decision analytics

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Published By: IBM     Published Date: Jul 23, 2015
This white paper explains how predictive analytics can be used in education to improve student retention and includes IBM SPSS case studies.
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data-driven decision-making, student life cycle, predictive analytics, higher education, student retention, data analytics, ibm spss
    
IBM
Published By: SAS     Published Date: Mar 06, 2018
With decisions riding on the timeliness and quality of analytics, business stakeholders are less patient with delays in the development of new applications that provide reports, analysis, and access to diverse data itself. Executives, managers, and frontline personnel fear that decisions based on old and incomplete data or formulated using slow, outmoded, and limited reporting functionality will be bad decisions. A deficient information supply chain hinders quick responses to shifting situations and increases exposure to financial and regulatory risk—putting a business at a competitive disadvantage. Stakeholders are demanding better access to data, faster development of business intelligence (BI) and analytics applications, and agile solutions in sync with requirements.
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SAS
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
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: 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
Published By: SAS     Published Date: Jun 06, 2018
A multitude of “things” generate floods of big data – cars, wearables, machines and appliances. Wouldn’t you like to sift through that noise and become an organization that relies on data to make fact-based decisions? Learn about the three foundations of becoming data-driven – data management, analytics and visualization – and how they can increase profitability, boost performance, raise market share and improve operations. Read about hurdles to becoming a data-driven organization and learn best practices from others. Then get a glimpse of what the future holds with the Internet of Things (IoT), edge analytics, artificial intelligence (AI) and other technology innovations.
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SAS
Published By: SAS     Published Date: Aug 27, 2010
This report describes a research project that investigated how organizations are attempting to improve specific decisions.
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sas, data management, decision making, tom davenport, decision making, iia, international institute of analytics
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This paper will discuss the barriers to data-driven decision making for midsized businesses, and how experts and non-experts alike can use SAS Visual Analytics to unlock the value of data – including big data – to increase revenue, cut operational costs and better manage their business.
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sas, bottom line, midsized businesses, leveraging data, data management, internal and external, decision making, big data
    
SAS
Published By: IBM     Published Date: Mar 04, 2014
In today’s mobile, connected era, customers expect perfection from their service providers. With competitors only a click (or tap) away, companies have a strong incentive to deliver flawless operations. Online retailers have “set a high bar” in the way that they engage customers throughout the entire sales process—not just during the commercial transaction, but before, during and after the transaction. Can your insurance company meet the challenge?
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smarter process approach, agility, business process management, bpm, insurance, case management, operational decision management, analytics
    
IBM
Published By: Datawatch     Published Date: Mar 21, 2014
Big Data is not a new problem. Companies have always stored large amounts of data—structured like databases, unstructured like documents—in multiple repositories across the enterprise. The most important aspect of big data is not how big it is, or where it should be stored, or how it should be accessed. It’s the efficacy of business intelligence tools to plumb its depths for patterns and trends, to derive insight from it that will give companies competitive advantage in an increasingly challenging business climate. Visualization allows companies to analyze big data in real-time across a variety of sources in order to make better business decisions.
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visual data discovery, decision making software, data variety, business analysis, data visualization, big data, business analytics, business intelligence
    
Datawatch
Published By: Datorama     Published Date: Aug 26, 2014
The extreme complexity of today’s marketing landscape, combined with the abundance of choices available to consumers, means marketers need to approach each marketing decision as intelligently as possible. Download this whitepaper to learn how marketers can utilize omni-channel analytics to garner strategic insights and guide them in the decision making process.
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marketing business intelligence, saas marketing optimization, measuring marketing performance, roi analytics, automated report generator, performance based marketing, online marketing data, roi metrics
    
Datorama
Published By: IBM     Published Date: Nov 12, 2013
In today’s mobile, connected era, customers expect perfection from their service providers. With competitors only a click (or tap) away, companies have a strong incentive to deliver flawless operations. Online retailers have “set a high bar” in the way that they engage customers throughout the entire sales process—not just during the commercial transaction, but before, during and after the transaction. Can your insurance company meet the challenge?
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smarter process approach, agility, business process management, bpm, insurance, case management, operational decision management, analytics
    
