real time data

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Published By: Datastax     Published Date: Mar 06, 2018
This person clearly didn’t work in the financial sector and clearly didn’t use real-time data management technology to power innovative financial apps. When it comes to numbers - specifically, money numbers and customer interactions - you really don’t want to leave your data at the mercy of a legacy system that doesn’t let you mine your data for all the instant insights it can offer.
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Datastax
Published By: LogMeIn     Published Date: Feb 27, 2018
Exceptional customer service is a competitive differentiator. Providing consistent service that is tailored to the individual across a variety of digital channels can help organizations deliver personalized experiences that exceed the expectations of today’s digital savvy consumers. Yet, many organizations fail to deliver real-time relevance in their service experiences because critical customer data is siloed in different systems (CRM, ticketing, etc.) across the enterprise, making it difficult to understand the full customer journey and their needs at that point in time. We partnered with IDG Research to survey top-performing customer service organizations to determine the challenges they face, and, more importantly, their plans for driving better outcomes. One thing is clear: taking a strategic approach to future investments can help organizations meet their customer service objectives and deliver on customer expectations.
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digital, engagement, customer, service, research, organizations
    
LogMeIn
Published By: Oracle     Published Date: Feb 21, 2018
Enable real-time transactions and securely share tamper-proof data across a trusted business network. Oracle Blockchain Cloud Service gives you a pre-assembled platform for building and running smart contracts and maintaining a tamper-proof distributed ledger.
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Oracle
Published By: Oracle     Published Date: Feb 21, 2018
Enable real-time transactions and securely share tamper-proof data across a trusted business network. Oracle Blockchain Cloud Service gives you a pre-assembled platform for building and running smart contracts and maintaining a tamper-proof distributed ledger.
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Oracle
Published By: Workday     Published Date: Feb 20, 2018
How can you put the power of real-time analytics into the hands of business users? Watch this video to learn about the technologies surrounding self-service analytics and data integration, and how you can use them to lead a data-driven organization.
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Workday
Published By: Cloudian     Published Date: Feb 15, 2018
We are living in an age of explosive data growth. IDC projects that the digital universe is growing 50% a year, doubling in size every 2 years. In media and entertainment, the growth is even faster as capacity-intensive formats such as 4K, 8K, and 360/VR gain traction. Fortunately, new trends in data storage are making it easier to stay ahead of the curve. In this paper, we will examine how object storage stacks up against LTO tape for media archives and backup. In addition to a detailed total cost of ownership (TCO) analysis covering both capital and operational expenses, this paper will look at the opportunity costs of not leveraging the real-time data access of object storage to monetize existing data. Finally, we will demonstrate the validity of the analysis with a real-world case study of a longstanding network TV show that made the switch from tape to object storage. The limitations of tape storage go way beyond its lack of scalability. Data that isn’t searchable is becoming
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Cloudian
Published By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
As organizations develop next-generation applications for the digital era, many are using cognitive computing ushered in by IBM Watson® technology. Cognitive applications can learn and react to customer preferences, and then use that information to support capabilities such as confidence-weighted outcomes with data transparency, systematic learning and natural language processing. To make the most of these next-generation applications, you need a next-generation database. It must handle a massive volume of data while delivering high performance to support real-time analytics. At the same time, it must provide data availability for demanding applications, scalability for growth and flexibility for responding to changes.
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database, applications, data availability, cognitive applications
    
Group M_IBM Q1'18
Published By: AWS     Published Date: Dec 15, 2017
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time. AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance. In this webinar, you’ll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences. Join this webinar to learn: How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development. How AWS supports BI solutions f
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AWS
Published By: Oracle OMC     Published Date: Nov 30, 2017
Lead nurturing is about helping buyers along in their educational journey. Thus, it’s most effective when triggered by prospect activity or behaviors. Lead management technologies are often used to automate such real-time marketing. This type of software makes it possible to track leads and automate content delivery while simultaneously collecting behavioral data and triggering corresponding actions.
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Oracle OMC
Published By: IBM APAC     Published Date: Nov 22, 2017
A user initiates the call and selects the source language, such as Spanish. (In this example, assume that the target language is set to English.) As the user is talking to the support representative, the audio is converted to text using the Speech to Text service. Then using Language Translator, the text is translated to English. English language text is then sent to the Text to Speech service as input. The output audio message is what the support representative hears. All of this happens in near real time. The text from Speech to Text and the Language Translator service also can be stored in a database for analytics. The same process is repeated in reverse for the audio message sent by support personnel.
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source, language, english, spanish, speech to text, database, analytics, audio message
    
