Network failures are surprisingly common. How can you be sure your enterprise is prepared for unforeseen downtime?
Network diversity and redundancy supports business continuity. Learn more about how the right network portfolio can prevent operational interruptions and assist in disaster recovery.
When it comes to effectively and efficiently protecting growing volumes of data, midsized organizations face unique
challenges. That is because they live in a world of constraints that are both operational and budgetary in nature. Cloud
disaster recovery offers new options for these organizations—they can optimize their data protection economics by
integrating on-premises protection solutions with cloud-based backup and recovery methods. Dell EMC’s cloud-ready
solutions, particularly its Integrated Data Protection Appliances with native cloud extension capabilities, along with its Data
Protection Software working in conjunction with its Data Domain backup storage appliances, provide cloud disaster
recovery with flexible features. These solutions enhance operational efficiency and provide midsized organizations with
clear economic and operational benefits.
La transformación digital se está produciendo cada vez en más organizaciones y las
pequeñas y medianas empresas no pueden permitirse el lujo de ignorarla. En el caso
concreto de RR. HH., las barreras para el éxito de la transformación son las personas, el
conocimiento y la cultura. Guste o no, el negocio confiará en que el departamento de RR. HH.
apoye estas iniciativas y haga que esto suceda desde la base de empleados.
Published By: Cohesity
Published Date: Nov 20, 2018
Today, hybrid cloud is increasingly the norm, and enterprises are challenged with ways to have visibility, manage and make use of all this data – both on-premises and in the cloud. While much attention has been given to primary data affecting mission-critical workloads, data for secondary workloads – backup, test/dev, disaster recovery, and archiving to name a few – have become siloed the same way application data has, leading to multiple point solutions to manage an increasing amount of data.
This white paper looks at the evolution of these challenges and offers practical advice on ways to store, manage and move secondary data in hybrid cloud architectures while extracting the hidden value it can provide. Download to learn more.
in our 24-criteria evaluation of experience optimization platform (EoP) providers, we identified the eight most significant ones — Adobe, dynamic Yield, Evergage, Monetate, optimizely, oracle, sAs, and sitespect — and researched, analyzed, and scored them. this report shows how each provider measures up and helps customer insights (Ci) professionals make the right choice.
In our 26-criteria evaluation of digital intelligence (DI) platform providers, we identified the 10 most significant ones — Adobe, Cxense, evergage, google, IBM, localytics, Mixpanel, optimizely, sAs, and Webtrekk — and researched, analyzed, and scored them. this report shows how each provider measures up and helps customer insights (CI) professionals make the right choice.
"Gartner’s SWOT on Zerto examines Zerto’s position as a top provider for enterprise disaster recovery and IT Resilience. This informative, enlightening report from Gartner, the world’s preeminent research and advisory firm, will help business leaders like you make informed decisions about using Zerto solutions to achieve IT resilience.
The Gartner SWOT on Zerto covers:
In-depth 3rd party Analysis
Recommendations on Zerto Use Cases Beyond Disaster Recovery
Implications for Your Business
Download the Gartner SWOT analysis to help guide you as you navigate the next steps for your organizations path to IT resilience!"
"Use this e-book to help move beyond the day to day challenges of protecting your business and start shifting to an IT resilience strategy. IT resilience is an emerging term that describes a stated goal for businesses to accelerate transformation and easily adapt to change while protecting the business from disruption.
How to prepare for both unplanned and planned disruptions to ensure continuous availability
Actionable steps to remove the complexity of moving and migrating workloads across disparate infrastructures
Guidance on hybrid and multi-cloud IT: gain the flexibility to move applications in and out of the cloud"
"Confused about RTOs and RPOs? Fuzzy about failover and failback? Wondering about the advantages of continuous replication over snapshots? Well, you’re in the right place. The Disaster Recovery 101 eBook will help you learn about DR from the ground up and assist you in making informed decisions when implementing your DR strategy, enabling you to build a resilient IT infrastructure.
This 101 guide will educate you on topics like:
How to evaluate replication technologies
Measuring the cost of downtime
How to test your Disaster Recovery plan
Reasons why backup isn’t Disaster Recovery
Tips for leveraging the cloud
Mitigating IT threats like ransomware
Get your business prepared for any interruption, download the Disaster Recovery 101 eBook now!"
