U.S. Flood is a high-gradient, intricate peril incorporating various sources, and causing a variety of effects. It requires sophisticated models, data science, and analytics technology to properly understand and assess each risk.
Published By: Aberdeen
Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
Published By: Corrigo
Published Date: Sep 12, 2019
We’re living in a new era defined by data, analytics, and intelligence. It can sound overwhelming, but once you understand how to make all this information work for you, it becomes really exciting.
The Intelligence Economy has already changed our day-to-day lives, and it’s revolutionizing the world of facilities management. The best FMs don’t just understand this change – they embrace it.
With a better handle on intelligence, you will see immediate results. From warranty enforcement to asset repair vs. replacement decisions, Corrigo’s collective intelligence can improve your bottom line and make you better at your job.
Read on to learn how Corrigo makes the Intelligence Economy work for you.
Discover how to revolutionize processing performance, data intelligence, customer experiences, and GRC.
The future of financial services will belong to those who can capture and capitalize on data. And it all begins with employing modern data strategies in four critical areas.
You’ll learn how to:
Leverage AI, machine learning and predictive analytics.
Get scalable, high-speed access to vast amounts of data.
Respond faster, become more competitive, and attract new customers.
"Agile BI requires more than just agile dashboards. True agility means prototyping data models quickly so business users can continuously iterate on them. Application development and delivery professionals working on BI initiatives should consider adding DWA platforms to their BI toolbox.
This Forrester report discusses how seven data warehouse automation vendors bring Agile options to all phases of BI/analytics application development. Read more to find out how these platforms help facilitate shorter development cycles."
As organizations continue to produce vast quantities of data, they increasingly need platforms that allow them to analyze, store, and extract meaningful insights from that data. Gartner helps data and analytics leaders evaluate 19 vendors in an increasingly split market.
Download the Gartner Magic Quadrant report and find out more.
Published By: Gigamon
Published Date: Jun 21, 2019
Accelerate your digital transformation journey by giving teams and tools the application visibility needed to monitor and secure modern digital applications.
Download this whitepaper to learn how you can
Isolate and extract application and component traffic across multiple tiers for monitoring,
Provide application metadata to analytics tools, enabling faster detection of customer experience, application performance and security-related issues and send only relevant traffic to the appropriate tools to reduce load and increase effectiveness.
Published By: Gigamon
Published Date: Sep 03, 2019
The IT pendulum is swinging to distributed computing environments, network perimeters are dissolving, and
compute is being distributed across various parts of organizations’ infrastructure—including, at times, their extended
ecosystem. As a result, organizations need to ensure the appropriate levels of visibility and security at these remote
locations, without dramatically increasing staff or tools. They need to invest in solutions that can scale to provide
increased coverage and visibility, but that also ensure efficient use of resources. By implementing a common
distributed data services layer as part of a comprehensive security operations and analytics platform architecture
(SOAPA) and network operations architecture, organizations can reduce costs, mitigate risks, and improve operational
Getting complex decisions right across complicated operational networks is the key to optimum performance. Find out how one of the UK’s biggest bus operators is using data and analytics to make better decisions and optimise the use of resources across their network.
Read this story to discover:
• how data and analytics can transform operational performance
• the benefits of using decision-support tools in the middle office
• key lessons for getting your plans for digital transformation right.
"We live and surf in a cyber world where attacks like APT, DDOS, Trojans and Ransomware are common and easy to execute. Domain names are an integral part of any business today and apparently an integral part of an attacker's plan too.
Domain names are carriers of malwares, they act as Command and Control servers and malware's ex-filtrate data too. In today's threat landscape - predicting threats, spotting threats and mitigating them is super crucial.. This is called Visibility and Analytics.
Watch this on demand session with our Cisco cloud security experts Shyam Ramaswamy and Fernando Ferrari as they talk about how Cisco Umbrella and The Umbrella Research team detect anomalies, block threats and identify compromised hosts. The experts also discuss how effectively Cisco spot, react, filter out IOC, block the network communications of a malware; identify and stop a phishing campaign (unknown ones too).
Analyst firms Gartner, Inc. and Forrester are projecting accelerated data virtualization adoption for both first-time and expanded deployments. What are the uses cases for this technology? At its Data and Analytics Summit in London in March 2018, Gartner answered this question by identifying 13 data virtualization use cases. This paper explores each of these use cases by:
Identifying key requirements
Showing how you can apply TIBCO® Data Virtualization to address these needs
Listing the benefits you can expect when implementing TIBCO Data Virtualization for the use case
Digital business initiatives have expanded in scope and complexity as companies have increased the rate of digital innovation to capture new market opportunities. As applications built using fine-grained microservices and functions become pervasive, many companies are seeing the need to go beyond traditional API management to execute new architectural patterns and use cases.
APIs are evolving both in the way they are structured and in how they are used, to not only securely expose data to partners, but to create ecosystems of internal and/or third-party developers.
In this datasheet, learn how you can use TIBCO Cloud™ Mashery® to:
Create an internal and external developer ecosystem
Secure your data and scale distribution
Optimize and manage microservices
Expand your partner network
Run analytics on your API performance
Tips and best practices for data analytics executives
Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time.
This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them.
With the new TIBCO Spotfire® A(X) Experience, we are revolutionizing analytics and business intelligence.
This new platform accelerates the personal and enterprise analytics experience so you can get from data to insights in the fastest possible way. With the fusion of technology enablers like machine learning, artificial intelligence, and natural language search, the Spotfire® X platform redefines what’s possible for analytics and business intelligence, simplifying for everyone how data and insights are generated, consumed, and acted on.
