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: 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: 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: 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: 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: Oracle Analytics     Published Date: Oct 06, 2017
Business decision making is undergoing a data-infused renaissance. Organizations are tired of the limitations of spreadsheets and dealing with long IT business intelligence (BI) development cycles just to gain access to the data they need now. Fortunately, with the advent of visual analytics and discovery tools (many offered in the cloud), the journey to data insight is getting simpler and faster. Rather than trying to divine meaning from a group of predefined reports or simple static dashboards, visual analytics helps users gain insights from data more quickly using intuitive data visualization. Increasingly, visual analytics tools provide easy-touse data preparation features for better data access. They support collaboration, mashups, and storytelling. TDWI Research sees growing interest in applying more modern, up-to-date tools for working with data.
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Oracle Analytics
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, data management, data center
    
Teradata
Published By: IBM     Published Date: May 16, 2016
Our objective in this Checklist Report is to share best practices that will position the reader to take advantage of a cloud-based data warehousing solution.
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ibm, tdwi, checklist report, data warehousing, cloud, analytics, analytic architecture, warehousing, data management
    
IBM
Published By: IBM     Published Date: Apr 18, 2017
Learn from this TDWI paper how right-sized information governance can improve the success of data warehousing or big data analytics initiatives, and how a chief data officer can help organizations to appreciate the value of data and its importance to their decisions and operations.
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system integration, data governance, data optimization, data efficiency, data currency, data lineage, data security, data integration
    
IBM
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: 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: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. Download this white paper to find out how you can improve your data preparation for business analytics.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.
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Waterline Data & Research Partners
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
Predictive analytics is on the verge of widespread adoption as enterprises become more interested in deploying predictive capabilities. In fact, a recent 2017 TDWI education survey, ranked predictive analytics the top analytics-related topic respondents wanted to learn about. The TDWI Navigator Report- Predictive Analytics provides a comprehensive overview of the state of the predictive analytics market. Download this report to better understand: Opportunities and obstacles of implementing predictive analytics Market forces and trends driving the adoption of predictive analytics Features and market landscapes that define predictive analytics Download this report today to get a better sense on how your organization can take advantage of predictive analytics to drive change in your business.
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Alteryx, Inc.
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: 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: MicroStrategy     Published Date: Mar 15, 2018
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MicroStrategy
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: Oracle     Published Date: May 11, 2012
This TDWI Checklist Report makes a case for private database clouds, especially as a platform for consolidated BI and DW applications.
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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
Published By: Trillium Software     Published Date: May 19, 2011
This report provides recommendations for improving the quality of operational data, which in turn contributes to an organization's drive toward operational excellence.
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trillium software, tdwi checklist report, philip russom, operational data quality, opdq, analytic data, the data warehousing institute, enterprise data quality
    
Trillium Software
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: 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
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
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