data and analytics

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Published By: MoreVisibility     Published Date: Dec 19, 2017
As the approach to strategic business decision making becomes more and more data driven, a method for consolidating our various data sets, which are often spread across multiple systems becomes exceedingly important. Two of the biggest players in data driven decision making are website analytics platforms and customer relationship management systems. The former includes accumulating data on top of the funnel behavior such as site traffic origins, lead generation, content consumption tracking, device usage, and overall site behavior. While the latter has a focus more on bottom of the funnel activity such as lead nurturing, customer status, lifetime value, etc. Lastly, without communication between these two essential platforms, a complete understanding of your customers, from lead to longtime client, may never be possible. A web analytics (Google Analytics) and CRM integration provides you with a 360 degree view of your customer base, so that you can understand not just what PPC efforts
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MoreVisibility
Published By: SAS     Published Date: Apr 25, 2017
Whether you call them customers, clients, patrons, guests or patients, customers are your organization’s most important asset. And that means customer loyalty should be among your top priorities. No matter when or where the customer journey begins – from websites and online chat to physical locations and call centers – customers expect you to provide a unique and personal experience. How can you use data and analytics to recognize your best customers across channels and know exactly where they are in their customer journey? Keep reading to find out.
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SAS
Published By: ClearSlide, Inc     Published Date: Feb 25, 2015
Organizations treat customer data as a key component of CRM. Many organizations see an effective customer data management strategy as an important cornerstone of their CRM strategy. Most organizations are moving beyond tactical CRM initiatives focused on saving cost and driving efficiency to making their organization more effective and focused on driving better customer engagement and experience. Customer data is used to enhance customer experiences, improve service quality, target marketing efforts, capture customer sentiment, increase upsell opportunities and trigger product and service innovation.
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crm, big data, customer engagement, customer service
    
ClearSlide, Inc
Published By: GoodData     Published Date: Aug 02, 2013
Salesforce provides a tremendous repository of customer information and interactions that’s organized and easily accessed. But that’s not enough! Once you begin to really use it, the questions you ask your salesforce data become more sophisticated. Then you start to uncover holes in your data, reporting strategy and Salesforce analytics itself. Download this white paper for 4 tips that are a surefire way to improve your salesforce analytics.
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sales metrics, how to forecast sales, sales analysis, sales analytics, crm analytics, salesforce reports, salesforce analytics, sales projections, sales kpis, sales prospecting, sales forecasting, sales forecast, sales pipeline, analytics, sales, business technology
    
GoodData
Published By: IBM     Published Date: Apr 29, 2014
For banks, mining data from social media can be a significant way to gain insights into customer mindsets and behavior, but effectively and accurately capturing and processing this unstructured data to gain useful customer insight requires sophisticated tools and advanced analytics.
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ibm, banking, data mining, social media, consumer insights, business analytics, social business, business technology
    
IBM
Published By: IBM     Published Date: Apr 29, 2014
Customer Profitability Analytics enables banks to analyze customer, account, product, and transaction data and apply costing models to determine a bank-wide view of profitability. Applying predictive analytics, they can model future behavior and derive a lifetime value for each customer.
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ibm, banking, customer profitability, customer profitability analytics, transaction data, predictive analytics, customer behavior
    
IBM
Published By: IBM     Published Date: Jan 06, 2016
In the age of big data and workforce analytics, statistics and metrics abound. In the face of an overabundance of numbers, knowing which metrics are most important can be a real challenge for Human Resource (HR) leaders and hiring managers.
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big data, workforce, statistics, metrics, ibm
    
IBM
Published By: IBM     Published Date: Mar 28, 2016
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
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ibm, talent analytics, kenexa talent insights, workforce science, talent insights, human resource technology
    
IBM
Published By: IBM     Published Date: Mar 28, 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, smarter workforce, talent management, talent analytics
    
IBM
Published By: IBM     Published Date: Mar 28, 2016
Five years ago in a Harvard Business Review article on how companies leveraged HR analytics for competitive advantage, my co-authors and I found only a small handful of companies to interview. Today, life is very different. Interest in analytics and storytelling through data in HR is booming. But, HR as a whole is still learning how to set up an effective analytics function. That is why I welcome this report. The first 100 days of any effort is critical to success. Analytics is no different. Lead authors of this report and the people they have interviewed have sat in analytics roles in HR. They’ve learned a lot about what works and what doesn’t. Here’s an opportunity to learn from those who have already undertaken this particular journey. They know that to succeed in analytics one needs to: • Focus on business priorities • Leverage your analytics through storytelling • Use analytics to help inform decision making, not as a substitute • Understand that perfect data isn’t required for a
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ibm, workforce analytics, ibm smarter workforce institute, human resource technology
    
