Published By: Cloudian
Published Date: Jun 08, 2015
For companies looking to build their own cloud storage infrastructure, including enterprise IT organizations and cloud service providers or cloud hosting providers, the decision to use cloud and cloud storage for the delivery of IT services is best made by starting with the knowledge and experience gained from previous work. This white paper gathers into one place the essentials of a scale-out storage reference architecture coupled with a real world example from the Cloudian support organization that is using the Cloudian HyperStore appliances and the Hortwonworks Hadoop Data Platform to analyze Big Data logs and troubleshoot customer issues.
This paper discusses the role of the QlikView Business Discovery platform as the foremost user-friendly analytics platform accompanying a Big Data solution. It is written for IT professionals and business leaders who are trying to understand how to gain the most leverage from a Big Data implementation by providing an analytics layer that can both access the data and make it relevant and accessible to the business users in an organization.
Data centers face financial pressure as server-side compute cycles grow while space, power, and funds continue to be limited. Increased demand for compute capacity comes from growth in mobile devices, big data and analytics, cloud computing, media consumption, hosted desktops, and more. Data center managers find themselves maxed out on storage, power, cooling, space, storage, and compute capacity. Even with virtualization, there is a capacity limit to existing resources. Building more facilities is an option, but the emerging technology of ultra-dense computing offers a more compelling financial alternative for many common workloads.
Customer demands have led to recent changes in the data warehouse market. As a result, companies and departments of all types and sizes can leverage advanced data warehouse solutions to facilitate effective use of innovative business analytics and support of various mission-critical business intelligence initiatives.
Without access to a mix of capabilities such as business intelligence, performance management, data quality, extensibility, analytics, location intelligence, and SaaS, organizations will be hard-pressed to optimize efficiency, performance, and the bottom line. The right partnership will drive new revenue and solutions for organizations of all sizes.
Published By: AccelOps
Published Date: Jun 27, 2013
Companies rely on the data center and IT to provide mission-critical services, like email, Web, and voice. However, assuring service delivery and reliability becomes increasingly difficult as growth in data center virtualization, remote access, cloud-based
applications, and outsourced service technologies fuel operational complexity. To improve service reliability, organizations must be able to see and manage all aspects of
performance, availability, and security related to that service.
Find out how the combination of discovery, data aggregation, correlation, out-of-the-box analytics, data management, and reporting can yield a single pane of glass into data center and IT operations and services.
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.
What’s the best analytic approach for your business?
As technology has evolved, so has our ability to process data at an incredible rate, making it possible to perform what has become known as Anticipatory Analytics. While still a relatively new concept, anticipatory analytics is gaining prevalence as a methodology.
If you’re seeking to understand the future needs of your business before they show obvious signs, anticipatory analytics can’t be ignored.
In this document, you’ll learn:
• The advantages of anticipatory analytics
• The key enablers of anticipatory analytics
• How anticipatory can be leveraged for your business
• Why anticipatory can give you first-mover advantage
• When to use anticipatory or predictive analytics, based on your goals
Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry
The democratization of financial services data and technology, together with more intense competition, makes the needs of today’s market participants vastly different from those of previous generations. Firms must locate untapped sources of data for both public and non-public companies. This alternative data, such as payment data and other non-public information, from sources beyond the common channels, can be a predictive indicator of market performance; a difference maker in assisting firms as they develop models to evaluate their investments.
By combining our unique data sets with advanced analytics, traders, analysts and managers can seek predictive signals and actionable information utilizing their own models.
View our research report to learn how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.
Stories and statistics behind successful analytics projects
The adoption of analytics across the enterprise is accelerating, and with good reason. Analytics can offer a competitive advantage by helping to identify growth opportunities, circumnavigate risk and improve customer relationships. These insights are becoming crucial parts of the business strategy for executives representing a wide array of industries.
Check out our latest eBook to see how some of the world’s leading companies are using analytics to meet their needs. You’ll receive diverse examples of how organizations applied the latest statistical methodologies, such as: scorecard build, regression, decision trees, machine learning and material change to uncover meaning in data.
The examples represent global brands across critical industries – Financial Services, Insurance, High-Tech, Aerospace, Manufacturing and others – where analytics helped answer their most challenging questions.
Published By: Progress
Published Date: Mar 06, 2017
The Digital Marketing Maturity Guide helps organizations determine the level of sophistication within their digital marketing operations. Dimensional Research to conducted a global survey of 700 marketing professionals gauging the level of digital marketing maturity across various verticals and company sizes. Use these findings to benchmark your organization against other groups.
The client is one of the world's leading insurance companies. In 2007, the client embarked on a journey to transform its actuarial function. The objective was to improve the operational efficiencies and accuracy of actuarial reporting, while meeting tight timelines and reducing operational costs.
The data integration tool market was worth approximately $2.8 billion in constant currency at the end of 2015, an increase of 10.5% from the end of 2014. The discipline of data integration comprises the practices, architectural techniques and tools that ingest, transform, combine and provision data across the spectrum of information types in the enterprise and beyond — to meet the data consumption requirements of all applications and business processes.
The biggest changes in the market from 2015 are the increased demand for data virtualization, the growing use of data integration tools to combine "data lakes" with existing integration solutions, and the overall expectation that data integration will become cloud- and on-premises-agnostic.
As a financial services provider, you have probably invested hundreds of thousands, if not millions of dollars, in building an analytic infrastructure. But, do your line-of-business analysts and managers have access to the data and insights they need, when they need them?
Three ways Alteryx can help you improve customer experience, manage risk and increase operational efficiency
Case studies on how your peer financial services companies are using self-service data analytics for a competitive edge
The traditional multiple-step, multi-tool legacy approach is a slow, time-consuming, and in most cases, a costly process that prevents organizations from making faster decisions with confidence. Data analysts today need an agile solution that empowers them to take charge of the entire analytics process.
Download The Definitive Guide to Self-Service Data Analytics to:
Understand why traditional analytic tools designed for data scientists are not ideal for data analysts like you
Learn how self-service data analytics delivers the ease of use, speed, flexibility, and scalability you require
See how Alteryx stacks up against traditional data prep and analytics tools
Data Analytics has become critical for many business decision makers. However, many of these managers and data analysts still rely on spreadsheets and other legacy-era tools that fall far short of current needs. As a result, they also rely heavily on a virtual army of data specialists and scientists, working under the auspices of a centralized analytics group, to prepare, blend, analyze, and even report on the critical data they need for decision making.
Download this new paper to get the details behind self-service data analytics, and how it lets business analysts:
Take charge of the entire analytical process, instead of relying on other departments
Overcome limitations of legacy tools to save time and prevent errors
Make more comprehensive and insightful business decisions at speed
In this Executive Brief, we share best practices in how to evaluate and deploy layered controls that will help you develop a holistic approach to controls, investigate and control where risk is introduced, assess your risk appetite and benchmark your cybersecurity posture against others in your industry.
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 customer base.
Published By: Mindfire
Published Date: May 07, 2010
In this report, results from well over 650 real-life cross-media marketing campaigns across 27 vertical markets are analyzed and compared to industry benchmarks for response rates of static direct mail campaigns, to provide a solid base of actual performance data and information.
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.