data into insight

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Published By: SAS     Published Date: Apr 04, 2018
Location analytics is the process of integrating geographical data into business intelligence (BI) and analytics-led decision making. Location analytics creates meaningful insight from relationships found in geospatial data to solve a broad variety of business and social problems. Location data is found everywhere – with an item or a device, in a conversation or behavior, in machines or sensors, tied to a customer or competitor, attached to a database record or recorded from vehicles or other moving objects. Organizations want to take advantage of location data to improve decisions, create better customer engagement and experiences, reduce risks and automate business processes.
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SAS
Published By: Bazaarvoice     Published Date: Aug 01, 2014
Thanks to social, consumers are more vocal than ever and their opinions are influencing the purchase decisions of consumers all across the web. Learn how to turn social data into strategic business advantage with nine guiding insights to improve your bottom line today.
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bazaarvoice, marketing, social, insights, content, consumers
    
Bazaarvoice
Published By: IBM     Published Date: Oct 17, 2016
See how you can turn data into actionable insights with predictive analytics. Take our brief assessment to learn which analytical capabilities will enable you to find the greatest value in your data and make confident, accurate business decisions.
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ibm, analytics, spss, stats, modeler, predictive analytics, data management, business technology
    
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: IBM     Published Date: Oct 03, 2017
Many new regulations are spurring banks to rethink how data from across the enterprise flows into the aggregated risk and capital reports required by regulatory agencies. Data must be complete, correct and consistent to maintain confidence in risk reports, capital reports and analytical analyses. At the same time, banks need ways to monetize, grant access to and generate insight from data. To keep pace with regulatory changes, many banks will need to reapportion their budgets to support the development of new systems and processes. Regulators continually indicate that the banks must be able to provide, secure and deliver high-quality information that is consistent and mature.
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data aggregation, risk reporting, bank regulation, enterprise, reapportion budgets
    
IBM
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: 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, leveraging analytics, actionable insight, data environments, analytical solution, effective analytics, analytical engagement, process efficiency, data capture, data optimization, data management, data center
    
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, it management, knowledge management, data management
    
IBM
Published By: IBM     Published Date: May 22, 2015
This presentation will demonstrate that it is possible to turn the promise of Big Data into business value by applying predictive analytics to Big Data sources such as Hadoop, Cloudera, and BigInsights.
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big data, analytics, forecasting, data management, hadoop
    
IBM
Published By: AdRoll     Published Date: Oct 05, 2016
AdRoll looked in detail at the attribution strategies that agencies and brands across these markets are employing to find out how well they are leveraging their data to attract, convert and grow their customer base, as well as the challenges they face in integrating attribution into their marketing. From all of this AdRoll and Econsultancy deliver key insights and actionable insights which you can apply to your business in implementing or optimising attribution modeling.
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AdRoll
Published By: Omniture     Published Date: Jun 27, 2007
The beauty of Web analytics - and the promise of the Internet - is the ability to capture nearly unlimited amounts of data about your Web site. So how can you turn these incredible data resources into clear and actionable insights? A good place to start is by defining key performance indicators or KPIs.
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key performance indicator, kpi, omniture, web analytics, website analytics, user data, data collection, web measurement, dashboard, dashboards, lead generation, site catalyst, sitecatalyst
    
Omniture
Published By: SAS     Published Date: Aug 03, 2016
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.
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best practices, embedding analytics, technology, data, operational analytics, business technology
    
SAS
Published By: MicroStrategy     Published Date: Aug 21, 2019
Ready or not, the future is here. For enterprise organizations, it must be a data-driven one. Whoever can use technology to transform the customer experience, and be the first to discover and deliver on new business models, will be the disruptor. Those who can’t, the disrupted in this period known as the “era of Digital Darwinism.” The future belongs to the Intelligent Enterprise which anticipates constantly evolving regulatory, technological, market, and competitive challenges and turns them into opportunity and profit. It delivers a single version of the truth and agility. It connects to any data and distributes reports to thousands. The Intelligent Enterprise goes beyond business intelligence, delivering transformative insight to every user, constituent and partner. Are most organizations there yet? As brands hone and focus their 2020 (and even 2030) vision, MicroStrategy has surveyed 500 enterprise analytics professionals on the state of their organization’s analytics initiatives.
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MicroStrategy
Published 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.
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big data, enterprise analytics, red hat, data analytics, powerlinux system, powervm virtualization
    
IBM
Published By: NetApp     Published Date: Mar 05, 2018
Discover how you can transform your data into a strategic asset. Read this white paper to learn how NetApp can help you thrive in a hybrid cloud world. Learn how our solutions enable you to gain insight into your hybrid cloud, protect and secure your data wherever it lives, and achieve new levels of agility with DevOps and cloud analytics.
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netapp, database performance, flash storage, data management, cost challenges
    
NetApp
Published By: SAP     Published Date: May 03, 2016
Indiana's Management and Performance Hub (MPH) program has developed a comprehensive, enterprise-wide, data-driven management system to help gain insight into the drug abuse crisis. Indiana MPH staff is tackling the issue using crime lab drug data from across the state to see new correlations in drug use over time and find insight into crime.
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sap, indiana, government, analytics, predictive analytics, hana, digital, data management
    
SAP
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: Datastax     Published Date: Aug 27, 2018
Graph databases have the power to see deeply into real-time data relationships and make it easy to use relationship patterns for instant insight into large data sets. From IoT to networking to customer 360 to solving business problems with multi-model support, the power of graph can never be understated. Read this white paper to learn the uses cases for graph databases and how graph databases work.
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Datastax
Published By: Kronos     Published Date: Sep 20, 2018
Advanced analytics tools can help SMB retailers trim expenses while simultaneously improving shopper/associate engagement. Armed with big data insight into the performance of individual associates and stores small- and mid-size retailers can close the gap on the industry’s biggest players. Download this Retail IQ report and discover how retailers both big and small can benefit from cutting-edge insight into the performance of their workforce to better schedule, plan and assign tasks.
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Kronos
Published By: IBM     Published Date: Jul 09, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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IBM
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