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By: FICO     Published Date: Jun 07, 2016
As any line of business (LOB) leader knows, making customer level decisions that balance risk and profit just keeps getting harder. And even when you think you have the right decisions, turning them into actions can be even trickier. You also need to consider the factors that make smart decisions difficult. Big data. Regulations. Customers who want an offer, fast, or else you’re going to lose them.
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     FICO
By: FICO     Published Date: Jun 07, 2016
As any line of business (LOB) leader knows, making customer level decisions that balance risk and profit just keeps getting harder. And even when you think you have the right decisions, turning them into actions can be even trickier. You also need to consider the factors that make smart decisions difficult. Big data. Regulations. Customers who want an offer, fast, or else you’re going to lose them.
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     FICO
By: FICO     Published Date: Dec 04, 2017
Whether you’re onboarding new customers, cross- or up-selling, getting your supply chain or logistics right, or even collecting unpaid debt, making the best choice of decisions means weighing not just what’s right for your department – but what is best for the business overall. Not to mention what is optimal for your customers and partners. And let’s face it, even with the availability of business intelligence and other analytic tools, it’s hard to know what constitutes the right actions to take in an era where Big Data consistently throws you curveballs. Prescriptive Analytics can help – but for most organizations, there are more questions and concerns than answers about how to implement it successfully. Read our white paper on how Prescriptive Analytics can transform your business decisions and actions – leveraging your existing analytics investment and organizational DNA while helping you drive transparency, customer experience, and profits
Tags : business, results, optimal, customer, experience, tools, analytics, big data
     FICO
By: IBM     Published Date: Jan 09, 2014
While some organizations are already utilizing Big Data or various large enterprise analytics techniques, many more are still working to grasp how these new usage models might help them. There’s a lot of undiscovered value in the vast amounts of data they currently have and the data that they can get from other sources. They know that they can somehow convert this data into insights that will let them ramp up efficiency and be ready for tomorrow today.
Tags : big data, enterprise analytics, red hat, data analytics, powerlinux system, powervm virtualization
     IBM
By: IBM     Published Date: Jul 08, 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.
Tags : ibm, idc, big data, data, analytics, information governance
     IBM
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.
Tags : ibm, idc, big data, data, analytics, information governance
     IBM
By: IBM     Published Date: Jul 14, 2015
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.
Tags : big data, data warehouse, data center, information governance, analytics, big data analytics, business management
     IBM
By: IBM     Published Date: Apr 06, 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.
Tags : big data, analytics, unstructured content, enterprise information, ibm, security, storage
     IBM
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.
Tags : big data, ibm, big data outcomes, information governance, big data analytics
     IBM
By: IBM     Published Date: Oct 21, 2015
IBM SPSS Solutions offer a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end.
Tags : ibm, data, analytics, data scientist, statistician
     IBM
By: IBM     Published Date: Jul 14, 2016
IBM SPSS Solutions offer a straightforward, visual solution that is easy to use on the front end and highly scalable on the back end.
Tags : ibm, big data, data analytics, ibm spss, predictive business
     IBM
By: SAS     Published Date: Mar 01, 2012
This paper shows the results of the survey, as well as trends and expert opinions focusing on why some top organizations are able to successfully extract insight from "big data."
Tags : sas, analytics, business analytics, business intelligence, customer intelligence, data management, fraud & financial crimes, high-performance analytics
     SAS
By: BlackLine     Published Date: Aug 06, 2018
The biotechnology and pharmaceutical industry is among the most heavily regulated industries in the world, challenged by evolving regulations, complex compliance requirements and close regulatory scrutiny. At the same time, companies must address the market pressures of globalization, the use of predictive data analytics and digital technologies, and the industry’s ongoing consolidation. In this challenging environment, confidence in internal controls is crucial.
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     BlackLine
By: SAP     Published Date: May 18, 2014
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management
     SAP
By: IBM     Published Date: Jan 14, 2015
Bloor Research investigated several key critical success factors for big data analytics. Their findings show that governance, data cleansing, and privacy and security have never been more important. Read this paper to see their conclusions.
Tags : big data, data management, data exploration, gain visibility, security extension, business intelligence, explore data, data analytics
     IBM
By: IBM     Published Date: Jan 09, 2015
Early adopters of an extended 360° view will get competitive advantages over their rivals and this will force laggards to adopt a similar approach or lose business.
Tags : ibm, big data, insights, mdm, infosphere, data management, dashboards, value
     IBM
By: IBM     Published Date: Oct 06, 2014
Born from new advances in data processing from IBM Research, IBM® DB2® with BLU Acceleration is a leap forward in database technology that raises the bar for performance and value. BLU Acceleration uses patented technologies to deliver a unique combination of performance, ease of use and cost-efficiency—with 8 to 25 times faster reporting and analytics1 and cases of more than 1,000 times faster answers to queries.2 BLU Acceleration also complements in-memory Dynamic Cubes in IBM Cognos® Business Intelligence with 24 times faster query performance.
Tags : data processing, blu accerleration, database technology, data analytics
     IBM
By: SAS     Published Date: Jun 05, 2017
It’s there for the taking – real-time information about every physical operation of a business. All you need is a key: data analytics.  This paper is based on Blue Hill Research’s interviews of three organizations – a US-based oil and gas company, a US municipality and an international truck manufacturer – each of which heavily invested in IoT analytics. Focusing on the key themes and lessons learned from their initiatives, this paper will help business decision makers make informed investment decisions about the future of their own IoT analytics projects.
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     SAS
By: SAS     Published Date: Mar 06, 2018
The Connected Customer is an individual who is intimately connected to the data, outcomes, decisions, and staff associated with any relationship to an organization. This intensely personal connection is not just a matter of the most recent transaction, but represents a combination of connected data, connected analytics, and collaborative decisions associated with improving the customer’s relationship with the organization over time. In this report, Blue Hill explores the key traits associated with supporting the Connected Customer through the Internet of Things, and provides guidance on why the Internet of Things will be essential across the general business landscape
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     SAS
By: IBM     Published Date: Oct 13, 2017
Blue Hill provides guidance on 1) the Customer Journey leading to a new generation of analytic tools, 2) key value propositions associated with moving to tools such as IBM Cognos Analytics and IBM Watson Analytics, and 3) recommendations for best practices in maximizing the value of insightful and predictive analytics for enterprise data.
Tags : blue hill research, data analytics, ibm cognos, watson analytics, enterprise data
     IBM
By: Group M_IBM Q2'19     Published Date: May 03, 2019
"Managing and securing endpoints with conventional mobile device management (MDM) or enterprise mobile management (EMM) solutions is time-consuming and ineffective. For this reason, global IT leaders are turning towards unified endpoint management (UEM) solutions to consolidate their management of smartphones, tablets, laptops and IoT devices into a single management console. To increase operational efficiency, maximize data security and deliver on their digital transformation goals, they’ll need a UEM platform that does more than just promise success. The answer is a smarter solution, built for today, that brings new opportunities, threats, and efficiency improvements to the forefront. With Watson™, IBM® MaaS360® UEM features cognitive insights, contextual analytics, and cloud-sourced benchmarking capabilities. It helps you make sense of daily mobile details while managing your endpoints, users, apps, document, and data from one platform."
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     Group M_IBM Q2'19
By: TIBCO Software     Published Date: Feb 14, 2019
Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them.
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     TIBCO Software
By: TIBCO Software     Published Date: Feb 14, 2019
Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them. We'll also provide a short overview of TIBCO Statistica, an easy-to-use predictive analytics software solution designed to turn big data into your bigg
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     TIBCO Software
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