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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, web analytics, customer experience/engagement, business intelligence, reputation monitoring
    
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
    
IBM
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: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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SAS
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: 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: Mar 31, 2016
In every industry today, businesses feel a fierce urgency to become customer-centric. They want to know what they can do to preserve and expand existing customer relationships and attract the best new customers.
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best practices, customer relations, business intelligence, analytics, big data, data management, customer data management, marketing, sales, traditional marketing
    
SAS
Published By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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SAS
Published By: SAS     Published Date: Apr 25, 2017
This Checklist explores how AI can be used to enhance marketing analytics and to help companies both better understand their customers and deliver a great customer experience. It also provides practical advice on how organizations can use what they may already be doing to become more effective in marketing.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
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: SAS     Published Date: Oct 18, 2017
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: 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: 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: 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: 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
    
Informatica
Published By: SAS     Published Date: Apr 25, 2017
This TDWI Checklist provides seven steps your organization can follow to apply a balanced governance strategy as you expand your use of self-service visual analytics and discovery.
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SAS
Published By: SAS     Published Date: Mar 07, 2018
The Internet of Things can bring big benefits, but what is IoT and how are retailers taking advantage of it? For answers, download this whitepaper.
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SAS
Published By: Oracle Corporation     Published Date: Sep 26, 2012
TDWI Checklist Report: Consolidating Data Warehousing on a Private Cloud
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tdwi, checklist, report: consolidating, data, warehousing, private cloud
    
Oracle Corporation
Published By: Pentaho     Published Date: Aug 22, 2016
This white paper covers the many options available for modernizing a data warehouse.
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big data, data integration, bi systems, hadoop
    
Pentaho
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
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