The Total Economic Impact Of HP Return On Information
In April 2013, HP commissioned Forrester Consulting to examine the total economic impact and potential return on investment (ROI) enterprises may realize by implementing the HP Vertica Analytics Platform and engaging HP for Hadoop solutions. The purpose of this study is to provide readers with a framework with which to evaluate thepotential financial impact of HP Hadoop and HP Vertica on their organizations.
The HP Return on Information initiative covers several big data solutions including SAP HANA and Autonomy (see HP Return on Information: Overview for details). For the purposes of this study, only Hadoop and the HP Vertica Analytics Platform are covered in the following financial model and case study.
The New Economics of Enterprise Data Warehousing
The new economics of data warehousing provide attractive alternatives in both costs and benefits. While big data gets most of the attention, evolved data warehousing will play an important role for the foreseeable future. In order to be relevant, data-warehouse design and operation need to be simplified, taking advantage of greatly improved hardware and software. On the benefits side, careful location of processing to separate data integration and stewardship activities from operational and analytical activities is clearly required.
Capitalize on the Untapped Potential of Sensor Data
In the coming years, enormous volumes of machine-generated sensor data from the Internet of Things will fuel a wide range of data-driven products, services, and business processes. But there’s a catch: To capitalize on the opportunity, your organization needs to have the right data analytics platform in place. This white paper details the use of sensor data across different industries and how to harness its power to improve products and services.
From Big Data to Knowledge
Read this interactive white paper to explore the entire value chain that communications service providers (CSPs) are transforming data into knowledge. Specifically, this white paper covers information sources, data collection tools, analytics platforms for quick data access, and finally the use cases with the presentation and visualization of results and predictions. Four primary use cases demonstrate the need for the HP Vertica Analytics Platform – subscriber network usage analytics, HP mobile experience personalization, HP ad experience personalization, and Machine to Machine (M2M).
5 Signs You Might be Outgrowing Your MySQL Data Warehouse
Most of us remember a favorite pair of pants or shirt we had as kids that seemed to ﬁt ﬁne one day, and the next time we put it on, we realized that they were suddenly much too small. Outgrowing things was a way of life back then, an inevitable step in the grand scheme and one that always seemed to lead to the next favorite shirt or toy. This is not an attempt to trivialize data warehouse and data mart systems, but they too evolve and mature, and one day you might wake up and realize that the MySQL data warehouse that you have so faithfully supported and maintained is just too small for your current analytics needs. This paper details the ﬁve most common signs that it may be time to consider replacing a MySQL system.
Winter Corp Report
In virtually ever industry, analytics has enabled new strategies, new products, new revenues and new efficiencies.
At the same time, the volume of data available for analysis has grown at an extraordinary rate. The largest data warehouses in operation today store petabytes of data. They are about 100 times as large as those of five years ago. All indications are that data growth will continue to accelerate, resulting in an even larger increase in the scale of data warehouses in the next several years.
The Disruptive Power of Big Data
The fact that the volume of data in the world is exploding is interesting. What’s compelling, though, is that businesses are harvesting and using this data to improve market knowledge, enhance competitiveness, and transform their operations and even their business models. It’s a disruptive change for business. And like the Internet and the computer itself, enterprises that learn to use Big Data for business advantage will thrive in the Big Data era. Those that do not will find themselves outpaced by more nimble competitors and risk extinction.
Make All Your Information Matter – Hadoop and HP Vertica Analytics Platform
HP Vertica Analytics Platform and Hadoop are highly complementary systems for Big Data analytics. HP Vertica Analytics Platform is ideal for interactive, real-time analytics and the Hadoop open-source platform is well suited for batch-oriented data processing.
But why and when do you use this combination together and how?
Download this new white paper and learn how this powerful combination enables you to extract higher levels of value from massive amounts of structured, unstructured, and semi-structured data, while accelerating analytics.
From Big Data to Knowledge: Analytic Use Cases for Communication Service Providers
This white paper looks at specific use cases in the CSP industry to examine how these companies can leverage the inordinate amount of information they take in about their customers. Four cases outline how they created more value for their customers and their businesses.
