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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
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
    
IBM
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
    
IBM
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
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: 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: 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
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
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
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
Published By: SAS     Published Date: Oct 03, 2018
Fraudsters are only becoming smarter. How is your organization keeping pace and staying ahead of fraud schemes and regulatory mandates to monitor for them? Technology is redefining what’s possible in fighting fraud and financial crimes, and SAS is at the forefront, offering solutions to: • Protect from reputational, regulatory and financial risks. • Reduce the cost of fraud and financial crimes prevention. • Gain a holistic view of risk across functions. • Include cyber events in regulatory report filings. In this e-book, learn the basics in how to prevent fraud, achieve compliance and preserve security. SAS fraud solutions use advanced analytics and artificial intelligence to help your organization better detect and prevent fraud. By applying analytics and powerful machine learning on a unifying platform, SAS helps organizations around the globe detect more financial offenses, reduce false positives and run more efficient investigations.
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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
Welcome to the results of our study about analytics and analytics platforms. Our latest research indicates that the use of analytics continues to evolve in most organizations, and that many organizations are on the path to using analytics strategically.
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SAS
Published By: SAS     Published Date: Mar 20, 2019
One of the best ways to learn is from your peers. How do they drive innovation? What are their challenges and business problems? And what technology and processes are they using to solve them? We’re all partners in this analytics journey. Sharing our experiences is how we learn, grow and find a better way. In this ebook, you’ll meet fellow CIOs and technology innovators. Through their stories, you’ll see how organizations of all sizes, all over the globe, encounter many of the same daily challenges. Most importantly, you’ll learn how they’re successfully overcoming them.
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SAS
Published By: SAS     Published Date: Mar 20, 2019
In today’s crowded analytics marketplace, who can you trust? What’s needed to deliver on the promise of transforming data into real value? And what do CIOs need to cost-effectively and successfully lead their organizations through changing technologies? For an organization to experiment with (and ultimately deploy) analytics, the responsibility falls squarely on the shoulders of IT. IT must provide secure access to lots of high-quality data, a friendly environment for experimentation and discovery, and a method for rapidly deploying and governing models. SAS can support an organization's journey toward becoming a data- and analytics-driven organization. We can help unlock the value by enabling with choices that make sense. Plus, we can show organizations how to get the most out of technology investments.
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SAS
Published By: SAS     Published Date: Apr 17, 2019
Organizations are charging ahead with investments in cloud and analytics to deliver agility, scalability and cost savings. With computing power advancements and continuous growth of data, cloud provides the elastic workloads and flexibility required for modern business. However, the environment of flexibility and choice that cloud provides also creates complexity and challenges. In this white paper, learn how organizations are applying expertise and using the latest methods to move analytics to the cloud, including: Why are organizations moving analytic work to the cloud? What are the key challenges and misconceptions? How do IT leaders provide choice while maintaining control?
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SAS
Published By: IBM     Published Date: Apr 04, 2013
Many organizations and agencies would like to improve their debt collection. They are aware that advanced analytics can help them optimize collections to drive down company debt and collection expenditures. However, they perceive that advanced analytics requires massive infrastructure changes, expensive software licenses, analytics expertise, long lead times and major upfront capital expenses.
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ibm analytics, ibm, prioritized collections, drive down, debt, debt collections, expenditures, many organizations
    
IBM
Published By: IBM     Published Date: Sep 27, 2013
Analytics: The Real-World Use of Big Data - How innovative enterprises in the midmarket extract value from uncertain data This study highlights the phases of the big data journey, the objectives and challenges of midsize organizations taking the journey, and the current state of the technology that they are using to drive results. It also offers a pragmatic course of action for midsize companies to take as they dive into this new era of computing.
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ibm, big data, big data solutions, midmarket businesses, analytics
    
IBM
Published By: IBM     Published Date: Jan 02, 2014
For midsize organizations, business analytics offers the crucial ability to transform data into insight and uncover opportunities for growth and competitive advantage. This Aberdeen Sector Insight explores the impact of business analytics in North American midsize organizations.
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ibm, aberdeen group, mid-market analytics, data into insight, business analytics, technology investment, aberdeen sector insight, opportunities for growth
    
IBM
Published By: IBM     Published Date: Jan 09, 2014
In this Advisory, Clabby Analytics looks more closely at the importance of implementing an integrated infrastructure — and its relationship to business analytics. The paper will examine the business analytics workloads, discuss how these workloads put different demands on the underlying systems and infrastructure, and provide guidance designing an integrated business analytics environment that will provide organizations with high performance, resiliency, and a scalable growth path.
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analytics, power 7, big data, power systems, virtualization, ibm, ibm power systems, infrastructure
    
IBM
Published By: IBM     Published Date: Jul 07, 2014
In this Advisory, Clabby Analytics looks more closely at the importance of implementing an integrated infrastructure — and its relationship to business analytics. The paper will examine the business analytics workloads, discuss how these workloads put different demands on the underlying systems and infrastructure, and provide guidance designing an integrated business analytics environment that will provide organizations with high performance, resiliency, and a scalable growth path.
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ibm, business analytics, integrated infrastructure, workload, infrastructure, business analytics environment, integrated business analytics, resiliency
    
IBM
Published By: IBM     Published Date: Jul 07, 2014
With so much emphasis in the business world being placed on big data and analytics, it can be easy for midsize businesses to feel like they’re being left behind. These organizations often recognize the benefits offered by big data and analytics, but have a hard time pursuing those benefits with the limited resources available to them.
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ibm, analytics, big data, midmarket, midsize businesses, data-driven insights, business insights, business value
    
IBM
Published By: IBM     Published Date: Jan 05, 2015
This white paper will discuss the challenges most companies face with managing their data and suggest some strategies and solutions to turn midmarket data into a working asset.
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analytics, midmarket organizations, analytic solutions, midmarket data
    
IBM
Published By: IBM     Published Date: Jan 05, 2015
Ziff Davis recently surveyed over 300 IT professionals on the state of big data and analytics initiatives in their organizations. The results tell a compelling story: while most IT pros understand the value of big data, actually operationalizing their analytics strategies to deliver usable insights to their organizations remains a challenge.
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big data, it professionals, analytics initiatives, analytics strategies, analytic solutions
    
IBM
Published By: IBM     Published Date: Jan 14, 2015
Cloud-delivered big data analytics presents an enormous opportunity for organizations that want to become more agile, more efficient and more competitive. To capitalize on the full potential of this opportunity, businesses need cloud-enabled big data analysis solutions that are flexible, simple, secure and open.
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big data, data management, the cloud, cloud solutions, data, data analytics, client engagement, computing model
    
IBM
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