sizes to take full advantage. Enterprises in industries such as media, entertainment, and research and development have long been dealing with data in large volumes and unstructured formats - data that changes in near real time. However, extracting meaning from this data has been prohibitive, often requiring custom-built, expensive technology. Now, thanks to advancements in storage and analytics, all organizations can leverage big data to gain the insight needed to make their businesses more agile, innovative, and competitive.