analytics organizations

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Creating Competitive Advantage with Business Intelligence Pervasiveness - IDC - Whitepaper

Published By: SAP     Published Date: Apr 07, 2011
There is growing evidence of the competitive value of BI and analytics solutions. An IDC study of North American and European organizations found that the median ROI of BI and analytics projects was 112%.
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business intelligence, competitive advantage, analytics solutions, idc, roi, business activity monitoring, business analytics
    
SAP

Predictive Analytics and Artificial Intelligence Technology: A Sirius Perspective

Published By: Mintigo     Published Date: Sep 05, 2018
One of the most common use cases for AI in B2B is to make predictions about which accounts are most likely to buy and which leads are most likely to convert. However, use cases for AI are being extended beyond predictive account and lead scoring to include decision-making and process automation as well. Download this SiriusDecisions technology perspective on Predictive Analytics and Artificial Intelligence Technology to learn more. This paper will cover: • The benefits, evolution and capabilities of AI technology solutions for B2B organizations • The core and extended capability groups of AI • The business priorities supported by AI Fill out the form to get your free copy!
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Mintigo

The Landscape of Self-service Analytics

Published By: IBM     Published Date: Apr 15, 2016
This report examines the current state of self-service analytics across all industries and company sizes. It also highlights the technology decisions and analytical performance of organizations that reported high levels of self-service in their analytical use base.
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ibm, analytics, self-service analytics, business analytics, analytical performance, business technology
    
IBM

Successful healthcare analytics begin with the right data blueprint

Published 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
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healthcare analytics, data blueprint, enterprise analytics systems, sustainable healthcare systems, mission-critical analysis, integrated data model, operational analytics
    
IBM

Successful healthcare analytics begin with the right data blueprint

Published 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.
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ibm, healthcare analytics, healthcare, big data, enterprise analytics systems
    
IBM

A New Breed of BI: Self-Service Analytics That Your Business and IT Users Will Love

Published By: SAS     Published Date: Mar 06, 2018
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and analytics. And you can do it through an interface that anyone can use. At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days. Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
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SAS

Data Lakes, Purposes, Practices, Patterns and Platforms

Published By: SAS     Published Date: Mar 06, 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. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS

IIA: IoT in Retail: Engaging the Connected Customer

Published By: SAS     Published Date: Mar 06, 2018
The Internet of Things enables retailers to do three basics better and faster: 1) Sensing who customers are and what they’re doing, 2) Understanding customer behavior and preferences, and 3)Acting on that insight to create a more engaging customer experience. - There are high-potential IoT applications in supply chain, in “smart store” operations, and especially in providing an engaging experience to the “connected customer.” IoT data can anticipate where the customer is headed and how to meet her there. - Much of the IoT ground, in both data management and analytics, may be unfamiliar. Retailers and their IT organizations have to be realistic about the technological challenges, their own capabilities, and where they need assistance. - To differentiate through IoT, focus on the analytics. Devices and their data — and even their platforms — are commodities. Advantage goes to the retailer who does the most with the data to engage the connected customer.
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SAS

Advancing Your Business Intelligence With Location Analytics

Published By: SAS     Published Date: Apr 04, 2018
Location analytics is the process of integrating geographical data into business intelligence (BI) and analytics-led decision making. Location analytics creates meaningful insight from relationships found in geospatial data to solve a broad variety of business and social problems. Location data is found everywhere – with an item or a device, in a conversation or behavior, in machines or sensors, tied to a customer or competitor, attached to a database record or recorded from vehicles or other moving objects. Organizations want to take advantage of location data to improve decisions, create better customer engagement and experiences, reduce risks and automate business processes.
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SAS

MIT Sloan Management Review. Analytics as a Source of Business Innovation

Published By: SAS     Published Date: Jun 06, 2018
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally. Learn more about key findings, including: Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics. Return on investment for analytics stems from the governing and sharing of data throughout the organization. Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
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SAS

Harvard Business Review Analytic Services: Real-Time Analytics

Published By: SAS     Published Date: Jun 06, 2018
Today’s consumers expect immediate, personalized interactions. To meet these expectations, companies must differentiate their brands through timely, targeted and tailored customer experiences based on real-time data analytics. This report, sponsored by SAS, Intel and Accenture and conducted by Harvard Business Review Analytic Services, looks at how businesses are using advanced customer data analytics, along with real-time analytics and real-time marketing, to enhance their customers’ experiences. Learn why organizations that place a high value on real-time capabilities still struggle to achieve them, what companies can do to ensure success as they adopt and implement real-time analytics solutions, and what benefits successful companies are already seeing.
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SAS

The Current State of Business Analytics: Where Do We Go From Here?