Download this whitepaper to learn more, then check out the new Spotfire analytics. It’s unlike anything you have ever seen. Simple, yet powerful, it changes everything.
The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and the need to rework faulty products.
To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support.
At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
Today, you can improve product quality and gain better control of the entire
manufacturing chain with data virtualization, machine learning, and advanced
data analytics. With all relevant data aggregated, analyzed, and acted on, sensors,
devices, people, and processes become part of a connected Smart Factory
•? Increased uptime, reduced downtime
•? Minimized surplus and defects
•? Better yields
•? Reduced cost due to better quality
•? Fewer deviations and less non-conformance
In the last few years we have seen a rapid evolution of data. The need to embrace the growing volume, velocity and variety of data from new technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) has been accelerated.
The ability to explore, store, and manage your data and therefore drive new levels of analytics and decision-making can make the difference between being an industry leader and being left behind by the competition. The solution you choose must be able to:
• Harness exponential data growth as well as semistructured and unstructured data
• Aggregate disparate data across your organization, whether on-premises or in the cloud
• Support the analytics needs of your data scientists, line of business owners and developers
• Minimize difficulties in developing and deploying even the most advanced analytics workloads
• Provide the flexibility and elasticity of a cloud option but be housed in your data center for optimal security and compliance
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already under pressure, Big Data footprints are getting larger and posing a huge storage challenge. This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
Digital disruption, economic instability, political upheavals and skills shortages have all at some point in the past 24 months been blamed for business failure, or at the very least, lost profitability and earnings.
It’s perhaps not a huge surprise that a Gartner CEO survey on business priorities revealed that digital business is a top priority for next year. Survey respondents were asked whether they have a management initiative or transformation program to make their business more digital. The majority (62 percent) said they did. Of those organisations, 54 percent said that their digital business objective is transformational while 46 percent said the objective of the initiative is optimisation.*
So, for businesses it’s a case of learning to evolve and be agile, to use technology to help compete more efficiently and not fall victim to inertia. As businesses become increasingly dependent on the insights from data analytics and face-up to competition fuelled by the 24/7 society of in
Published By: Flexential
Published Date: Jul 17, 2019
In a data environment that’s become increasingly centralized by public cloud services, the “edge” is emerging as a critical solution for reducing latency for network-based services. Consumption habits of services and the need for analytics are shifting beyond core population centers, becoming local and even hyper-local within a region or city. As the online population continues to grow and new services emerge, the ability to handle data traffic securely – close to the customer or application – will become a common pattern for the new service evolution.
Organizations are charging ahead with investments in cloud and analytics to deliver agility, scalability and cost savings. With computing power advancements and continuous growth of data, cloud provides the elastic workloads and flexibility required for modern business. However, the environment of flexibility and choice that cloud provides also creates complexity and challenges.
In this white paper, learn how organizations are applying expertise and using the latest methods to move analytics to the cloud, including:
• Why are organizations moving analytic work to the cloud?
• What are the key challenges and misconceptions?
• How do IT leaders provide choice while maintaining control?
Envision this situation at a growing bank. Its competitive landscape demands an agile
response to evolving customer needs. Fortunately, analytically minded professionals in
different divisions are seeing results that positively affect the bottom line.
• A data scientist in the business development team analyzes data to create customized
• experiences for premium customers.
• A digital marketer tracks and influences the customer journey for prospective
• mortgage customers.
• A risk analyst builds risk models for the bank’s loan portfolios.
• A data analyst examines data about local customers.
• A technical architect defines a new system to protect bank data from internal and
• external cyberthreats.
• An application developer builds a new mobile app for online customer portfolio
Between them, these employees might be using more than a dozen packages for
analytics and data management.
Over the last decade, the enterprise analytics landscape has dramatically
transformed. Vendors have come and gone, and platforms have
continually expanded their offerings to include new functionality and
keep pace with the demands of the businesses they serve. Originally
envisioned as an IT-centric tool for enterprise reporting, analytics today
has evolved into a business solution—empowering a range of users
across every line of business, including front-line employees, field
personnel, and executives.
The rise of self-service analytics over the past decade has played a key role in promoting a data-driven mindset
within every business function. However, this practice is limited to a skilled few. The vast majority of business
professionals lack the time, analytical skills, or inclination to conduct their own analyses, and fail to effectively use
analytics on a day-to-day basis. The result? Despite decades of investments, BI adoption at most organizations
remains at 30%.
The failure of e
Today, despite massive investments in data, IT infrastructure,
and analytics software, the adoption of analytics continues to lag
behind. In fact, according to Gartner, most organizations fail to hit
the 30% mark. That means that more than 70% of people at most
organizations are going without access to the critical information
they need to perform to the best of their abilities.
What’s stopping organizations from breaking through the 30%
barrier and driving the pervasive adoption of intelligence? Simple.
The majority of existing tools only cater to users who are naturally
analytically inclined—the analysts, data scientists, and architects
of the world. The other 70%—the people making the operational
decisions daily within a business—simply lack the time, skill, or
desire to seek out data and intelligence on their own.
HyperIntelligence helps organizations operationalize their
existing investments and arm everyone across the organization
with intelligence. Whether
Data and analytics are the key accelerants of digitalization, transformation and “ContinuousNext” efforts. As a result, data and analytics leaders will be counted upon to affect corporate strategy and value, change management, business ethics, and execution performance.