The more holistic view of risk a property underwriter can get, the better decisions they are likely to make. In order to build up a detailed picture of risk at an individual location, underwriters or agents at coverholders have, until now, had to request exposure analytics on single risks from their portfolio managers and brokers. Also, they had to gather supplementary risk data from a range of external resources, whether it is from Catastrophe Risk Evaluation and Standardizing Target Accumulations (CRESTA) zones to look-ups on Google Maps.
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.
"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.
From intelligent automation to advanced analytics, disruptive technology will enable the finance function to provide all of its services more effectively and efficiently. Find out how technology will change the way finance delivers transactional services, expert services and business partnering,
Read this report to discover:
• how automation in finance can deliver expert services more reliably and with smaller workforces
• how to design an agile workforce of humans and technologies
• how the finance function will shift to be part of a cross-functional analytics model that provides key insights to the business
Today's energy, environment, and utility companies face an unfamiliar landscape in which they must integrate alternative energies, expand situational awareness across the system, and deepen their relationships with customers-all while continuing to deliver reliable, safe, and affordable electricity, gas and water to everyone.By combining predictive analytics with IoT, cloud and mobile technologies, utilities companies can Lower costs, improve operational efficiency and increase equipment reliability.
HfS published the Top 10 Cognitive Assistant Service Provider report that explores the emerging conversational service provider ecosystem across key areas including execution ability, innovation capability, and the voice of the customer. The conversational services called as Cognitive Assistants, go beyond the traditional chatbots and augment human-customer interaction across both front- and back-office business operations. IBM was recognized for its market leadership in Cognitive Assistant that harness the power of IBM Watson capabilities – including NLP, conversation and analytics. HfS also merits IBM for demonstrating the greatest volume and depth of cognitive assistant use cases across industry verticals and enterprise processes.
Organizations continue to rush down the digital transformation path. Whether by modernizing their IT infrastructures, leveraging the cloud, or becoming data-centric and data-driven, organizations must become more agile in their business practices and within their IT infrastructure stack to effectively compete in today’s dynamic business environment. Between the speed and distributed nature of modern businesses, as well as the expectation of instantaneous access to data from everyday users, it’s not surprising that nearly one in three organizations are looking into ways to improve data analytics for real-time business intelligence and customer insight.
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data.
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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.
Published By: Workday
Published Date: Jul 30, 2019
In HR, you have access to an enormous amount of data. How do you filter out the noise from the insights? In this eGuide, and as part of the Getting the Basics Right series, you'll learn how to align on business and HR KPIs including a priority sequenced step-by-step approach to forming these processes.
Consultants and advisors are on the front lines of benefits data analytics. They’re the folks who dig through thousands of rows in a spreadsheet to surface problems and find solutions. In this whitepaper, we’ll dive deep on the advantages consultants offer to employers:
• Access to data analytics, trend analysis, and program measurement
• The expertise to identify, track, and take action on chronic conditions in a member population
• The industry insight into public policy and regulation changes
Artemis Health teamed up with Employee Benefit News to research the current “benefits landscape.” We surveyed self-insured employers to find useful trends, interesting stats, and info on what leaders like you are doing to improve their benefits strategy. In this whitepaper, you'll discover:
• Regional differences in benefits offerings and priorities
• What benefit leaders believe they can accomplish with benefits data analytics
• The features and functionality your peers are looking for when they evaluate a data solution
Let’s face it: in today’s B2B landscape, the buyers call the shots. Buyers today are proactive, research
their own options, and often include many decision makers rather than just one who can be wooed on
a golf course or over dinner.
So, where does that leave the salesperson? To succeed in this new landscape, sales professionals must
understand how the buyer’s journey has changed and unlock the advantages that data analytics and
statistical modeling can offer. Sales and marketing teams must also learn how to align their efforts to
present a truly coordinated experience.
Read this paper to learn how to take advantage of untapped opportunities for helping sales teams
evolve in today’s buyer-empowered landscape.
When data center architecture extends beyond even moderate amounts of sophistication and complexity, it becomes a daunting challenge for operators to understand what is going on by relying on common tools and processes alone.
Read this eGuide for five recommendations that can help you make sense of your data.
As modernized data centers scale up and out, there is a strong potential for growing complexity as well. IT teams need to be vigilant in simplifying architectures and operations as the technological landscape changes.
While the potential of monitoring and analytics in your data center is huge, to take advantage, you'll need an architecture that can handle shifting traffic patterns.
In this expert eGuide, you'll discover three ways of simplifying architectures, and how they can help you reduce complexity, improve workforce efficiency, and ease administration.
Published By: Genesys
Published Date: Jun 19, 2019
Successfully managing a contact center requires a collaborative, multidisciplinary approach to handle a broad range of operational and tactical tasks. Planning, day-to-day operations and quality management must be seamlessly orchestrated, along with human resources functions like recruitment, learning and development, and employee scheduling.
Read this executive brief to learn how to transition to an AI strategy that can take your team – and business results – to the next level. See how you can:
Create an AI strategy with a single data model that includes routing, interaction analytics, forecasting/scheduling and predictive engagement
Harness the power of your data to align customers with the best resource
Drive employee effectiveness by ensuring you hire the right people and manage their performance to drive their success over the long term
From child welfare and public health to combating prescription abuse and improving education, analytics is improving government programs around the world.
The articles in this e-book touch on several areas where analytics is making, or could make, a significant impact in the way governments operate. We’ve pulled together some of our favorite best practices that showcase the role analytics plays in better decision making.
To support open government initiatives and uphold the values of transparency, participation and collaboration in the US, federal agencies today make their data open, or publicly accessible. Citizens can use this open data to assess college affordability, the economy, educational issues, environmental damage, health care, taxes, agriculture, the climate and more. Governments can use APIs to pull this open data into SAS Visual Analytics as a way to identify trends and patterns and obtain all sorts of new insights. With public health surveillance, for example, governments can monitor and evaluate indicators that point to high-risk areas so they’ll know where and how to focus efforts. Such public health surveillance can serve as an early warning system for impending emergencies, document the impact of an intervention, track progress toward public health goals, and clarify health problems to inform public health policies and strategies.
To stay ahead of the competition in a global marketplace, firms are increasingly speeding up operations, in many cases adopting real-time systems and tools to allow for instant decision-making and faster business cycles. Download here to learn how.