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Published By: IBM     Published Date: Aug 08, 2016
IBM API Connect is a comprehensive management solution that addresses all four aspects of the API lifecycle: create, run, manage and secure. This makes API Connect far more cost-effective than limited point solutions that focus on just a few lifecycle phases and can end up collectively costing more as organizations piece components together. Download this datasheet and find out how IBM API Connect can help your organization.
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ibm, api, api economy, integration, application program interface, cloud, middleware, digital transformation, ibm api connect, enterprise applications
    
IBM
Published By: IBM     Published Date: Nov 17, 2016
IBM API Connect is a comprehensive management solution that addresses all four aspects of the API lifecycle: create, run, manage and secure. This makes API Connect far more cost-effective than limited point solutions that focus on just a few lifecycle phases and can end up collectively costing more as organizations piece components together. Download this datasheet and find out how IBM API Connect can help your organization.
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ibm, api, api economy, integration, application program interface, cloud, middleware, digital transformation, ibm api connect, knowledge management, enterprise applications
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
In today’s highly distributed, multi-platform world, the data needed to solve any particular decision making need is increasingly likely to be found across a wide variety of sources. As a result, traditional manual approaches requiring prior collection, storage and integration of extensive sets of data in the analyst’s preferred exploration environment are becoming less useful. Data virtualization, which offers transparent access to distributed, diverse data sources, offers a valuable alternative approach in these circumstances.
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IBM
Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
As the information age matures, data has become the most powerful resource enterprises have at their disposal. Businesses have embraced digital transformation, often staking their reputations on insights extracted from collected data. While decision-makers hone in on hot topics like AI and the potential of data to drive businesses into the future, many underestimate the pitfalls of poor data governance. If business decision-makers can’t trust the data within their organization, how can stakeholders and customers know they are in good hands? Information that is not correctly distributed, or abandoned within an IT silo, can prove harmful to the integrity of business decisions.
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Group M_IBM Q2'19
Published By: Oracle     Published Date: Nov 07, 2018
At many organisations, planning and budgeting take too long and devour resources. Finance teams spend too much time on administrative tasks relating to these processes—collecting, consolidating and reconciling data, for example—and are left with little time for analysis, strategy development and target setting. Accuracycan be patchy, results unreliable.
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Oracle
Published By: Avaya     Published Date: Dec 18, 2013
Your contact center is a hotbed of activity, constantly processing calls and emails, chats and social media posts, problems and solutions. As a result, it generates the kind of “big data” that other departments wish they had. But collecting that data is just the beginning. The next step is turning it into a plan of action.
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avaya, big data, contact center, social media, software development, it management, data center
    
Avaya
Published By: Visier     Published Date: Jan 30, 2015
Until recently, ConAgra Foods struggled to collect accurate, analyzable data about its workforce. Information was spread across the organization in siloed systems within various departments and was often difficult to reconcile. In a few short years, however, ConAgra Foods has been able to leverage technology solutions to gain real-time insights into its employee data and can now analyze its workforce across a wide range of subject areas. Download this case study to see how ConAgra Foods transformed the role of HR through analytics, and to get guidance on how your organization can adopt a similar approach.
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visier, conagra, workforce, hr analytics, real-time insights, human resources, human resources management
    
Visier
Published By: Equinix     Published Date: Oct 27, 2014
Enterprises grapple with a host of challenges that are spurring the creation of hybrid clouds: collections of computing infrastructure spread across multiple data centers and multiple cloud providers. This new concept often provokes uncertainty, which must be addressed head on. As more applications and computing resources move to the cloud, enterprises will become more dependent on cloud vendors, whether the issue is access, hosting, management, or any number of other services. Cloud consumers want to avoid vendor lock-in—having only one cloud provider. They want to know that they will have visibility into data and systems across multiple platforms and providers. They want to be able to move servers and storage around without a negative impact on application availability.
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data center, enterprise, cloud, experience, hybrid, performance, strategy, interconnectivity, network, drive, evolution, landscape, server, mobile, technology, globalization, stem, hyperdigitization, consumer, networking
    
