warehouses

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Published By: Infor     Published Date: Mar 03, 2017
The goal of warehouse operations is perfect order fulfillment: to deliver exactly the items a customer ordered, on time and with optimal efficiency. The warehouse module of an enterprise resource planning (ERP) solution can help smaller warehouses achieve this goal. But ERP warehouse modules have limitations that can make it smarter for companies with more complex operations to turn to the added capabilities of an advanced warehouse management system (WMS).
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enterprise resource planning, erp, erp solutions
    
Infor
Published By: SAS     Published Date: Nov 10, 2014
Learn how data is evolving and the 7 reasons why a comprehensive data management platform supersedes the data integration toolbox that you are using these days.
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sas, data integration, data evolution, comprehensive data, data management, data virtualization, data warehouses, data profiling, metadata management
    
SAS
Published By: IBM     Published Date: Mar 29, 2017
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.
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cloud, analytics, data, organization, ibm
    
IBM
Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
One of the biggest changes facing organizations making purchasing and deployment decisions about analytic databases — including relational data warehouses — is whether to opt for a cloud solution. A couple of years ago, only a few organizations selected such cloud analytic databases. Today, according to a 2016 IDC survey, 56% of large and midsize organizations in the United States have at least one data warehouse or mart deploying in the cloud
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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
One of the biggest changes faces organizations making purchasing and deployment decisions about analytic databases -- including relational data warehouses -- is whether to opt for a cloud solution.
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Group M_IBM Q2'19
Published By: IBM     Published Date: Mar 05, 2014
For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years allowing organizations to use business intelligence (BI) tools to analyze, compare and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.
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ibm, big data, data, big data platform, analytics, data sources, data complexity, data volume, data generation, data management, storage, acceleration, business intelligence, data warehouse
    
IBM
Published By: SAS     Published Date: Sep 08, 2010
This paper describes five business analytics styles used today and the building blocks required in implementing these styles. It is important to consider which of these styles is valid for your organization now and into the future.
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sas, reporting, data warehouses, business activity monitoring, data integration
    
SAS
Published By: SAP     Published Date: May 18, 2014
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.
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sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management, business insights, architecture, business intelligence, big data tools
    
SAP
Published By: BMC ASEAN     Published Date: Dec 18, 2018
Big data projects often entail moving data between multiple cloud and legacy on-premise environments. A typical scenario involves moving data from a cloud-based source to a cloud-based normalization application, to an on-premise system for consolidation with other data, and then through various cloud and on-premise applications that analyze the data. Processing and analysis turn the disparate data into business insights delivered though dashboards, reports, and data warehouses - often using cloud-based apps. The workflows that take data from ingestion to delivery are highly complex and have numerous dependencies along the way. Speed, reliability, and scalability are crucial. So, although data scientists and engineers may do things manually during proof of concept, manual processes don't scale.
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BMC ASEAN
Published By: SAP Inc.     Published Date: Jul 28, 2009
Although many organizations have made significant investments in data collection and integration (through data warehouses and the like), it is a rare enterprise that can analyze and redeploy its accumulated data to actually drive business performance.  In the years to come, as globalization and increased reliance on the Internet further complicate, accelerate and intensify marketplace conditions, actionable business intelligence promises to deliver a formidable competitive advantage to firms that leverage its power.
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sap, business intelligence, business insight, business transparency, cross-enterprise data, inter-enterprise data, data integration
    
SAP Inc.
Published By: WorldTelemetry, Inc.     Published Date: Mar 26, 2007
Business Intelligence Software are applications that build on existing data warehouses and provide analytical processing tools that allow users to more effectively analyze such data. This, in turn, permits businesses to more rapidly develop existing and new analyses and reports for improved decision-making power and information dissemination capacity.
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analytical applications, business analytics, business metrics, business intelligence, enterprise software, bi software, world telemetry, worldtelemetry
    
WorldTelemetry, Inc.
Published By: CA Technologies EMEA     Published Date: Apr 10, 2018
Effective Competition Depends on Continuous Delivery of Quality Software In today’s application economy every company is a software company, no matter what industry it is in: • Shipping companies depend on logistics software to efficiently route packages, arrange drivers and automate warehouses. • Retail companies rely on software to manage inventory, engage with customers online and to give in-store associates the tools they need to answer customer questions on the spot. • Marketing firms lean on applications to gather consumer data and parse it, automate communication with prospects and effectively manage advertising campaigns. The examples are endless. The point is that in order to compete today, every business must be able to quickly build and tweak software to adjust to always evolving market demands. Ultimately, business success depends on faster development iterations while still maintaining the high quality of service expected by customers, stakeholders and end users.
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CA Technologies EMEA
Published By: IBM     Published Date: Oct 06, 2014
Business Intelligence (BI) has become a mandatory part of every enterprise’s decision-making fabric. Unfortunately in many cases, with this rise in popularity, came a significant and disturbing complexity. Many BI environments began to have a myriad of moving parts: data warehouses and data marts deployed on multiple platforms and technologies – each requiring significant effort to ensure performance and support for the various needs and skill sets of the business resources using the environment. These convoluted systems became hard to manage or enhance with new requirements. To remain viable and sustainable, they must be simplified. Fortunately today, we have the ability to build simpler BI technical environments that still support the necessary business requirements but without the ensuing management complexity. This paper covers what is needed to simplify BI environments and the technologies that support this simplification.
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data warehouses, bi environments, bi technologies, faster deployments
    
