tdwi

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Published By: SAP     Published Date: May 18, 2014
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.
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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: SAS     Published Date: Apr 10, 2019
El cómputo en la nube es una tendencia importante que ofrece ventajas en flexibilidad, escalabilidad y agilidad. Aun así, ha habido un gran despliegue publicitario. La realidad es que, hasta hace poco, la nube ha tardado en despegar para desplegar soluciones de inteligencia empresarial y analítica. Las organizaciones están preocupadas por la seguridad, el rendimiento, la funcionalidad y otros problemas críticos. TDWI Research está experimentando un cambio significativo a medida que las organizaciones muestran voluntad de experimentar con la nube. Este informe expone las experiencias de las organizaciones con la inteligencia de negocios, la analítica y la nube, así como lo que debe tomarse en cuenta respecto a este tipo de plataformas.
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SAS
Published By: IBM     Published Date: Jul 24, 2008
Tune into this TDWI Radio News interview, with Eric Kavanagh, to hear Karen Parrish, vice president of business intelligence solutions for IBM, as she explains Big Blue's take on the evolving industry of data warehouse appliances.
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IBM
Published By: SAS     Published Date: Mar 20, 2019
What’s on the chief data and analytics officer’s agenda? Defining and driving the data and analytics strategy for the entire organization. Ensuring information reliability. Empowering data-driven decisions across all lines of business. Wringing every last bit of value out of the data. And that’s just Monday. The challenges are many, but so are the opportunities. This e-book is full of resources to help you launch successful data analytics projects, improve data prep and go beyond conventional data governance. Read on to help your organization become truly data-driven with best practices from TDWI, see what an open approach to analytics did for Cox Automotive and Cleveland Clinic, and find out how the latest advances in AI are revolutionizing operations at Volvo Trucks and Mack Trucks.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
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, networking, it management, wireless, platforms, data management, business technology
    
Pentaho
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: IBM     Published Date: Apr 01, 2016
Traditional business intelligence (BI) looks backward at what has happened. In today’s marketplace, enterprises need to look ahead. In this eGuide from TDWI, you'll discover how advances in predictive analytics are enabling organizations to use insights about the past and present to make accurate predictions about the future.
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ibm, business intelligence, ibm connect, predictive analytics, enterprise applications, business technology
    
IBM
Published By: IBM     Published Date: May 19, 2015
Traditionally, business intelligence (BI) has looked backward at what has happened. In today’s marketplace, enterprises need to look ahead. From predictive to prescriptive intelligence, TDWI and IBM look at what businesses need most.
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business intelligence, prescriptive intelligence, predictive analytics, immersive user experience, analytic technology, informative visualization
    
IBM
Published By: SAS     Published Date: Jan 17, 2018
The Internet of Things can bring big benefits. But what exactly is IoT, and how are different industries taking advantage of it? This TDWI e-book explores in detail what IoT and the Industrial IoT (IIoT) do for retailers, the automotive industry, state and local governments working with utilities firms, and the manufacturing industry. Common themes include connectedness, data-driven insights, predictive capabilities and transformation.
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SAS
Published By: IBM     Published Date: Nov 16, 2015
The report is sponsored by vendor firms Actian Corporation, Cloudera, Exasol, IBM, MapR Technologies, MarkLogic, Pentaho, SAS, Talend, and Trillium Software.
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ibm, tdwi, enterprise, information technology, data management, business technology, hadoop, data security
    
IBM
Published By: Informatica     Published Date: Apr 16, 2007
On-demand computing is apparently in demand, at least in the eyes of data integration specialist Informatica Corp. The Switzerland of data integration, as some have called it, just announced availability of its first on-demand offering, which is specifically designed to handle data from Software-as-a-Service (SaaS) stalwart, Salesforce.com.
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salesforce, crm tools, crm, customer relationship management, salesforce.com, crm tool, on-demand, ondemand, on demand, podcast, informatica
    
