analytical

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Published By: Tableau     Published Date: Apr 13, 2018
In this whitepaper, discover the benefits of expanding your analytics toolkit. Combine Excel’s data collection and management capabilities with Tableau’s intuitive, analytical power to transform your raw data into actionable insights. Focus on the questions that take your data beyond the spreadsheet. Read more at about this partnership.
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Tableau
Published By: SPSS     Published Date: Mar 31, 2009
This whitepaper details how predictive analysis can help your business.  Predictive analytics help you make better, faster decisions, giving your organization a significant competitive advantage in the technology sector.
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spss, predictive analysis, roi, data, data driven decision making, mitigate risk, fraud, real time intelligence, data analysis, analytical methods, strategic planning, customer intimacy, crm, best practices, business intelligence, statistics, statistical analysis, data management, decision-making
    
SPSS
Published By: SPSS, Inc.     Published Date: Mar 31, 2009
This whitepaper details how predictive analysis can help your business.  Predictive analytics help you make better, faster decisions, giving your organization a significant competitive advantage in the marketing sector.
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spss, predictive analysis, roi, data, data driven decision making, mitigate risk, fraud, real time intelligence, data analysis, analytical methods, strategic planning, customer intimacy, crm, best practices, business intelligence, statistics, statistical analysis, data management, decision-making, web analytics
    
SPSS, Inc.
Published By: Qlik     Published Date: Aug 12, 2015
This white paper provides 10 tips for ensuring a successful analytics strategy.
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analytical strategy
    
Qlik
Published By: Birst     Published Date: Jul 16, 2013
This whitepaper first looks at the fundamental requirements that a BI solution should deliver to your company. Next, this whitepaper covers the 11 Key Questions that you should be asking of a future BI technology partner. When the BI provider can answer Yes to all of these questions, you have BI that is capable of fulfilling your analytical and reporting needs .
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business intelligence, agile bi, self service bi, bi in the cloud, unified bi, mobile bi, embedded analytics, business analytics, sales analytics, hr analytics, marketing analytics, financial analytics, supply chain analytics, automated data management, dashboards, infrastructure
    
Birst
Published By: Teradata     Published Date: Feb 04, 2015
Optimize your customer experience with Teradata Integrated Marketing Cloud Teradata is recognized for vision, innovation and broad set of marketing capabilities. The 2014 Gartner Magic Quadrant for Integrated Marketing Management is an invaluable resource with insights that can help you execute your marketing initiatives.
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teradata, integrated, marketing, vendors, executional, operational, analytical, campaign, management, resources, research, oracle, infor, microsoft, direxxis, sdl, repoint global, adobe, market, sap
    
Teradata
Published By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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SAS
Published By: Teradata     Published Date: May 02, 2017
A Great Use of the Cloud: Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs. This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.
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data projects, data volume, business data, cloud security, data storage, data management, cloud privacy, encryption, security integration
    
Teradata
Published By: Tealeaf     Published Date: Mar 31, 2011
Given the increasing importance of connected channels to nearly all businesses and government functions, it is no wonder that analysis of these channels is getting more attention.
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tealeaf, advanced site optimization, web analytics, digital analysis application, analytics intelligence, marketing software, customer experience/engagement, business intelligence
    
Tealeaf
Published By: TIBCO Software     Published Date: Oct 03, 2018
The Insurance industry continues to undergo significant transformation, with new technologies, business models, and competitors entering the market at an increasing rate. To be successful in attracting and retaining the most valuable customers, insurance companies must innovate and increase the speed at which they respond to customer demands. Traditionally, the insurance software market was dominated by a handful of specialist vendors with products that were initially expensive, difficult to deploy, costly to maintain, and did not provide the speed needed for today's market. Now there has been a shift away from these "black box" applications to platforms that allow insurers to make their algorithmic IP available to business users, allowing much faster response to business demands. The algorithmic platform approach also comes at a fraction of the cost of black box solutions, while delivering advanced analytical techniques like Machine Learning and Artificial Intelligence (AI).
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artificial intelligence, machine learning, dynamic pricing, predictive claims, real-time fraud, contextual customer experience, operational effectiveness
    
TIBCO Software
Published By: IBM     Published Date: Jan 20, 2016
High-performing finance organizations are analytically advanced.
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ibm, finance analytics
    
IBM
Published By: SAS     Published Date: Aug 27, 2010
Forget spreadsheets. Organizations that are winning in this down economy are using automated analytical tools to take a more scientific approach to decision making through observation, experimentation and measurement to improve their business processes.
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sas, analytics software, business intelligence, data mining, automated analytical tools, computerworld
    