Email is the primary communication system and file transport mechanism used in organizations of all sizes. Email systems generate enormous amounts of content that must be preserved for a variety of reasons, including:
-Compliance with local, state, federal and international statutory requirements
- Electronic discovery requirements and best practices
- Knowledge management applications
- Disaster recovery and business continuity
All those employees who access email, financial systems, human resources, and other core corporate applications; Replay for Exchange continuously protects and monitors the health of your Exchange data stores and allows administrators to quickly search, recover, and analyze mailbox content. With Replay for Exchange you can restore individual email messages, folders, or mailboxes to a live Exchange server or directly to a PST, thereby solving some of your most costly and time consuming challenges. Take advantage of these Free Trial Offer!!
Windows Server 2012 represents a paradigm shift from the traditional client/server model to a new cloud-based infrastructure. Is your business ready? Download this whitepaper to learn the 7 key questions you need to answer now
Businesses are struggling with numerous variables to determine what their stance should be
regarding artificial intelligence (AI) applications that deliver new insights using deep learning.
The business opportunities are exceptionally promising. Not acting could potentially be a
business disaster as competitors gain a wealth of previously unavailable data to grow their
customer base. Most organizations are aware of the challenge, and their lines of business
(LOBs), IT staff, data scientists, and developers are working to define an AI strategy.
IDC believes that this emerging environment is to date still highly undefined, even as
businesses must make critical decisions. Should businesses develop in-house or use VARs,
systems integrators, or consultants? Should they deploy on-premise, in the cloud, or in some
hybrid form? Can they use existing infrastructure, or do AI applications and deep learning
require new servers with new capabilities? We believe that many of these questions can be
You may know some data management basics, but are you aware of the transformational results that can result from doing data management right? This paper explains core data management capabilities, then describes how a solid data management foundation can help you get more out of your data.
Fraudsters are only becoming smarter. How is your organization keeping pace and staying ahead of fraud schemes and regulatory mandates to monitor for them? In this e-book, learn the basics in how to prevent fraud, achieve compliance and preserve security.
Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues.
As our corporate data stores have grown in both size and subject area diversity, it has become clear that a strategy to address data is necessary. Yet some still struggle with the idea that corporate data needs a comprehensive strategy.
There’s no shortage of blue-sky thinking when it comes to organizations’ strategic plans and road maps. To many, such efforts are just a novelty. Indeed, organizations’ strategic plans often generate very few tangible results for organizations – only lots of meetings and documentation. A successful plan, on the other hand, will identify realistic goals along with a r
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data.
To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever.
Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
You may know some basics about data management, but do you realize the transformational results data-management-done-right can produce? This paper explains core data management capabilities, then describes how a solid data management foundation can help you get more out of your data. From getting fast, easy access to trustworthy data to making better decisions and becoming a data-driven business, you’ll learn why good data management is essential to success. Multiple real-world examples illustrate how SAS customers have used data management to improve customer experience, boost revenue, remain compliant and become more efficient.
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
Some organizations focus on the scary aspects of failing to comply with the EU General Data Protection Regulation. But there are many long-term benefits of following through with plans for sustainable GDPR compliance – such as gaining a competitive edge, or developing new products or services.
To learn how organizations have approached compliance efforts, SAS conducted a global survey among 183 cross-industry businesspeople involved with GDPR. Based on the results, this e-book delves into the biggest opportunities and challenges faced.
Read the e-book to:
• Get advice from industry experts.
• Find out what steps peers have taken.
• Learn how an integrated approach from SAS can continue to guide your journey.
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
Starting data governance initiatives can seem a bit daunting. You’re establishing strategies and policies for data assets. And, you’re committing the organization to treat data as a corporate asset, on par with its buildings, its supply chain, its employees or its intellectual property.
However, as Jill Dyché and Evan Levy have noted, data governance is a combination of strategy and execution. It’s an approach that requires one to be both holistic and pragmatic:
• Holistic. All aspects of data usage and maintenance are taken into account in establishing
• Pragmatic. Political challenges and cross-departmental struggles are part of the
equation. So, the tactical deployment must be delivered in phases to provide quick
“wins” and avert organizational fatigue from a larger, more monolithic exercise.
To accomplish this, data governance must touch all internal and external IT systems and establish decision-making mechanisms that transcend organizational silos. And, it must provi