Published By: SAS     Published Date: Mar 01, 2012
This white paper reveals the results of a Bloomberg Businessweek Research Services survey of 930 respondents globally on the current state of business analytics within organizations. You'll discover how and why the use of analytics is growing!
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sas, analytics, business analytics, business intelligence, customer intelligence, data management, fraud & financial crimes, high-performance analytics
    
SAS

Five Steps to Embedding Business Analytics into Your Organizational Processes

Published By: IBM     Published Date: Nov 08, 2013
Because all processes should be aligned to customer metrics, process improvement is an important goal for organizations in every industry. This paper illustrates the impact analytics can make on business processes through real-world examples based on IBM client experiences, and describes the steps organizations can take to refine quality, warranty, financial, inventory and other processes that are essential to achieving operational excellence.
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ibm, embedding business analytics, organizational processes, business analytics, building smarter processes, insight and alignment, consistent information, accurate information
    
IBM

The Benefits of an Integrated Approach to Security in the Cloud

Published By: IBM     Published Date: Feb 11, 2015
As cloud computing gains more traction, more businesses are beginning to align their security strategy to better manage the privacy and compliance challenges of this new deployment model. Indeed, cloud models are being used not only to add compute and storage resources, they are also becoming an imperative for data analytics and mobility. In this paper, we will look at how the growing adoption of cloud computing is changing the way organizations are implementing security.
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security strategies, ibm, deployment model, cloud security, data analytics, security, business analytics, data protection
    
IBM

Big Data, Good Data, Bad Data-the link between information governance and Big Data outcomes

Published By: IBM     Published Date: Feb 24, 2015
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, ibm, big data outcomes, information governance, big data analytics, it management, data management, data center
    
IBM

Redefining Networks For Cloud, Analytics, Mobile, Social and Security

Published By: IBM     Published Date: Oct 06, 2015
This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.
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networks, ibm, redefining networks, cloud, analytics, mobile, social, security
    
IBM

Three questions every Mobile leader needs to ask (and answer) about their mobile apps

Published By: IBM     Published Date: Oct 08, 2015
This 30-minute webcast is about analytics because without end-to-end analytics, organizations have little to no insight into what’s really happening with their mobile apps.
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ibm, mobile, security, app, analytics, application development, software development, mobile computing
    
IBM

Three questions every Mobile leader needs to ask (and answer) about their mobile apps

Published By: IBM     Published Date: Jan 04, 2016
This 30-minute webcast is about analytics because without end-to-end analytics, organizations have little to no insight into what’s really happening with their mobile apps.
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ibm, mobile, application, development, mobile apps, analytics, software development, enterprise applications
    
IBM

Redefining networks for cloud, analytics, mobile and social

Published By: IBM     Published Date: Feb 29, 2016
This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.
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ibm, network, cloud, analytics, mobile, social, big data, it security
    
IBM

Big Data, Bad Data, Good Data - The Link Between Information Governance and Big Data Outcomes

Published By: IBM     Published Date: Oct 18, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, enterprise applications
    
IBM

From Business Insight To Business Action

Published By: IBM     Published Date: Apr 03, 2017
Businesses today certainly do not suffer from a lack of data. Every day, they capture and consume massive amounts of information that they use to make strategic and tactical decisions. Yet organizations often lack two critical capabilities when it comes to making the right decisions for the business: the ability to make accurate predictions about the future, and to then use those predicted insights in conjunction with organizational goals to identify the best possible actions they should take. The combination of predictive analytics and decision optimization provides organizations with the ability to turn insight into action. Predictive analytics offers insights into likely scenarios by analyzing trends, patterns and relationships in data. Decision optimization prescribes best-action recommendations given an organization’s business goals and business dynamics, taking into account any tradeoffs or consequences associated with those actions.
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predictive analytics, analytics, data analytics, financial marketing, market analytics, data resources, data optimization
    
IBM

How Can Analytics Help Organizations Recruit Top Talent?

Published By: Cornerstone OnDemand     Published Date: Jul 28, 2017
Finding and retaining great talent today is challenging. Not only do Millennials expect more from employers, they expect more from their careers. The growing talent shortage is nothing to sneeze at either: in 2016, 68% of surveyed HR professionals found it difficult to fill full-time positions. Finally, there’s the shocking skills shortage: 84% of HR professionals reported seeing applied skill deficits (such as problem-solving skills) in candidates in the past 12 months.
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millennials, human resources, recruiting tips, career growth
    
Cornerstone OnDemand

An Open Analytics Platform Extends Open Source Technologies with Governance and Scalability

Published By: SAS     Published Date: Jun 05, 2017
Data professionals now have the freedom to create, experiment, test and deploy different methods easily – using whatever skill set they have – all within one cohesive analytics platform. IT leaders gain the ability to centrally manage the entire analytics life cycle for both SAS and other assets with one environment. Organizations get faster results and better ROI from analytics efforts.
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SAS

Data Lakes, Purposes, Practices, Patterns and Platforms

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

TDWI: Data Preparation Challenges facing every organization

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
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