SAS
Published By: IBM     Published Date: Mar 05, 2014
For many years, companies have been building data warehouses to analyze business activity and produce insights for decision makers to act on to improve business performance. These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse. Typically, a history of business activity is built up over a number of years allowing organizations to use business intelligence (BI) tools to analyze, compare and report on business performance over time. In addition, subsets of this data are often extracted from data warehouses into data marts that have been optimized for more detailed multi-dimensional analysis.
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ibm, big data, data, big data platform, analytics, data sources, data complexity, data volume, data generation, data management, storage, acceleration, business intelligence, data warehouse
    
IBM
Published By: IBM     Published Date: May 02, 2014
These traditional analytical systems are often based on a classic pattern where data from multiple operational systems is captured, cleaned, transformed and integrated before loading it into a data warehouse.
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ibm, big data platform, architecting big data, analytics, intelligent business strategies, data complexity, data types, workload growth, workload complexity, big data analytic applications, operational decisions, multi-structured data, querying data, scalable data management, analytical ecosystem, hadoop solutions
    
IBM
Published By: SAS     Published Date: Mar 14, 2014
This Q&A with Tom Davenport, Director of Research for the International Institute for Analytics (IIA), will help you understand how analytics is evolving, where you need to go, and how to get there.
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sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, analytics, analytical study, visualization deployment, deployment, institute for analytics
    
SAS
Published By: SAS     Published Date: Oct 18, 2017
Are you thinking about adding artificial intelligence to your organization? It’s certainly a hot topic and worth the attention. This paper outlines the SAS approach to AI and explains key concepts. It also provides process and implementation tips if you are considering adding AI technologies to your business and analytical strategies.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Information on artificial intelligence (AI) is flooding the market, media and social channels. Without doubt, it’s certainly a topic worth the attention. But, it can be difficult to sift through market hype and grandiose promises to understand exactly how AI can be applied in practical and reliable solutions. Like most technological advances, incorporating new technology into business processes requires significant leadership and effective direction that all stakeholders can easily understand.
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SAS
Published By: SAS     Published Date: May 24, 2018
Information on artificial intelligence (AI) is flooding the market, media and social channels. Without doubt, it’s certainly a topic worth the attention. But, it can be difficult to sift through market hype and grandiose promises to understand exactly how AI can be applied in practical and reliable solutions. Like most technological advances, incorporating new technology into business processes requires significant leadership and effective direction that all stakeholders can easily understand. Great leaders become great by balancing strategy with tactics, future vision with current reality and strengths with weaknesses – all with the goal of accomplishing a clearly defined objective. Great leaders also understand that people are the most valuable resources within their organization. To drive and inspire their success, you must optimize strengths while recognizing inherent weaknesses. Many of our daily human experiences and interactions involve machines or devices of some sort. Technolo
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SAS
Published By: IBM Software     Published Date: Oct 05, 2010
Read how Business analytics investments are more important than ever for business competitiveness and profitability and are becoming increasingly essential to maximize a company's return on investment.
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ibm cognos, business analytics, business intelligence, bi investment, analytical methods, business planning, data analysis
    
IBM Software
Published By: Oracle     Published Date: Apr 23, 2015
This white paper reveals how most advanced organisations are using predictive analytics to reveal where future opportunities lie, and to deploy resources and training accordingly.
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be insight-driven, insights, sales, data, social media, smart use of data, analytic, improve analytical capabilities, web analytics, social media marketing
    
Oracle
Published By: Qlik     Published Date: Jun 24, 2016
This research reveals some key opportunities for these companies to broaden their analytical footprint and magnify the impact of business analytics.
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qlik, analytics, business analytics, finance business intelligence
    
Qlik
Published By: Vertica     Published Date: Aug 15, 2010
If you are responsible for BI (Business Intelligence) in your organization, there are three questions you should ask yourself: - Are there applications in my organization for combining operational processes with analytical insight that we can't deploy because of performance and capacity constraints with our existing BI environment?
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business intelligence, vertica, aggregated data, olap, rolap, sql, query, data warehouse, oltp
    
Vertica
Published By: Teradata     Published Date: Feb 26, 2013
This survey and research report discusses shifts in the data management landscape and the movement to align data with operational and analytical workloads creating the best possible unified data architecture platform. Read on to learn more.
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big data, survey report, data management, landscape, movement, align data, operational and analytics workloads, business intelligence, terdata
    
Teradata
Published By: IBM     Published Date: Mar 12, 2014
Businesses that build strong analytically oriented teams, adopt new ways to use information and take advantage of the power of the cloud can jump ahead of their competition.
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ibm, business analytics, cloud, cloud analytics, cloud platform, data, trends, ibm smartcloud, ibm smarter analytics
    
IBM
Published By: IBM     Published Date: May 07, 2013
Read this whitepaper to learn how businesses take advantage of the power of the cloud to build strong analytically oriented teams, adopt new ways to use information and jump ahead of their competition. Learn more about ‘Big Data and Business Analytics’ through IBM’s latest market leading solutions. Register and attend this complimentary virtual event on June 11 by IBM Business Analytics
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analytics, cloud, business, innovation, computing, information, advantage, power
    
IBM
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