Equinix
Published By: IBM     Published Date: Apr 21, 2016
Industry analysts expect North American sports industry revenue to grow at a compounded annual rate of 4.8% to reach $67.7 billion by 2017, with customer-generated data the most collected type of external data.1 IBM Fan Insight, a software as a service-based solution available in the IBM Softlayer Cloud, and offers sports teams the breakthrough customer intelligence they need to lead in their industry.
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ibm, sports marketing, sports industry, fan insight, kpi dashboard
    
IBM
Published By: IBM     Published Date: Oct 21, 2016
The greatest challenge of the big data revolution is making sense of all the information generated by today's vast digital economy. It's well enough for an organization to collect every slice of data it can reach, but how does it extract value from this massive volume of information?
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ibm, analytics, data science, data, big data, aps data, aps, enterprise applications, data management, data center
    
IBM
Published By: IBM     Published Date: Oct 28, 2016
In this webinar, you will learn: - How to create a safe environment for honest feedback so it’s an invitation, not an imposition - Best practices for collecting feedback, from unstructured conversations to regular pulse surveys - What to do with the data you’ve collected, and how to use insights to create action plans that result in happier employees and better business performance
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ibm, engagement social, employee engagement, kenexa, knowledge management, enterprise applications
    
IBM
Published By: IBM     Published Date: Jan 18, 2017
It's all well enough for an organization to collect every slice of data it can reach, but having more data doesn't mean you'll automatically get better insights. First, you have to figure out what you want from your data you have to find its value.
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ibm, aps data, data science, open data science, analytics, enterprise applications
    
IBM
Published By: IBM     Published Date: Jan 30, 2017
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, cognitive computing, analytics, engagement, human resources
    
IBM
Published By: IBM     Published Date: May 22, 2017
Only a handful of industries have been transformed by the digital age the way banking has. Internet and mobile banking, digital wallets, and a raft of new and innovative products have redefined “the bank” from a local, brick-and-mortar branch to an anytime-anywhere process. The new banking environment has opened opportunities for national, regional, and community banks alike, which are no longer constrained to serve only customers located in the areas where they maintain a physical branch presence. But it has also brought challenges associated with collecting, processing, analyzing, storing, and protecting vast amounts of new data, from multiple locations and sources.
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cloud privacy, cloud security, cloud management, cloud assurance, cloud visibility, enterprise management, data management
    
IBM
Published By: IBM     Published Date: Nov 14, 2017
Data is the hottest topic in business today. In discussions that range from understanding performance to predicting future outcomes, data is at the core. However, data has a bad reputation. Because businesses have been collecting data for decades, the amount that we must analyze can seem insurmountable. Simply saying “data” is enough to conjure images of someone poring over a thick stack of spreadsheets, manually going through row after row to identify performance, trends and figure out what to do with them. This intimidating view is all too common.
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data, ibm, data insight, data analytics
    
IBM
Published By: Datastax     Published Date: Aug 23, 2017
About 10 years ago big data was quickly becoming the next big thing. It surged in popularity, swooning into the tech world's collective consciousness and spawning endless start-ups, thought pieces, and investment funding, and big data's rise in the startup world does not seem to be slowing down. But something's been happening lately: big data projects have been failing, or have been sitting on a shelf somewhere and not delivering on their promises. Why? To answer this question, we need to look at big data's defining characteristic - or make that characteristics, plural - or what is commonly known as 'the 3Vs": volume, variety and velocity.
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datastax, big data, funding
    
