house analytics

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Published By: Group M_IBM Q119     Published Date: Mar 04, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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Group M_IBM Q119
Published By: AWS     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
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AWS
Published By: Oracle CX     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle CX
Published By: Oracle PaaS/IaaS/Hardware     Published Date: Jul 25, 2017
"With the introduction of Oracle Database In-Memory and servers with the SPARC S7 and SPARC M7 processors Oracle delivers an architecture where analytics are run on live operational databases and not on data subsets in data warehouses. Decision-making is much faster and more accurate because the data is not a stale subset. And for those moving enterprise applications to the cloud, Real-time analytics of the SPARC S7 and SPARC M7 processors are available both in a private cloud on SPARC servers or in Oracle’s Public cloud in the SPARC cloud compute service. Moving to the Oracle Public Cloud does not compromise the benefits of SPARC solutions. Some examples of utilizing real time data for business decisions include: analysis of supply chain data for order fulfillment and supply optimization, analysis of customer purchase history for real time recommendations to customers using online purchasing systems, etc. "
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Oracle PaaS/IaaS/Hardware
Published By: Teradata     Published Date: Jan 30, 2015
This TDWI Checklist Report discusses adjustments to DW architectures that real-world organizations are making today, so that Hadoop can help the DW environment satisfy new business requirements for big data management and big data analytics.
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data, data warehouse, hadoop, hadoop ecosystem, data architectures, data archiving, advanced analytics
    
Teradata
Published By: Teradata     Published Date: Jan 30, 2015
Data from the Internet of Things makes an integrated data strategy more vital than ever.
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teradata, internet, things, iot, data, warehouse, analytics, patchwork, value, development, integration, supply chain, inventory, sales, market demand, channel partners, deployment, hadoop, aster, discovery
    
Teradata
Published By: EMC Corporation     Published Date: Jul 07, 2013
Forward-looking enterprises know there's more to big data than strong and managing large volumes of information. Big data presents an opportunity to leverage analytics and experiment with all available data to derive value never before possible with traditional business intelligence and data warehouse platforms. Through a modern, big data platform that facilitates self-service and collaborative analytics across all data, organizations become more agile and are able to innovate in new ways.
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enterprises, storage, information management, technology, platform, big data analytics, emc, self service, business intelligence
    
EMC Corporation
Published By: Workday     Published Date: Aug 07, 2018
Just like a house needs a good foundation, a finance system needs the right technology. Workday took a fresh approach to technology to give finance a distinct advantage in analytics, close and consolidation, growth, and more. See how Workday helps finance become a strategic partner.
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technology, analytics, strategic partner
    
Workday
Published By: Teradata     Published Date: Jan 20, 2015
This Neil Raden and Teradata webinar explores: The business values gained from an integrated view of SAP® and non-SAP® data; Existing solutions and challenges; Requirements for the optimal BI and analytics platform, and; A new solution that simplifies and enhances BI analytics for SAP® ERP data.
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data warehouse, teradata, business value, analytics platform, erp data
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
Should the data warehouse be deployed on the cloud? Read this IDC Research Spotlight to learn more.
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data warehouse, data storage, data management, data analytics, data preparation, data integration, system integration
    
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
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
In an environment where data is the most critical natural resource, speed-of-thought insights from information and analytics are a critical competitive imperative.
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ibm, data warehouse, big data, analytics, data warehouse, business intelligence
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
While any number of reasons can prompt a change in data warehouse solutions, there are four key facts you need to know to help you make the right choice: 1. Complexity stifles business 2. Speed is business-friendly 3. Cost reduction is crucial 4. Analytics: The key to current and future success This e-book will explore those facts and explain why they are essential when evaluating your data warehouse options.
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IBM
Published By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements. To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
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cloud, database, hybrid cloud, database platform
    
Group M_IBM Q1'18
Published By: Pentaho     Published Date: Aug 29, 2016
This white paper covers the many options available for modernizing a data warehouse.
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Pentaho
Published By: Teradata     Published Date: Jun 12, 2013
Health plans and insurers know that to thrive over the next 3-5 years, they must dramatically improve their ability to engage with individual consumers. The combination of Teradata products; an integrated data warehouse, Aster big data analytics and Aprimo integrated communication management, creates actionable analytic capabilities unparalleled in its ability to help companies achieve these goals. this white paper details how health plans and insurers can use Teradata to succeed in today’s healthcare environment.
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healthcare, data, insights, health plans, integrated data
    
Teradata
Published By: IBM     Published Date: Nov 08, 2017
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, ibm, deployment models
    
IBM
Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18
Published By: Group M_IBM Q119     Published Date: Mar 11, 2019
In this paper, we focus on the DWA and how it has evolved over the years since its introduction. The XDW architecture is then described, in which the need to maintain the data warehouse is documented while adding new components and capabilities to extend the analytical capabilities. This section also discusses the appropriate usage of appliances within the XDW. The rest of the paper covers the benefits from implementing the DWA, the selection considerations for them and what the future holds for them.
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Group M_IBM Q119
Published By: Epicor     Published Date: Jul 06, 2017
Distribution is being transformed by digital innovations. More and more distributors are replacing manual, paper-driven processes with digital tools and automation that allow employees to work more efficiently and effectively in the warehouse and beyond. Efficiency doesn’t just benefit employees—modern technology can help keep customers and vendors happier with up-to-date information accessible anytime. Epicor Prophet 21 software was designed to meet the unique needs of distributors. It offers robust features including eCommerce, mobility tools, customer relationship management (CRM), wireless warehouse management, purchase management, and analytics to make more informed business decisions. With deep industry knowledge and best practices built into features throughout the solution, Prophet 21 can deliver the digital transformation your organization needs to grow.
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erp software, enterprise resource planning software, distribution software, prophet 21, p21, inventory management
    
Epicor
Published By: TIBCO Software     Published Date: Mar 04, 2019
A perfect storm of legislation, market dynamics, and increasingly sophisticated fraud strategies requires you to be proactive in detecting fraud quicker and more effectively. TIBCO’s Fraud Management Platform allows you to meet ever-increasing requirements faster than traditional in-house development, easier than off-the-shelf systems, and with more control because you’re in charge of priorities, not a vendor. All this is achieved using a single engine that can combine traditional rules with newer predictive analytics models. In this webinar you will learn: Why a fraud management platform is necessary How to gain an understanding of the components of a fraud management platform The benefits of implementing a fraud management platform How the TIBCO platform has helped other companies Unable to attend live? We got you. Register anyway and receive the recording after the event.
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TIBCO Software
Published By: Oracle     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
Tags : 
    
Oracle
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: 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
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
Tags : 
    
Amazon Web Services
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