it analytics

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Published By: Tippit, CRM     Published Date: May 15, 2009
Today's Customer Relationship Management (CRM) solutions aim to recapture the personalized customer service provided by local mom-and-pop shops of yore – except with high-tech-analytics capabilities, collaborative platforms and automated processes. By gathering information from multiple data sources and storing it in a centralized location, a hosted CRM solution provides a holistic view of a customer in real time. Armed with this insight, a company’s management, sales and service people can better generate leads, target top customers, manage marketing campaigns, drive sales and boost customer satisfaction.
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tippit, marketing, analytics, sales force automation, crm, onyx software, rightnow technologies
    
Tippit, CRM
Published By: SAS     Published Date: Sep 30, 2014
When Information Revolution1 was published in 2006, no Chinese based companies were among the top 10 largest companies by market capitalization. Apple didn’t sell phones. Facebook was something college kids used to connect with their friends. Back then, we talked a lot about the amount of data coming in and faster processing speed. What we believed then remains true today: Data, and the decision-making process, can be moved throughout the organization to equip every decision maker (automated, line worker, analyst, executive) to make the best choices. By operationalizing analytics, organizations can identify and quantify both opportunity and risk. Information Revolution highlighted SAS’ Information Evolution Model, which helps organizations understand how they interact with their information and how to extract more value from it through analytics.
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sas, organizational insights, operationalizing analytics, sas’ information evolution model
    
SAS
Published By: SAS     Published Date: Sep 30, 2014
As analytics and Big Data have been embraced, analysts are working to become better at communicating the insights from complex analysis. This makes the use of visual analytics increasingly important as a tool to tell compelling stories and to engage decision makers in dialogue. Importantly, the best visual analytics are not necessarily the coolest, most sophisticated, or most complex. Visual analytics are most effective when there is a clear purpose and when data can be visualized and communicated in a way that is easily understandable.
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sas, big data, visual analytics
    
SAS
Published By: SAS     Published Date: Mar 31, 2016
Analytics is more important to success than ever before, and it’s a business practice that has momentum. Fifty-eight percent of the respondents in a recent survey published in the MIT Sloan Management Review stated that the use of analytics gave their companies a competitive advantage, up from 37 percent the prior year. Enterprise-scale companies report dramatic successes with analytics.
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analysis, development management, data management, business intelligence, best practices
    
SAS
Published By: SAS     Published Date: May 12, 2016
This paper examines the barriers to adoption from an IT and end-user perspective, and shows how self-service analytics in general – and SAS Visual Analytics in particular – can eliminate these barriers. Self-service analytics empowers users to truly exploit the wealth of data available to them, while ensuring that the IT organization maintains governance and control over that data.
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sas, business analytics, it organization, networking, it management, data management, business technology
    
SAS
Published By: SAS     Published Date: May 17, 2016
This report provides a guide to some of the opportunities that are available for using machine learning in business, and how to overcome some of the key challenges of incorporating machine learning into an analytics strategy. We will discuss the momentum of machine learning in the current analytics landscape, the growing number of modern applications for machine learning, as well as the organizational and technological challenges businesses face when adopting machine learning. We will also look at how two specific organizations are exploiting the opportunities and overcoming the challenges of machine learning as they’ve embarked on their own analytic evolution.
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oreilly, evolution of analytics, sas, machine learning, analytics landscape, networking, it management, data management, business technology
    
SAS
Published By: SAS     Published Date: Jun 10, 2016
Understanding security analytics can be a daunting job. It is more than just analyzing log files but it is less than a full-blown information security platform. In fact, according to Anton Chuvakin, research vice president for security and risk management at Gartner, it is not yet even a “market,” but rather still just a ”concept” that has yet to define best practices.
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security, business metrics, best practices, business intelligence, enterprise applications, analytics, prevention
    