IBM
Published By: IBM     Published Date: Mar 28, 2016
Workforce analytics is a very significant development in human resources. It promises the potential for deeper understanding of the ways workers contribute to organizational performance. However, workforce analytics is not just about analyzing data to reveal exciting insights; it also requires the active involvement of a firm’s workers if the potential of analytics is to be fully realized. Without active employee participation, workforce analytics efforts face at best, restricted data sources and data sets that are incomplete and at worst, the risk of damaging employee relations and, ultimately, productivity. This white paper summarizes recommendations that will encourage enthusiasm for workforce analytics and active employee participation, using the FORT (Feedback, Opt-in, Reciprocal, Transparent) framework. The FORT criteria could prove particularly useful in European countries. This is because the 1995 European Union Data Protection Directive, along with certain local legislative pr
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ibm, ibm smarter workforce institute, work trends, employee engagement
    
IBM
Published By: IBM     Published Date: Jun 13, 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: IBM     Published Date: Jun 13, 2016
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
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ibm, talent acquisition, talent acquisition technology, human resources, recruiting, talent acquisition technology
    
IBM
Published By: IBM     Published Date: Jul 20, 2016
Analytics has permeated, virtually, every department within an organization. It’s no longer a ‘nice to have’. It’s an organizational imperative. HR, specifically, collects a wealth of data; from recruiting applications, employee surveys, performance management data and it sits in systems that remain largely untapped. This data can help drive strategic decisions about your workforce. Analytic tools have, historically, been difficult to use and required heavy IT lifting in order to get the most out of them. What if an analytics system learned and continue to learn as it experienced new information, new scenarios, and new responses. This is referred to as Cognitive Computing and is key to providing an analytics system that is easy to use but extremely powerful.
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ibm, talent acquisition, talent acquisition technology, human resources, recruiting, talent acquisition technology, human resource technology
    
IBM
Published By: SnowFlake     Published Date: Jul 08, 2016
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access to only a few highly-skilled data scientists. However, these efforts are quickly frustrated by the limitations of current data warehouse technologies. These systems simply were not built to handle the diversity of today’s data and analytics. They are based on decades-old architectures designed for a different world, a world where data was limited, users of data were few, and all processing was done in on-premises data centers.
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snowflake, data, technology, enterprise, application, best practices, social media, storage, business technology
    
SnowFlake
Published By: IBM     Published Date: May 27, 2014
Big data and analytics help insurance companies identify the next best action for customers. With the right solutions, companies can extract, integrate and analyze a large volume and variety of data, from call-center notes and voice recordings to web chats, telematics and social media
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ibm, big data, analytics, insurance, insurance industry, big data solutions, integration, risk assessment, policy rates, customer retention, claims data, transaction data
    
IBM
Published By: IBM     Published Date: Feb 24, 2015
IBM offers a comprehensive solutions portfolio that helps insurance companies integrate big data and analytics capabilities into their telematics strategy. The portfolio incorporates dozens of new patented technologies developed by IBM Research and builds on IBM Watson Foundations.
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telematics, ibm, insurance, big data, comprehensive solutions
    
IBM
Published By: IBM     Published Date: Feb 24, 2015
Big data and analytics help insurance companies identify the next best action for customers. With the right solutions, companies can extract, integrate and analyze a large volume and variety of data, from call-center notes and voice recordings to web chats, telematics and social media.
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big data, ibm, claims operations, customer service
    
IBM
Published By: IBM     Published Date: Sep 08, 2015
Please join us for an in-depth discussion of the latest data and analytics capabilities that banks are using today to uncover new customer insight - at the individual level - to improve offers and cross sell, optimize campaigns and deliver profitable revenue. The discussion will explore how leading banks are employing predictive analysis on customer data not often analyzed - like individual transactions, interactions, behavior and social media. See how banks can move beyond traditional segmentation and enable marketing to the "Segment of One."
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banking, finance, customer engagement, analytics, insights, segmentation, data management, business technology
    
IBM
Published By: IBM     Published Date: Apr 15, 2016
Ziff Davis Custom Whitepaper: Analytics relies on BI, Big Data, and data discovery to provide reporting, trend analysis, and what-if analysis.iii Analytics is defined as the scientific process of transforming data into insight for making better decisions.
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ibm, business analytics, business intelligence, agile, decision making, big data, business technology
    
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
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. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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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: Mar 06, 2018
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses. SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
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SAS
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