analytics systems

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By: MoreVisibility     Published Date: Dec 19, 2017
As the approach to strategic business decision making becomes more and more data driven, a method for consolidating our various data sets, which are often spread across multiple systems becomes exceedingly important. Two of the biggest players in data driven decision making are website analytics platforms and customer relationship management systems. The former includes accumulating data on top of the funnel behavior such as site traffic origins, lead generation, content consumption tracking, device usage, and overall site behavior. While the latter has a focus more on bottom of the funnel activity such as lead nurturing, customer status, lifetime value, etc. Lastly, without communication between these two essential platforms, a complete understanding of your customers, from lead to longtime client, may never be possible. A web analytics (Google Analytics) and CRM integration provides you with a 360 degree view of your customer base, so that you can understand not just what PPC efforts
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     MoreVisibility
By: IBM     Published Date: Mar 28, 2016
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
Tags : ibm, talent analytics, kenexa talent insights, workforce science, talent insights
     IBM
By: IBM     Published Date: Jun 13, 2016
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
Tags : ibm, talent acquisition, talent acquisition technology, human resources, recruiting, talent acquisition technology
     IBM
By: IBM     Published Date: Jul 20, 2016
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
Tags : ibm, talent acquisition, talent acquisition technology, human resources, recruiting, talent acquisition technology
     IBM
By: IBM     Published Date: Jul 23, 2015
IBM Predictive Analytics Solution for Schools and Educational Systems,PASSES,is a product and services offering that enables schools to improve student performance.
Tags : predictive analytics solutions, educational systems, compliance, data analytics, student performance, ibm, passes
     IBM
By: IBM     Published Date: Jul 01, 2015
This white paper discusses how enterprise analytics systems can assist provider organizations in building sustainable healthcare systems and achieving their vision for accountable care
Tags : healthcare analytics, data blueprint, enterprise analytics systems, sustainable healthcare systems, mission-critical analysis, integrated data model, operational analytics
     IBM
By: IBM     Published Date: Nov 12, 2015
Hear from the University of Michigan Health System’s CMIO, Dr. Andrew Rosenberg, to learn how this institution is achieving their goal for internal/external operability in support of their enterprise analytics roadmap to support its clinical, research, education and administrative missions. Learn more about the specific challenge's that were solved, how they integrated systems of record with medical devices, and hear about their plans for future integration.
Tags : ibm, big data, enterprise analytics, emr, research interfaces, it management, data management
     IBM
By: IBM     Published Date: Mar 30, 2016
It is important for healthcare organizations to become data driven. IBM can help organizations leverage a wide range of big data to deliver clinical and financial benefits, and the provide the steps organizations should take to become data driven.
Tags : ibm, healthcare, big data, healthcare analytics, enterprise analytics systems
     IBM
By: IBM     Published Date: Mar 30, 2016
This white paper discusses how enterprise analytics systems can assist provider organizations in building sustainable healthcare systems and achieving their vision for accountable care—from near-term demands for regulatory and quality reporting to transforming care delivery.
Tags : ibm, healthcare analytics, healthcare, big data, enterprise analytics systems
     IBM
By: SAS     Published Date: Mar 06, 2018
Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes need not stand in the way. To explore the challenges, opportunities, and value of tax fraud analytics, IIA spoke with Deborah Pianko, a Government Fraud Solutions Architect within the SAS Security Intelligence practice.
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     SAS
By: SAS     Published Date: Mar 06, 2018
The most recent decade has seen rapid advances in connectivity, mobility, analytics, scalability, and data, spawning what has been called the fourth industrial revolution, or Industry 4.0. This fourth industrial revolution has digitalized operations and resulted in transformations in manufacturing efficiency, supply chain performance, product innovation, and in some cases enabled entirely new business models. This transformation should be top of mind for quality leaders, as quality improvement and monitoring are among the top use cases for Industry 4.0. Quality 4.0 is closely aligning quality management with Industry 4.0 to enable enterprise efficiencies, performance, innovation and business models. However, much of the market isn’t focusing on Quality 4.0, since many quality teams are still trying to solve yesterday’s problems: inefficiency caused by fragmented systems, manual metrics calculations, quality teams independently performing quality work with minimal cross-functional own
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     SAS
By: SAS     Published Date: Mar 14, 2014
The solution to operationalizing analytic s involves the effective combination of a Decision Management approach with a robust, modern analytic technology platform. This paper discusses both how to use a focus on decisions to ensure the right problem gets solved and what such an analytic technology platform looks like.
Tags : sas, predictive analytics, technology platform, solution, operationalizing, production systems
     SAS
By: IBM     Published Date: Jan 12, 2016
The core principles of retailing may remain the same but the methods by which retailers must reach out to customers are constantly evolving. As the need for real-time analytics and customer information grows more important, retailers need robust systems to manage the ever-expanding volumes of data.