IBM
Published By: Gigaom     Published Date: Oct 15, 2019
Data pipelines are a reality for most organizations. While we work hard to bring compute to the data, to virtualize and to federate, sometimes data has to move to an optimized platform. While schema-on-read has its advantages for exploratory analytics, pipeline-driven schema-on-write is a reality for production data warehouses, data lakes and other BI repositories. But data pipelines can be operationally brittle, and automation approaches to date have led to a generation of unsophisticated code and triggers whose management and maintenance, especially at-scale, is no easier than the manually-crafted stuff. But it doesn’t have to be that way. With advances in machine learning and the industry’s decades of experience with pipeline development and orchestration, we can take pipeline automation into the realm of intelligent systems. The implications are significant, leading to data-driven agility while eliminating denial of data pipelines’ utility and necessity. To learn more, join us fo
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Gigaom
Published By: Safe Software     Published Date: Aug 21, 2009
Spatial data warehouses are becoming more common as government agencies, municipalities, utilities, telcos and other spatial data users start to share their data. This paper illustrates some of the issues that arise when undertaking data replication and data sharing.
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data warehousing, share data, data management, data distribution, data sharing, replication, safe, safe software
    
Safe Software
Published By: Pentaho     Published Date: Apr 28, 2016
As data warehouses (DWs) and requirements for them continue to evolve, having a strategy to catch up and continuously modernize DWs is vital. DWs continue to be relevant, since as they support operationalized analytics, and enable business value from machine data and other new forms of big data. This TDWI Best Practices report covers how to modernize a DW environment, to keep it competitive and aligned with business goals, in the new age of big data analytics. This report covers: • The many options – both old and new – for modernizing a data warehouse • New technologies, products, and practices to real-world use cases • How to extend the lifespan, range of uses, and value of existing data warehouses
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pentaho, data warehouse, modernization, big data, bug data analytics, best practices
    
Pentaho
Published By: IBM     Published Date: Jun 15, 2009
The ability to make quick, well-informed decisions is critical to competitiveness and growth for most companies. Read the white paper to see how Data warehouse solutions can deliver business insight across virtually any business process or function. And also how they're particularly valuable for understanding sales, profiling customers and analyzing business costs.
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ibm, data warehouses, warehouse, data, data solutions, sales, business costs, olap, online, analytical processing, customer relationship management, crm, ibm db2 warehouse, regulatory, compliance
    
IBM
Published By: Google     Published Date: Oct 26, 2018
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions. This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
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Google
Published By: Google     Published Date: Jan 24, 2019
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions. This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
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Google
Published By: SRC,LLC     Published Date: Jun 01, 2009
Companies spend millions of dollars every year on building data warehouses, buying business intelligence (BI) software tools and managing their analytic processes in the hope of gaining consumer insight and winning market share. Yet, many companies fail to realize the full benefits of their technology investments because they are hamstrung by the layers of expertise and the complexity of technology tools needed to integrate various data warehouses and associated tools within their existing analytic environments. Since analysis is only as good as the accessibility, timeliness and accuracy of the information being analyzed, the interoperability of any data warehouse with any analytic environment is essential to achieving insightful, actionable analysis and making better decisions.
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src, enterprise, streamline, analytics, economy, analytic imperative, business intelligence, seamless, data warehouse, interoperability, analytic environment, data assets, report generation, output options, total cost of ownership, tco, roi, return on investment, olap
    
SRC,LLC
Published By: IBM     Published Date: Feb 02, 2009
A comprehensive solution for leveraging data in today's financial industry. Most organizations realize that the key to success lies in how well they manage data—and the banking industry is no exception. From customer statistics to strategic plans to employee communications, financial institutions are constantly juggling endless types of information.
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ibm, information management software, leveraging data, dynamic warehousing, data management, improve customer service, real-time risk analysis, analytics capabilities, information on demand framework, ibm db2 warehouse, ibm master data management server, ibm omnifind, ibm industry data models, ibm balanced warehouses, oltp-based transactional data
    
IBM
Published By: IBM     Published Date: Feb 02, 2009
A comprehensive solution for leveraging data in today's retail environment. From customer data to product placement statistics, retail organizations are constantly juggling information. As the sheer amount of data continues to grow, it becomes increasingly difficult to manage. Not only does data come in many different forms—such as reports, memos and e-mails—but often it’s scattered across multiple repositories.
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ibm, ibm balanced warehouses, ibm master data management server, ibm omnifind, ibm industry data models, dynamic warehousing, retail buyer’s guide, leveraging data, customer data, product placement statistics, data management, scalable system, customer relationships, cross-sell, up-sell opportunities, db2 warehouse, oltp-based transactional data server, information management software
    
IBM
Published By: IBM     Published Date: Dec 30, 2008
Most long-standing data warehouses are designed to support a relatively small number of users who access information to support strategic decisions, financial planning and the production of standard reports that track performance. Today, many more users need to access information in context and on demand so that critical functions are optimized to run efficiently. Learn how to create a roadmap for a truly dynamic warehousing infrastructure, and move ahead of your competition with your business intelligence system
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warehousing infrastructure, ibm, business intelligence, data warehouse, dynamic warehousing, data warehouse model, master data
    
IBM
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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