IBM APAC
Published By: ADP     Published Date: Nov 16, 2017
People are the most important part of your organization. Understanding what they do, how they do it, and even why they do it, provides invaluable insights for optimizing your processes, department, or entire organization. And as a business leader, in many ways you’ve never had it so good. There’s more people data available than ever before, and you’ve got the opportunity to put it to work through data-driven initiatives like changes to workforce demographics, employee retention, or benefits allocations. And most important, you have a chance to fuel business decision-making with smart workforce insights. Once you’ve learned how to turn your people data into real business value, you’ll create more visibility within your business, and everyone will see that the rewards of HR analytics are worth the effort. It’s time to take the opportunity to prove the value of data-driven HR. No more darkness. Otherwise, the only journey ahead is into some challenging pitfalls. We’ve identified five of t
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ADP
Published By: MemSQL     Published Date: Nov 15, 2017
FREE O'REILLY EBOOK: BUILDING REAL-TIME DATA PIPELINES Unifying Applications and Analytics with In-Memory Architectures You'll Learn: - How to use Apache Kafka and Spark to build real-time data pipelines - How to use in-memory database management systems for real-time analytics - Top architectures for transitioning from data silos to real-time processing - Steps for getting to real-time operational systems - Considerations for choosing the best deployment option
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hardware trends, data pipelines, database management, architectures, technology
    
MemSQL
Published By: MemSQL     Published Date: Nov 15, 2017
THE LAMBDA ARCHITECTURE SIMPLIFIED Your Guide to Building a Scalable Data Architecture for Real-Time Workloads YOU'LL LEARN: - What defines the Lambda Architecture, broken down by each layer - How to simplify the Lambda Architecture by consolidating the speed layer and batch layer into one system - How to implement a scalable Lambda Architecture that accommodates streaming and immutable data - How companies like Comcast and Tapjoy use Lambda Architectures in production
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data, scalable, architecture, production
    
MemSQL
Published By: MemSQL     Published Date: Nov 15, 2017
Pairing Apache Kafka with a Real-Time Database Learn how to: ? Scope data pipelines all the way from ingest to applications and analytics ? Build data pipelines using a new SQL command: CREATE PIPELINE ? Achieve exactly-once semantics with native pipelines ? Overcome top challenges of real-time data management
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digital transformation, applications, data, pipelines, management
    
MemSQL
Published By: Adobe     Published Date: Nov 09, 2017
In our 32-criteria evaluation of real-time interaction management (RTIM) providers, we identified the 12 most significant ones — Adobe, Emarsys, FICO, IBM, IgnitionOne, Infor, Pegasystems, Pitney Bowes, Rocket Fuel, Salesforce, SAS, and Teradata — and researched, analyzed, and scored them. This report shows how each provider measures up and helps B2C marketing professionals make the right choice.
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Adobe
Published By: IBM Watson Health     Published Date: Oct 27, 2017
With the proliferation of health and fitness data due to personal fitness trackers, medical devices and other sensors that collect real-time information, cognitive computing is becoming more and more important. Cognitive computing systems, with the ability to understand, reason and learn while interacting with human-generated data, enable providers to find meaningful patterns in vast seas of information. IBM Watson Health is leveraging the power of cognitive computing to help providers make data-driven decisions to improve and save lives worldwide, while controlling healthcare costs. Read our whitepaper and learn about the new era of cognitive computing and how it can improve health outcomes, optimize care and engage individuals in making healthy choices.
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cognitive computing, data, insights, health outcomes, healthcare delivery, healthcare costs, care, healthy choices
    
