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Published By: MicroStrategy     Published Date: Aug 21, 2019
To survive and thrive in an era of accelerating digital disruption, organizations require accessible data, actionable insights, continuous innovation, and disruptive business models. It’s no longer enough to prioritize and implement analytics – leaders are being challenged to stop doing analytics just for analytics’ sake and focus on defined business outcomes. In addition, these leaders are being challenged to bring predictive capabilities and even prescriptive recommended actions into production at scale. As AI and accelerated growth and transformation become top of mind, many enterprises are realizing that their current segmented analytics approach isn’t built to last, and that real transformation will require proper endto- end data management, data security, and a data processing platform company-wide. The year 2019 will be a turning point for many organizations that realize being data-driven doesn’t guarantee future success.
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MicroStrategy
Published By: IBM Business Analytics     Published Date: Jun 23, 2011
David Axson, author of The Management Mythbuster, explains how insightful analytics and a systematic approach to risk management can drive dramatic improvements in the quality and value that finance organizations deliver.
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ibm business analytics, david axson, enterprise risk management, erp, enterprise resource planning, insightful analytics, finance function
    
IBM Business Analytics
Published By: IBM Software     Published Date: Jun 08, 2011
This white paper explores how predictive analytics helps marketing organizations increase ROI by executing highly targeted campaigns focused on high revenue-generating customers and prospects.
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ibm cognos, predictive analytics, marketing campaign management system, roi, customer behaviour
    
IBM Software
Published By: Dell Software     Published Date: Apr 17, 2013
The paper showcases how IT can proactively address problems before they result in service degradation. Foglight provides the visibility organizations need into the why of customer behavior. Line-of-business owners can identify sources of customer frustration and work with IT to eliminate issues.
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beyond web analytics, dell, quest software, reduce customer frustration, visible organizations
    
Dell Software
Published By: Cisco     Published Date: Sep 15, 2015
IDC finds that leveraging data analytics in business decisions is becoming a top priority for an increasing number of companies. This in turn is placing new demands on IT organizations; the need is twofold: to manage new streams of unstructured data from sources such as social media and to speed response times to deliver real-time analytics.
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sap, analytics, data, real-time
    
Cisco
Published By: IBM     Published Date: Jul 20, 2016
Big data. We've heard the phrase for quite some time, but how can human resource leaders get into the action? One way is through the development and implementation of talent analytics strategies. Talent analytics is fundamentally changing the way organizations and practitioners are thinking about the role of HR and organizations uncovering never before seen insights.
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ibm, talent acquisition, talent acquisition technology, human resources, recruiting
    
IBM
Published By: AWS     Published Date: Sep 04, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technology
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AWS
Published By: FICO     Published Date: Mar 22, 2018
Predictive analytics provide the foresight to understand cybersecurity risk exposure. Cybersecurity strategies often consist of “whack-a-mole” exercises focused on the perpetual detection and mitigation of vulnerabilities. As a result, organizations must re-think the ever-escalating costs associated with vulnerability management. After all, the daily flow of cybersecurity incidents and publicized data breaches, across all industries, calls into question the feasibility of achieving and maintaining a fully effective defense. The time is right to review the risk management and risk quantifcation methods applied in other disciplines to determine their applicability to cybersecurity. Security scoring is a hot topic, and rightfully so. When evaluating ways to integrate these scores into your cybersecurity strategy, be sure to look for an empirical approach to model development. The FICO Enterprise Security Score is the most accurate, predictive security score on the market.
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FICO
Published By: Pure Storage     Published Date: Apr 18, 2018
Apache Spark has become a critical tool for all types of businesses across all industries. It is enabling organizations to leverage the power of analytics to drive innovation and create new business models. The availability of public cloud services, particularly Amazon Web Services, has been an important factor in fueling the growth of Spark. However, IT organizations and Spark users are beginning to run up against limitations in relying on the public cloud—namely control, cost and performance.
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Pure Storage
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