data warehousing

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Published By: TIBCO     Published Date: Apr 08, 2013
As the volume of available data increases, and we have new ways of extracting insights from data, it is valuable to take a step back and examine the impact of these insights on integration and company actions. Join us for this webinar and learn how you can use big data in your organization.
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big data, integration, architecture, database, data warehousing, operations management
    
TIBCO
Published By: Amazon Web Services     Published Date: Oct 09, 2017
As easy as it is to get swept up by the hype surrounding big data, its just as easy for organisations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt. However, given big data's power to transform business, it's critical that organisations overcome these challenges and realise the value of big data. The cloud can help organisations to do so. Drawing from IDG's 2015 Big Data and Analytics Survey, this white paper analyses the top five challenges companies face when undergoing a big data initiative and explains how they can effectively overcome them.
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amazon, web services, intel, migration, data warehousing, organization optimization, security, software
    
Amazon Web Services
Published By: Teradata     Published Date: Jan 16, 2015
This Neil Raden paper describes the current need for data warehousing, why SAP® BW is an incomplete choice and how Teradata Analytics for SAP® Solutions is a superior option. Download now!
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teradata, sap solutions, data warehouse, extracted data, data management
    
Teradata
Published By: Dassault Systèmes     Published Date: Jul 21, 2017
Obtaining a first-mover competitive advantage or faster time-to-market requires a new wave in analytics. Dassault Systèmes remains a leading innovator in Product Lifecycle Management (PLM) and has invested heavily in analytical technologies to further drive business benefits for its customers in the related areas of planning, simulation, insight and optimization. This white paper examines the challenges peculiar to PLM and why Dassault Systèmes’ EXALEAD offers the most appropriate solution. It also clearly positions EXALEAD PLM Analytics alongside related technologies like BI, data-warehousing and Big Data solutions. Understand and implement PLM Analytics to access actionable information, support accurate decision-making, and drive performance.
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product solutions, lifecycle management, tech products, data management tools, pdm, plm, process automation, product development speed, manufactures
    
Dassault Systèmes
Published By: Teradata     Published Date: May 02, 2017
Read this article to discover the 4 things no data warehouse should be without.
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cloud data, cloud security, cloud management, storage resource, computing resources, data warehousing, data storage, cloud efficiency
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
Constellation Research presents case studies of two companies that cut costs, gained flexibility, and eased administration by adding cloud data warehouse capacity to their on-premises environment. Both of these deployments: • Solved problems and yielded expected and unexpected benefits • Required some adjustments to administrative processes and expectations • Led to hybrid architecture as being an invaluable aspect of their respective data warehousing strategies Download the case studies now to explore each company’s journey, understand recurring themes, and walk away with actionable insights.
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cloud data, online marketing, customer acquisition, mobile marketing, social marketing, data warehouse, data storage, data collection
    
Teradata
Published By: IBM     Published Date: Jul 05, 2016
Cloud-based data warehousing as-a-service, built for analytics
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ibm, dashdb, data, analytics, data warehouse, cloud, analytics, business insights, knowledge management, data management, business technology, data center
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business.
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IBM
Published By: IBM     Published Date: Jul 06, 2017
DB2 is a proven database for handling the most demanding transactional workloads. But the trend as of late is to enable relational databases to handle analytic queries more efficiently by adding an inmemory column store alongside to aggregate data and provide faster results. IBM's BLU Acceleration technology does exactly that. While BLU isn't brand new, the ability to spread the column store across a massively parallel processing (MPP) cluster of up to 1,000 nodes is a new addition to the technology. That, along with simpler monthly pricing options and integration with dashDB data warehousing in the cloud, makes DB2 for LUW, a very versatile database.
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memory analytics, database, efficiency, acceleration technology, aggregate data
    
IBM
Published By: Forrester     Published Date: May 10, 2012
In the never-ending race to stay ahead of the competition, companies are developing advanced capabilities to store, process, and analyze vast amounts of data from social networks, sensors, IT systems, and other sources to improve business intelligence and decisioning capabilities.This report will help security and risk professionals understand how to control and properly protect sensitive information in this era of big data.
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data-centric security, information protection, information management, data protection, security, risk and compliance, data warehousing
    
Forrester
Published By: Teradata     Published Date: Jul 30, 2013
This paper examines the obstacles that make interactive customer management a challenge for many insurance carriers. It describes how they can create a more customer-centric business by using a sophisticated analytics platform to uncover valuable insights into their business and provides concrete examples of specific areas where insurers gain value. The adoption of a customer-centric approach need not be accomplished all at once. Rather, it can be managed and self-funded by following a roadmap that delivers incremental capabilities and revenue.
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insurance policies, integrated data warehousing, interactive customer management, customer management, insurance carriers
    
Teradata
Published By: IBM     Published Date: Oct 17, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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IBM
Published By: Informatica     Published Date: Oct 28, 2011
In this White Paper, Bloor Research director Philip Howard discusses how Data Replication can help you deliver active data warehousing for analytics.
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data management
    
Informatica
Published By: Oracle     Published Date: Nov 06, 2012
The purpose of this white paper is to take a time-to-business-value look at financial services data warehousing technologies with a focus on the selection process and how it should take deeper considerations of the real-world implementation hurdles.
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oracle, data, analytical data, data marts, industry-specific data warehouse, financial services, business technology
    
Oracle
Published By: Oracle     Published Date: Nov 06, 2012
The purpose of this white paper is to take a time-to-business-value look at financial services data warehousing technologies with a focus on the selection process and how it should take deeper considerations of the real-world implementation hurdles.
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oracle, data, analytical data, data marts, industry-specific data warehouse, financial services
    
Oracle
Published By: SAP Inc.     Published Date: Jun 16, 2009
This paper reviews the current status of MDM, and offers suggestions for planning, building and deploying an MDM environment.
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sap, master data management, mdm, business intelligence, data warehousing, sor, system of record, enterprise mdm, mds, mms
    
SAP Inc.
Published By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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AWS
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set: • Enterprise-class relational database query and management system • Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools • Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Sep 05, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technolog
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Amazon Web Services
Published By: InfoSys     Published Date: Apr 08, 2013
Big Data: Too Much information, Too Little illumination; As enterprises go about their Big Data adoption journey, there are many pressing questions at hand. What are the Big Data capabilities they desire?
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big data, business intelligence, analytics, data warehousing, infosys
    
InfoSys
Published By: AWS     Published Date: Jun 20, 2018
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated q
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AWS
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