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.
The insurance industry stands on the precipice of change, with waves of innovation and disruption driving new possibilities across all departments, including pricing, underwriting, claims, and fraud.
This webinar recording of a live panel debate is ideal for insurance professionals wanting to understand how best to unlock the possibilities created by advanced analytical techniques such as Artificial Intelligence (AI), Machine Learning (ML), and others.
This TIBCO and Marketforce webinar on “The Fourth Industrial Revolution in Insurance” includes speakers Ian Thompson, chief claims officer at Zurich; David Williams, chief underwriting officer at AXA; and Clare Lunn, GI fraud director at LV=. The panel discusses:
Moving towards the algorithmic insurer: the opportunities created by AI and ML
How insurers can become more agile in the face of new innovations and disruptive technologies
How the industry can turn structured and unstructured data into insights
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.
What if you could use just one platform to detect all types of major financial crimes?
One platform to handle the analytical tasks of fraud detection, including:
Data processing and aggregation
Statistical/mathematical/machine learning modeling
One platform that could successfully reduce complex and time-consuming fraud investigations by combining extremely different domains of knowledge including Business, Economics, Finance, and Law. A platform that can cover payments, credit card transactions, and know your customer (KYC) processes, as well as similar use cases like anti-money laundering (AML), trade surveillance, and crimes such as insurance claims fraud.
Learn more about TIBCO's comprehensive software capabilities behind tackling all these types of fraud in this in depth whitepaper.
Security operations centers need advanced analytical tools that can quickly collect and shift through security data. This brief looks at the latest options and processes to speed up detection of advanced threats.
Published By: Anaplan
Published Date: Nov 27, 2017
"The pressure on sales to meet and exceed ever-increasing revenue targets is higher than ever before. At the heart of this challenge lies a complex analytical and modeling problem that involves data spread across many rigid–and usually disconnected–systems, teams, and geographies. Leading companies handle this problem by focusing first on creating a sales performance plan that is data-driven and tied to business objectives.
The research report conducted by Harvard Business Review provides you with how today's sales executives:
• Overcome technology weaknesses to uncover sophisticated analytics
• Change ingrained, cultural tendances of sales organizations
• Adopt dynamic practices to respond to change quicker"
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.
Published By: Vertica
Published Date: Oct 30, 2009
Independent research firm Knowledge Integrity Inc. examine two high performance computing technologies that are transitioning into the mainstream: high performance massively parallel analytical database management systems (ADBMS) and distributed parallel programming paradigms, such as MapReduce, (Hadoop, Pig, and HDFS, etc.). By providing an overview of both concepts and looking at how the two approaches can be used together, they conclude that combining a high performance batch programming and execution model with an high performance analytical database provides significant business benefits for a number of different types of applications.
Join John Myers, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), and Heine Krog Iversen, CEO at TimeXtender, for a discussion on how to improve your organization’s responsiveness to business change and how to adapt to a data-driven environment.
Attendees will gain insight on:
How data-driven organizations are changing business and technology
Which data sources are empowering data-driven organizations and challenging IT departments
How IT departments and analytical teams can get ahead of data-driven change
How data warehouse automation enables not only data-driven business stakeholders, but proactive IT departments
IBM Multiform Master Data Management manages master data domains - customers, accounts, products - that have significant impact on the most important business processes and realize the promise of Service Oriented Architecture (SOA). IBM is the only vendor that delivers an integrated MDM product with significant out-of-the-box functionality for each MDM usage style - collaborative, operational and analytical - across multiple data domains, thereby managing the complete data lifecycle.
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
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.
Published By: SPSS Inc.
Published Date: Mar 31, 2009
In an intensely competitive marketplace, knowledge is power. The more an airline can learn about what its customers like and don't like about its offerings, the more effective it can be at building customer loyalty and maximizing its revenues.
Stop to think about how - and how often - your business interacts with customers. Most organizations believe that only a small fraction of data on interactions generated are effectively put to use. Why is that? Check out this whitepaper to see.
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.
See how you can turn data into actionable insights with predictive analytics. Take our brief assessment to learn which analytical capabilities will enable you to find the greatest value in your data and make confident, accurate business decisions.
In today’s world, the data is flowing from all directions: social media, phones, weather, location and sensor equipped devices, and more. Competing in this digital age requires the ability to analyze all of this data, and use it to drive decisions that mitigate risk, increase customer satisfaction and grow revenue. Using a combination of proprietary software and open source technology can give your data scientists and statisticians the analytical power they need to find and act on insights quickly.
IBM® SPSS® Statistics provides all of the data analysis tools you need, and integrates with thousands of R extensions for maximum power and flexibility. In this next Data Science Central Webinar event, we will show how SPSS Statistics can help you keep up with the influx of new data and make faster, better business decisions without coding.
Many new regulations are spurring banks to rethink how data from across the enterprise flows into the aggregated risk and capital reports required by regulatory agencies. Data must be complete, correct and consistent to maintain confidence in risk reports, capital reports and analytical analyses. At the same time, banks need ways to monetize, grant access to and generate insight from data.
To keep pace with regulatory changes, many banks will need to reapportion their budgets to support the development of new systems and processes. Regulators continually indicate that the banks must be able to provide, secure and deliver high-quality information that is consistent and mature.
For midsize organizations, business analytics offers the crucial ability to transform data into insight and uncover opportunities for growth and competitive advantage. This Aberdeen Sector Insight explores the impact of business analytics in North American midsize organizations.
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.
Self-service analytics implies that users design and develop their own reports and do their own data analysis with minimal support by IT. Most recently, due to the availability of tools, such as those from Qlik, Spotfire, and Tableau, self-service analytics has become immensely popular. Besides powerful analytical and visualization capabilities, they all support functionality for accessing and integrating data sources. With respect to this aspect of data integration four phases can be identified in the relatively short history of self-service analytics. This whitepaper describes these four phases in detail and shows how the tools Cisco Data Preparation (CDP) and Cisco Information Server (CIS) for data virtualization can strengthen and enrich the self-service data integration capabilities of tools for reporting and analytics.
DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Our portfolio of live events, online and print publishing, business intelligence and professional development brands are centred on the complexities of technology convergence. Operating in 42 different countries, we have developed a unique global knowledge and networking platform, which is trusted by over 30,000 ICT, engineering and technology professionals.
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