IBM
Published By: IBM     Published Date: Dec 10, 2015
Ovum has produced this Ovum Decision Matrix to identify how the leading customer analytics vendors stack up against each other in terms of their technology, execution of strategy, and market impact.
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IBM
Published By: IBM     Published Date: Feb 16, 2016
Analytics has been a hot topic for a long time. In insurance, it's been a core area of focus forever, and descriptive analytics have been followed by predictive analytics. But companies are just beginning to explore prescriptive analytics for decision management, and now comes a whole new era: Cognitive.
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analytics, cognitive computing, decision management, prescriptive analytics, data, insurance, data insights
    
IBM
Published By: IBM     Published Date: Feb 05, 2015
Revealing new insights into agency performance, claims processing, customer profitability and exposure.
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agency performance, claims processing, customer probability, advanced analytics
    
IBM
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
    
IBM
Published By: IBM     Published Date: Feb 04, 2016
IBM Operational Decision Manager Advanced applies insights and analytics to operational decisions by bringing together data from different sources and looking at historical trends and patterns to determine the next best action. With IBM Operational Decision Manager Advanced, you gain scope, scale, speed, and simplicity. You can now capture events, build context, and apply it to operational decisions in real-time. This helps detect situations as they occur – presenting risks or opportunities – to enable action.
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analytics, ibm, operational decision manager, operations, brand, data, big data, trends
    
IBM
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
    
IBM
Published By: Group M_IBM Q2'19     Published Date: May 21, 2019
ODM is the evolution of business rules management. It provides a complete, easy-to-use system for automating day-today operational decisions and allows businesspeople and IT to collaborate on business rules by using an interface and a language that are comfortable and intuitive for both. ODM not only allows you to automate your business rules, but also it enables you to apply insights and analytics to operational decisions by bringing together data from different sources and looking at historical trends and patterns to determine the next best action. It ensures that you’re making the right decisions at the right time, when it can make a difference. What can this mean for your business? Adopting operational decision management can: • Improve customer centricity (acquisition and retention) by engaging individuals at the right time with the right offers
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Group M_IBM Q2'19
Published By: Cisco     Published Date: Jul 11, 2016
Companies rely on an expanding set of applications to compete in today's rapidly evolving business environment: - They rely on a fast-growing array of applications and devices (email, collaboration tools, and smartphones/tablets) to communicate and conduct business with customers and business partners. - They are creating, collecting, and repurposing large, unstructured data sets in life sciences, geophysics, media, and manufacturing. - They are collecting, storing, and analyzing more social and sensor-generated data about environments, products, customers, and transactions. The promise of better and faster data-driven decision making based on all this information is pushing big data and analytics (BDA) technology to the top of executive agendas. To succeed, CIOs must place a laserlike investment focus on datacenter solutions that allow them to deliver scalable, reliable, and flexible infrastructure for fast-growing BDA environments. Read more to learn how!
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Cisco
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
Published By: SAS     Published Date: Aug 17, 2018
This SAS and Intel collaborated piece demonstrates the value of modernizing your analytics infrastructure using SAS® software on Intel processing. Readers will learn: • Benefits of applying a consistent analytic vision across all functions within the organization to make more insight-driven decisions. • How IT plays a pivotal role in modernizing analytics infrastructures. • Competitive advantages of modern analytics.
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SAS
Published By: SAS     Published Date: Dec 20, 2018
Think of the self-service things you use in a day. Gas pumps. ATMs. Online apps for shopping. They’re convenient and easy to use. People choose what they want, when they want – without involving others in their minute-to-minute decisions. What if your organization could treat data discovery and analytics the same way? SAS has combined two of its visual solutions to do just that. SAS Visual Analytics and SAS Visual Statistics share the same web-based interface to provide self-service data exploration and easy-to-use interactive predictive analytics in a collaborative environment. This white paper takes a look at this convergence and outlines how these products can be used together so that everyone, even nontechnical users, can investigate data on their own, create analytical models and uncover new insights that drive competitive differentiation. Your analytics journey just got a lot easier.
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SAS
Published 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
Published By: SAS     Published Date: Mar 20, 2019
What’s on the chief data and analytics officer’s agenda? Defining and driving the data and analytics strategy for the entire organization. Ensuring information reliability. Empowering data-driven decisions across all lines of business. Wringing every last bit of value out of the data. And that’s just Monday. The challenges are many, but so are the opportunities. This e-book is full of resources to help you launch successful data analytics projects, improve data prep and go beyond conventional data governance. Read on to help your organization become truly data-driven with best practices from TDWI, see what an open approach to analytics did for Cox Automotive and Cleveland Clinic, and find out how the latest advances in AI are revolutionizing operations at Volvo Trucks and Mack Trucks.
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SAS
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