IBM Watson Health
Published By: Oracle     Published Date: Oct 20, 2017
Modern technology initiatives are driving IT infrastructure in a new direction. Big data, social business, mobile applications, the cloud, and real-time analytics all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Customers increasingly drive the speed of business, and organizations need to engage with customers on their terms. The need to manage sensitive information with high levels of security as well as capture, analyze, and act upon massive volumes of data every hour of every day has become critical. These challenges will dramatically change the way that IT systems are designed, funded, and run compared to the past few decades. Databases and Java have become the de facto language in which modern, cloud-ready applications are written. The massive explosion in the volume, variety, and velocity of data increases the need for secure and effective analytics so that organizations can make bette
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
Modern technology initiatives are driving IT infrastructure in a new direction. Big data, social business, mobile applications, the cloud, and real-time analytics all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Customers increasingly drive the speed of business, and organizations need to engage with customers on their terms. The need to manage sensitive information with high levels of security as well as capture, analyze, and act upon massive volumes of data every hour of every day has become critical. These challenges will dramatically change the way that IT systems are designed, funded, and run compared to the past few decades. Databases and Java have become the de facto language in which modern, cloud-ready applications are written. The massive explosion in the volume, variety, and velocity of data increases the need for secure and effective analytics so that organizations can make bette
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by the aggressive build-out for cloud computing. Big data and machine learning applications that perform tasks such as fraud and intrusion detection, trend detection, and click-stream and social media analysis all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of business up, and organizations need to support their customers with real-time data. The task of managing sensitive information while capturing, analyzing, and acting upon massive volumes of data every hour of every day has become critical. These challenges have dramatically changed the way that IT systems are architected, provisioned, and run compared to the past few decades. Most compani
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
This whitepaper explores the new SPARC S7 server features and then compares this offering to a similar x86 offering. The key characteristics of the SPARC S7 to be highlighted are: Designed for scale-out and cloud infrastructures SPARC S7 processor with greater core performance than the latest Intel Xeon E5 processor Software in Silicon which offers hardware-based features such as data acceleration and security The SPARC S7 is then compared to a similar x86 solution from three different perspectives, namely performance, risk and cost. Performance matters as business markets are driving IT to provide an environment that: Continuously provides real-time results. Processes more complex workload stacks. Optimizes usage of per-core software licenses Risk matters today and into the foreseeable future, as challenges to secure systems and data are becoming more frequent and invasive from within and from outside. Oracle SPARC systems approach risk management from multiple perspectiv
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
What do these market-defining trends have in common? · Analytics for all · Analytics as competitive differentiator · Internet of Things · Artificial intelligence/Machine learning/Cognitive computing · Real-time analytics/event management They all rely on data – timely, accurate data delivered within an insightful context – to deliver value. The question is: who in the enterprise is most qualified and prepared to help deliver on the vision and values of the data-driven enterprise? It’s going to take a special type of professional to deliver that value to enterprises. Organizations are seeking professionals to step forward and take the lead, provide guidance and lend expertise to move into the brave new world of digital. The move to digital and all that it entails – sophisticated data analytics, online customer engagement and digital process efficiency – requires, above all, the skills and knowledge associated with handling data and turning it into insights. The move to digital i
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
What do these market-defining trends have in common? · Analytics for all · Analytics as competitive differentiator · Internet of Things · Artificial intelligence/Machine learning/Cognitive computing · Real-time analytics/event management They all rely on data – timely, accurate data delivered within an insightful context – to deliver value. The question is: who in the enterprise is most qualified and prepared to help deliver on the vision and values of the data-driven enterprise? It’s going to take a special type of professional to deliver that value to enterprises. Organizations are seeking professionals to step forward and take the lead, provide guidance and lend expertise to move into the brave new world of digital. The move to digital and all that it entails – sophisticated data analytics, online customer engagement and digital process efficiency – requires, above all, the skills and knowledge associated with handling data and turning it into insights. The move to digital is also a
Tags : 
    
Oracle
Published By: Oracle CX     Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s databases, accessing and using the right information at the right time has become increasingly critical. Real-time access and analysis of operational data is key to making faster and better business decisions, providing enterprises with unique competitive advantages. Running analytics on operational data has been difficult because operational data is stored in row format, which is best for online transaction processing (OLTP) databases, while storing data in column format is much better for analytics processing. Therefore, companies normally have both an operational database with data in row format and a separate data warehouse with data in column format, which leads to reliance on “stale data” for business decisions. With Oracle’s Database In-Memory and Oracle servers based on the SPARC S7 and SPARC M7 processors companies can now store data in memory in both row and data formats, and run analytics on their operatio
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Oracle CX
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