Datastax
Published By: Datastax     Published Date: May 14, 2018
The data management practices of old will no longer work. If you’re still trying to use a centralized approach, you are probably finding it difficult to keep up with the real-time demands of the Right-Now Economy. This special Gartner report describes the main drivers for modernizing data management — operational efficiency and analytics — and explains why balancing connecting data with collecting data will be a fundamental requirement for modern data management moving forward.
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Datastax
Published By: IBM     Published Date: Jun 04, 2018
"What would you do if you didn’t have to rely on disparate analytics solutions to meet the needs of business users while following the rules of IT? View this 'Charting Your Analytical Future' webinar to learn about a world of innovation and independence for users that does not limit the confidence and controls of IT. With the cognitive-guided self-service features available in IBM business analytics solutions, more users than ever before can get the answers they need. Next-generation business analytics capabilities make it possible to access relevant data, prepare it for analysis and understand performance. But it doesn’t stop there. Users can package the results in a visually-appealing format and share them throughout the organization. Don’t miss this opportunity to hear how you can: * Benefit from advanced analytics without the complexity * Operationalize insights and dashboards from a collection of trusted data sources * Tell your story with rich visualizations and geospati
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business analytics, analytics solutions
    
IBM
Published By: IBM     Published Date: Jul 09, 2018
As the information age matures, data has become the most powerful resource enterprises have at their disposal. Businesses have embraced digital transformation, often staking their reputations on insights extracted from collected data. While decision-makers hone in on hot topics like AI and the potential of data to drive businesses into the future, many underestimate the pitfalls of poor data governance. If business decision-makers can’t trust the data within their organization, how can stakeholders and customers know they are in good hands? Information that is not correctly distributed, or abandoned within an IT silo, can prove harmful to the integrity of business decisions. In search of instant analytical insights, businesses often prioritize data access and analysis over governance and quality. However, without ensuring the data is trustworthy, complete and consistent, leaders cannot be confident their decisions are rooted in facts and reality
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IBM
Published By: TIBCO Software     Published Date: Feb 14, 2019
Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them.
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TIBCO Software
Published By: TIBCO Software     Published Date: Feb 14, 2019
Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them. We'll also provide a short overview of TIBCO Statistica, an easy-to-use predictive analytics software solution designed to turn big data into your bigg
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TIBCO Software
Published By: Group M_IBM Q119     Published Date: Dec 20, 2018
The security information and event management (SIEM) market is defined by the customer's need to analyze event data in real time for the early detection of targeted attacks and data breaches, and to collect, store, analyze, investigate and report on event data for incident response, forensics and regulatory compliance. The vendors included in our Magic Quadrant analysis have products designed for this purpose, and they actively market and sell these technologies to the security buying center.
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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
Delivering personalized customer experience remains the top business challenge for communications service providers (CSPs). Ovum's recently published 2018 ICT Enterprise survey saw almost all CSP IT executives interviewed identify delivering personalized customer experience as one of their three most important business challenges for the next 18 months. This trend emphasizes the high priority CSPs place on how customer relationships are managed. However, several factors have an impact on CSPs' ability to identify and then deliver customers' core needs. These include understanding the data sets they should focus on; collecting, cleansing, and consolidating these data sets; and having the right expertise to mine the data sets.
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Group M_IBM Q2'19
Published By: Group M_IBM Q3'19     Published Date: Jul 01, 2019
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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Group M_IBM Q3'19
Published By: Sierra Wireless     Published Date: Jun 19, 2019
The IoT is transforming the energy industry by eliminating tradeoffs between operation, SCADA systems, maintenance and new services for assets deployed in industrial and power facilities, buildings and across the grid. When it comes to building the best IoT system for your business application, it’s vital to keep your use case and business requirements at the forefront of your technical design strategy. In the energy industry, accessing and collecting data at the edge from disparate, heterogenous, multi-site, fixed topologies and transferring that data efficiently to the cloud to perform analytics and action business decisions is still the greatest challenge. Mission-critical data collected from the edge is integral to energy facility operations and cannot be excluded or corrupted.
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Sierra Wireless
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