Tags : customer experience, transaction processing, omnichannel banking, consumer demands, customer interaction, shopper experience, retail
     IBM
By: IBM     Published Date: Feb 04, 2016
Amy McCormick, offering and product manager at IBM, discusses IBM in Healthcare, the value of integration, IBM Healthcare integration, and client successes as well as integration use cases.
Tags : ibm, integration bus, cloud, analytics, mobile, it systems
     IBM
By: Cisco     Published Date: Jul 11, 2016
CTOs, CIOs, and application architects need access to datacenter facilities capable of handling the broad range of content serving, Big Data/analytics, and archiving functions associated with the systems of engagement and insight that they depend upon to better service customers and enhance business outcomes. They need to enhance their existing datacenters, they need to accelerate the building of new datacenters in new geographies, and they need to take greater advantage of advanced, sophisticated datacenters designed, built, and operated by service providers. IDC terms this business and datacenter transformation the shift to the 3rd Platform.
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     Cisco
By: Group M_IBM Q1'18     Published Date: Jan 16, 2018
In our 36-criteria evaluation of security analytics (SA) providers, we identified the 11 most significant ones — BAE Systems, E8 Security, Fortinet, Hewlett Packard Enterprise (HPE), Huntsman Security, IBM, Intel Security, LogRhythm, RSA, Securonix, and Splunk — and researched, analyzed, and scored them. This report shows how each provider measures up and helps security and risk (S&R) professionals make the right choice.
Tags : security analytics platforms, ibm security, security analytics, security and risk
     Group M_IBM Q1'18
By: SAS     Published Date: Jan 04, 2019
How can you open your analytics program to all types of programming languages and all levels of users? And how can you ensure consistency across your models and your resulting actions no matter where they initiate in the company? With today’s analytics technologies, the conversation about open analytics and commerical analytics is no longer an either/or discussion. You can now combine the benefits of SAS and open source analytics technology systems within your organization. As we think about the entire analytics life cycle, it’s important to consider data preparation, deployment, performance, scalability and governance, in addition to algorithms. Within that cycle, there’s a role for open source and commercial analytics. For example, machine learning algorithms can be developed in SAS or Python, then deployed in real-time data streams within SAS Event Stream Processing, while also integrating with open systems through Java and C APIs, RESTful web services, Apache Kafka, HDFS and more.
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     SAS
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
By: Oracle     Published Date: Jan 22, 2014
More than ever, marketers are being held accountable for demonstrating how marketing investments directly translate into sales. That’s why Modern Marketing is fueled by intelligent data analytics and reporting. Implementing the proper analytics systems can help you make critical decisions regarding which parts of your marketing efforts are working or not, and provide the reporting tools necessary to justify those decisions by connecting them directly with pipeline and revenue. By implementing systems for reporting and intelligence, you can better understand the impact that sales and marketing efforts are having on overall business. You then can refine strategies and develop repeatable processes for success.
Tags : analytics, modern marketing, tenants, customer journey, segmentation, lead generation, eloqua, oracle
     Oracle
By: Oracle     Published Date: Jan 22, 2014
More than ever, marketers are being held accountable for demonstrating how marketing investments directly translate into sales. That’s why Modern Marketing is fueled by intelligent data analytics and reporting. Implementing the proper analytics systems can help you make critical decisions regarding which parts of your marketing efforts are working or not, and provide the reporting tools necessary to justify those decisions by connecting them directly with pipeline and revenue. By implementing systems for reporting and intelligence, you can better understand the impact that sales and marketing efforts are having on overall business. You then can refine strategies and develop repeatable processes for success.
Tags : analytics, modern marketing, tenants, customer journey, segmentation, lead generation, eloqua, oracle
     Oracle
By: IBM     Published Date: Jan 02, 2014
Business intelligence derived from sophisticated analytics has given large companies an edge for years. It helps them be more competitive, make information---based decisions faster and better, improves operational efficiencies, and boosts the bottom line. Midsize businesses are increasingly eager to reap similar benefits. Business intelligence derived from sophisticated analytics has given large companies an edge for years. It helps them be more competitive, make information---based decisions faster and better, improves operational efficiencies, and boosts the bottom line. Midsize businesses are increasingly eager to reap similar benefits.
Tags : ibm, business analytics, midsize businesses, geeknet, business intelligence, customer volatility, market volatility, variety of data, it managers, implementing analytics, ba systems, ba solutions, in-house analytics, ba capability, scorecarding, time-to-insight, business risk, business planning, data management
     IBM
By: IBM     Published Date: Jan 09, 2014
To operate effectively in different markets, Finnish manufacturer Meka Pro needed to be able to adjust its pricing and manufacturing strategies to each locality, but had no insight into their own data. Read this case study to learn how Meka Pro used IBM® Cognos® software modules to make faster, better business decisions and improve profitability.
Tags : ibm, meka pro, analytics, improve profitability, manufacturing projects, business strategy, competition support, manufacturing strategies, it systems, increase profitability, customer needs, production processes, automated reports, distinct markets, production costs, job profitability, company growth, secure solutions, business performance, business analytics
     IBM
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