From Big Data to Knowledge: Value Chain for Communication Service Providers
Big data is an opportunity for communications service providers (CSPs) to create the intelligence for operating network more efficiently, to analyze the success of the services that CSPs are offering, and to create a better personal experience for their customers. This white paper addresses how the mass amounts of data coming in can be leveraged for business value.
Turning big data into big value with the HP Vertica Analytics Platform and R
The integration of R—a no-charge offering—into the HP Vertica Analytics Platform lets your enterprise sift through
your data quickly to find anomalies using advanced data mining algorithms provided by R. Now, no complex import,
export, or extract/transform/load (ETL) jobs are required. By integrating data mining into your processes, people in your organization are poised to make better business decisions, in less time, based on data mining results.
Every year, the scammers, skimmers, and schemers get better at what they do. And as their methods of defrauding businesses—and avoiding detection—get more clever, they become even tougher to catch. That’s why your business needs a new breed of fraud detection and prevention, one that uses powerful real-time pattern recognition to stay one step ahead of the crooks.
Innovative technology for big data analytics
Regulatory compliance, increased competition, and other pressures mean you need to accumulate and analyze larger and larger quantities of data. Many companies now have hundreds of terabytes of data to store and analyze.
Yet, most database management innovation has not kept pace. Performing ad hoc queries on such large data volumes does not come naturally for existing database management systems (DBMS), which use a row-oriented design for writeintensive transaction processing rather than for read-intensive analytics. Desperate for better performance, many roworiented DBMS customers spend millions of dollars every year on stop-gap measures, such as adding database administrator (DBA) resources, creating and maintaining OLAP cubes, or replacing their DBMS with expensive and proprietary data warehouse appliances.
Using credit card transaction records to detect skimming
The techniques described in this paper are not rocket science; they’ve been around in one form or the other since the beginning of the 19th century.5 However, their application to the problem of credit card fraud is relatively new. Why? Until recently, there hasn’t been a simple way to handle the volume of data required to do so.
Delivering a Comprehensive Analytics Framework for Converged Services
Communications companies are currently embroiled in a series of initiatives to enable the convergence of systems to deliver converged services – the utilization of a single network to transport all information and services (voice, data and video) by encapsulating the data into packets. While the movement to converged systems is inevitable and will be commoditized, it’s the “analytics” that are computed and utilized that will be the key competitive differentiator. This paper discusses the analytics framework required for today’s converged services.
A Primer on Web Analytics
Web Analytics is the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage. This paper will provide a broad overview of web analytics, explain the importance of integrating clickstreams with additional data sets, review the challenges of moving beyond metrics to insights, and outline how the Vertica Analytics Platform enables customers to succeed when facing the challenges related to web analytics.
Leveraging Social Media Analytics for Competitive Advantage
This paper outlines the advantages of using the Vertica Analytics Platform to collect, parse and process social media data using Natural Language Processing (NLP) techniques. Vertica is a “New SQL” Relational Database Management System (RDBMS) currently used by media and entertainment companies who understand and act on social media data as well as those organizations powering the social media world. Vertica is a linearly scalable, high performance Big Data platform that supports standard SQL, User Deﬁned Functions (UDF’s) and connectors to other data sources including Hadoop.
The Impact of Social Graphing Analysis on the Bottom Line: How Zynga Performs Graph Analysis with the Vertica Analytics Platform
A lot can be learned from the world’s largest social gaming company, as Zynga’s user community offers a microcosm of several real-world social graphing scenarios. This white paper describes how Zynga uses the Vertica Analytics Platform to improve its business and game features.
Analytic Architectures: Approaches to Supporting Analytics Users and Workloads
Wayne Eckerson, Director of Research, Business Applications and Architecture Group at TechTarget, writes about the four main types of intelligences designed to turn data into information.
Business Intelligence Is In The Details
Neil Raden describes how to create a no-compromise, 24 x 7 data warehouse that dramatically improves load and query speed, scalability and cost effectiveness.