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.
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.
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-c entric 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.
The outcome of any big data analytics project, however, is only as good as the quality of the data being used. As big data analytics solutions have matured and as organizations have developed greater expertise with big data technologies he quality and trustworthiness of the data sources themselves are emerging as key concerns. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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.
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
This paper explores the results of a survey, fielded in April 2013, of 304 data managers and professionals, conducted by Unisphere Research, a division of Information Today Inc. It revealed a range of practical approaches that organizations of all types and sizes are adopting to manage and capitalize on the big data flowing through their enterprises.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Over the course of several months in 2011, IDC conducted a research study to identify the opportunities and challenges to adoption of a new technology that changes the way in which traditional business solutions are implemented and used. The results of the study are presented in this white paper.
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
This white paper, produced in collaboration with SAP, provides insight into executive perception of real-time business operations in North America. It is a companion paper to Real-time Business: Playing to win in the new global marketplace, published in May 2011, and to a series of papers on real-time business in Europe, Asia-Pacific and Latin America.
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.
Published By: Pentaho
Published Date: Feb 26, 2015
This TDWI Best Practices report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies.
Published By: Pentaho
Published Date: Nov 04, 2015
Amid unprecedented data growth, how are businesses optimizing their data environments to ensure data governance while creating analytic value? How do they ensure the delivery of trusted and governed data as they integrate data from a variety of sources?
If providing appropriately governed data across all your data sources is a concern, or if the delivery of consistent, accurate, and trusted analytic insights with the best blended data is important to you, then don’t miss “Delivering Governed Data For Analytics At Scale,” an August 2015 commissioned study conducted by Forrester Consulting on behalf of Pentaho.