SAS
Published By: SAS     Published Date: Apr 25, 2017
If you are working with massive amounts of data, one challenge is how to display results of data exploration and analysis in a way that is not overwhelming. You may need a new way to look at the data – one that collapses and condenses the results in an intuitive fashion but still displays graphs and charts that decision makers are accustomed to seeing. And, in today’s on-the-go society, you may also need to make the results available quickly via mobile devices, and provide users with the ability to easily explore data on their own in real time. SAS® Visual Analytics is a data visualization and business intelligence solution that uses intelligent autocharting to help business analysts and nontechnical users visualize data. It creates the best possible visual based on the data that is selected. The visualizations make it easy to see patterns and trends and identify opportunities for further analysis. The heart and soul of SAS Visual Analytics is the SAS® LASR™ Analytic Server, which ca
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SAS
Published By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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SAS
Published By: SAS     Published Date: Apr 25, 2017
Are you a marketing leader on the path to modernizing your marketing organization? Are you a marketing analyst championing analytical transformation in your campaigns? If you answered yes to either question, this e-book is for you. It offers a practical account of how to create a new marketing culture that adds value through data and analytics. You’ll meet marketing leaders from Comerica, Lenovo, RCI, SAS and Visa – and read how they’re implementing analytics, redefining marketing strategies and transforming their cultures. By sharing their perspectives, we hope to provide a new set of best practices to guide your analytical transformation – and to help you reinvent your marketing organization for the digital age.
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SAS
Published By: SAS     Published Date: May 04, 2017
Should you modernize with Hadoop? If your goal is to catch, process and analyze more data at dramatically lower costs, the answer is yes. In this e-book, we interview two Hadoop early adopters and two Hadoop implementers to learn how businesses are managing their big data and how analytics projects are evolving with Hadoop. We also provide tips for big data management and share survey results to give a broader picture of Hadoop users. We hope this e-book gives you the information you need to understand the trends, benefits and best practices for Hadoop.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
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.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
One of the biggest inhibitors of analytics success is the delay between developing and implementing models. This paper reviews how an analytics factory, a rapid scoring and model development environment, can help organizations turn models into insight faster than before.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
As a new era of analytics takes hold, a key characteristic is using analytics to automate IT tasks and decisions. In this Harvard Business Review paper, Tom Davenport describes the opportunity for greater use of analytics in IT, the eras of analytics and how IT will grow and change.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
This TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for embedded analytics across both organizational and technical dimensions, including organizational culture, infrastructure, data and processes. It looks at challenges and how organizations are overcoming them, and offers recommendations and best practices for successfully operationalizing analytics in the organization.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
Find out what text analytics can do for an organization and the top three things people need to know when adopting text analytics. This research brief from the International Institute for Analytics and SAS outlines the challenges of implementing text analytics solutions and explores what makes this technology unique and exciting.  
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SAS
Published 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
Published By: SAS     Published Date: Jun 05, 2017
The Internet of Things is fast becoming a fixture in some industries, and the technologies for transformative business applications are at hand. Yet many organizations have been slow to recognize and act on these new opportunities. This report from the International Institute for Analytics explores the many business opportunities IoT presents, details its associated implementation challenges and describes how organizations can accelerate their progress so they don’t fall behind.
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SAS
Published By: SAS     Published Date: Aug 02, 2017
With more data in the hands of more people – and easier access to easy-to-use analytics – conversations about data and results from data analysis are happening more often. And becoming more important. And expected. So it’s not surprising that improved collaboration is one of the most common organizational goals. Why? Because two heads, or 10 heads, are better than one. Because bouncing ideas off of others helps you consider more options. And because sharing what you know may help someone else make better decisions.
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SAS
Published By: SAS     Published Date: Oct 18, 2017
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: Oct 18, 2017
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
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SAS
Published By: SAS     Published Date: Oct 18, 2017
Organizations need to accelerate the pace with which they realize business value from data. The focus is on improving “time to value,” which is the length of time it takes from the beginning of a project to the delivery of anticipated business value. This TDWI Best Practices Report focuses on realizing value from BI and analytics and how organizations can accelerate the path to higher value. The report looks at multiple factors impacting the ability of organizations to quickly derive greater value from data and analytics, including the organizational issues, practices, and development methods that are often just as important as keeping pace with technological innovation.
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SAS
Published By: SugarCRM     Published Date: Jan 20, 2015
After an initial rollout to more than 45,000 sales users, IBM wanted even greater insights. Thanks to the high usability of Sugar, IBM was able to increase sales data quality - which has gone on to power predictive sales analytics. Learn how IBM and SugarCRM are driving more effective sales teams.
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sales analytics, data quality, sales, high usability, data management
    
SugarCRM
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