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Published By: CA Technologies EMEA     Published Date: Aug 03, 2017
Tenuto conto del fatto che la GDPR è stato annunciato formalmente solo di recente, si evidenzia un buon livello di consapevolezza tra i partecipanti. Una volta informati sul regolamento, l'88% degli intervistati ha dichiarato che la propria azienda deve affrontare difficoltà tecnologiche per la compliance alla GDPR. Il percorso verso la compliance è percepito come molto laborioso.
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generazione di dati sintetici, virtualizzazione di servizi, controllo e protezione dei database, masking dei dati, warehouse dei dati, ca technologies, gdpr
    
CA Technologies EMEA
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: Oracle Hardware     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 Hardware
Published By: Oracle Hardware     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle Hardware
Published By: Attivio     Published Date: Aug 20, 2010
With the explosion of unstructured content, the data warehouse is under siege. In this paper, Dr. Barry Devlin discusses data and content as two ends of a continuum, and explores the depth of integration required for meaningful business value.
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attivio, data warehouse, unified information, data, content, unstructured content, integration, clob
    
Attivio
Published By: Attivio     Published Date: Aug 20, 2010
Current methods for accessing complex, distributed information delay decisions and, even worse, provide incomplete insight. This paper details the impact of Unified Information Access (UIA) in improving the agility of information-driven business processes by bridging information silos to unite content and data in one index to power solutions and applications that offer more complete insight.
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attivio, data warehouse, unified information, data, content, unstructured content, integration, clob
    
Attivio
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
    
SAP
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
Tags : 
    
Oracle CX
Published By: Oracle CX     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle CX
Published By: Cognizant     Published Date: Oct 03, 2017
Impact that situation awareness can have on extended supply chain operations w/focus on logistics companies
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data science, predictive analytics, applications services, systems integration, business process management, digital transformation, social mobile analytics cloud (smac), integrated cloud services
    
Cognizant
Published By: Cognizant     Published Date: Sep 19, 2017
Focus on creating consistent terminology in order to generate insights from the digital data encircling employees, partners, processes and customers.
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data science, predictive analytics, applications services, systems integration, business process management, digital transformation, social mobile analytics cloud (smac), integrated cloud services
    
Cognizant
Published By: Cognizant     Published Date: Sep 21, 2017
Additional insight on Forbes Research that ends with four “How To Get Started” steps.
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data science, predictive analytics, applications services, systems integration, business process management, digital transformation, social mobile analytics cloud (smac), integrated cloud services
    
Cognizant
Published By: Cognizant     Published Date: Sep 21, 2017
The impact that state-of-the-art simulation and modeling techniques can have on supply chain operations.
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data science, predictive analytics, applications services, systems integration, business process management, digital transformation, social mobile analytics cloud (smac), integrated cloud services
    
Cognizant
Published By: Dell EMC     Published Date: Nov 09, 2015
While the EDW plays an all-important role in the effort to leverage big data to drive business value, it is not without its challenges. In particular, the typical EDW is being pushed to its limits by the volume, velocity and variety of data. Download this whitepaper and see how the Dell™ | Cloudera™ | Syncsort™ Data Warehouse Optimization – ETL Offload Reference Architecture can help.
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Dell EMC
Published By: Collaborative Consulting     Published Date: Dec 23, 2013
There are some surprisingly straightforward reasons behind the glitches, delays, and cost-overruns that can bedevil data warehouse initiatives. ...The first is simply confusing expectations with requirements. But four other troublemakers can also lead to big problems for developers, IT departments, and organizations seeking to maximize the business value of information.
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collaborative consulting, data warehouse, failed projects, business intelligence, business solution, meet expectations, big data, profile importance
    
Collaborative Consulting
Published By: Collaborative Consulting     Published Date: Jan 15, 2014
When a pharmaceutical company discovered its risks under the new Patient Protection and Affordable Care Act, it turned to Collaborative to comb and consolidate its data. The result: compliance and insight into new business opportunities, too, through a company-wide business data warehouse and enhanced business intelligence.
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collaborative consulting, data, data warehouse, complicane, risk management, business intelligence, consolidation, infrastructure
    
Collaborative Consulting
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