Informatica
Published By: Informatica     Published Date: Sep 28, 2007
On-demand computing is apparently in demand, at least in the eyes of data integration specialist Informatica Corp. The Switzerland of data integration, as some have called it, just announced availability of its first on-demand offering, which is specifically designed to handle data from Software-as-a-Service (SaaS) stalwart, Salesforce.com.
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salesforce, crm tools, crm, customer relationship management, salesforce.com, crm tool, on-demand, ondemand, on demand, podcast, informatica, data management
    
Informatica
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.
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big data, big data analytics, data warehousing technologies, data storage, business intelligence, data integration, enterprise applications, data management
    
Pentaho
Published By: Pentaho     Published Date: Nov 04, 2015
This 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 based on TDWI research plus survey responses from 325 data management professionals across 13 industries. It also covers Hadoop best practices and provides an overview of tools and platforms that integrate with Hadoop.
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pentaho, analytics, platforms, hadoop, big data, predictive analytics, data management, networking, it management, knowledge management, enterprise applications, data center, data science
    
Pentaho
Published By: SAS     Published Date: Jan 17, 2018
This TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for embedded analytics across both organizational and technical dimensions, including organizational culture, infrastructure, data and processes. It looks at challenges and how organizations are overcoming them, and offers recommendations and best practices for successfully operationalizing analytics in the organization.
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SAS
Published By: Oracle Corporation     Published Date: May 11, 2012
This white paper presents two case studies that illustrate how Oracle Exadata increased storage capacity for data warehouses by 150%, reduced operational and database running costs by 50%, and on average improved database query performance by 10x.
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oracle, data warehousing, database, exadata, database machine, infrastructure, operation, operation costs, mobile, growth, payback, architecture, demands, enterprise applications, data management
    
Oracle Corporation
Published By: IBM     Published Date: Jan 18, 2013
This report offers recommendations for achieving greater return on investment (ROI) from customer analytics processes.
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analytics, social media, best practices, crm, marketing
    
IBM
Published By: SAS     Published Date: Mar 06, 2018
There is a lot of excitement in the market about artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Although many of these technologies have been available for decades, new advancements in compute power along with new algorithmic developments are making these technologies more attractive to early adopter companies. These organizations are embracing advanced analytics technologies for a number of reasons including improving operational efficiencies, better understanding behaviors, and gaining competitive advantage.
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SAS
Published By: Pentaho     Published Date: Nov 04, 2015
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
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pentaho, analytics, platforms, hadoop, big data, predictive analytics, networking, it management, knowledge management, data management, data science
    
Pentaho
Published By: Pentaho     Published Date: Apr 28, 2016
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
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pentaho, best practices, hadoop, next generation analytics, platforms, infrastructure, data, analytics in organizations, it management, wireless, enterprise applications, data management, business technology, data center
    
Pentaho
Published By: SAS     Published Date: Jan 04, 2019
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment. Newer examples of operational analytics include support for logistics, customer call centers, fraud detection, and recommendation engines to name just a few. Embedding analytics is certainly not new but has been gaining more attention recently as data volumes and the freq
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SAS
Published By: SAS     Published Date: Oct 18, 2017
Organizations need to accelerate the pace with which they realize business value from data. The focus is on improving “time to value,” which is the length of time it takes from the beginning of a project to the delivery of anticipated business value. This TDWI Best Practices Report focuses on realizing value from BI and analytics and how organizations can accelerate the path to higher value. The report looks at multiple factors impacting the ability of organizations to quickly derive greater value from data and analytics, including the organizational issues, practices, and development methods that are often just as important as keeping pace with technological innovation.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
With decisions riding on the timeliness and quality of analytics, business stakeholders are less patient with delays in the development of new applications that provide reports, analysis, and access to diverse data itself. Executives, managers, and frontline personnel fear that decisions based on old and incomplete data or formulated using slow, outmoded, and limited reporting functionality will be bad decisions. A deficient information supply chain hinders quick responses to shifting situations and increases exposure to financial and regulatory risk—putting a business at a competitive disadvantage. Stakeholders are demanding better access to data, faster development of business intelligence (BI) and analytics applications, and agile solutions in sync with requirements.
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SAS
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