data analytics

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By: ServiceNow     Published Date: Sep 18, 2018
What is a performance based business? A performance-based business is an organization guided by data-driven decisions. It is proactive, self-aware, and highly competitive. Data isn’t siloed in a business analytics department. Instead, the right people have the right data at the right time and in the right context. The top 5 reasons to become a performance-based business: 1. Get better results. 2. Align your entire business. 3. Make data-driven decisions. 4. Manage change more effectively. 5. Spot trends faster. Discover how ServiceNow Performance Analytics could benefit your business by downloading this eBook.
Tags : performance, business, data, servicenow
     ServiceNow
By: ServiceNow     Published Date: Sep 18, 2018
Worldpay deployed ServiceNow Performance Analytics to replace multiple data tools and promote the use of analytics throughout its organization for improved decision making. Nucleus found the project enabled the company to boost analyst productivity, while simultaneously increasing data accessibility and engagement for hundreds of additional employees. Download this case study to learn more
Tags : nucleus, research, worldpay, servicenow
     ServiceNow
By: TIBCO Software     Published Date: Sep 12, 2018
The Internet of Things (IoT) didn’t just connect everything everywhere; It laid the groundwork for the next industrial revolution. Connected devices sending data was only one achievement of the IoT—but one that helped solve the problem of data spread across countless silos that was not collected because it was too voluminous and/or too expensive to analyze. Now, with advances in cloud computing and analytics, cheaper and more scalable factory solutions are available. This, in combination with the cost and size of sensors continuously being reduced, supplies the other achievement: the possibility for every organization to digitally transform. Using a Smart Factory system, all relevant data is aggregated, analyzed, and acted upon. Sensors, devices, people, and processes are part of a connected ecosystem providing: • Reduced downtime • Minimized surplus and defects • Deep insights • End-to-end real-time visibility
Tags : internet of things, connected ecosystem, big data, operations monitoring, process control, analytical techniques
     TIBCO Software
By: Infosys     Published Date: Sep 11, 2018
Infosys has been recognized as a ‘Leader’ in NelsonHall’s Vendor Evaluation and Assessment (NEAT) report on big data and analytics services 2018.We have also been highly rated for our focus on automation. Our ability to meet future client requirements as well as deliver immediate benefits such as analytics, data management and support functions to our clients with a specific focus on process automation enabled us to secure this position.
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     Infosys
By: Group M_IBM Q418     Published Date: Sep 10, 2018
There are three things that senior executives in the financial services industry want from their investments in computing systems. They are the same three things these institutions require for their very survival. First is unwavering security. The integrity of customer accounts and records is paramount to maintain trust across the financial ecosystem. Cybercrime is anathema to the core function of banking and cannot be tolerated. Next is captivating, personalized experiences based on real-time data analytics leading to instantaneous customer fulfillment. And finally, there is the essential delivery of these secure experiences while providing a cost and efficiency advantage over competing solutions
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     Group M_IBM Q418
By: Group M_IBM Q418     Published Date: Sep 10, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does. From an IT perspective, there is a fairly straightforward sequence of applications that businesses can adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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     Group M_IBM Q418
By: Splunk     Published Date: Sep 10, 2018
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential. Download this white paper to learn: *How organizations can answer critical questions that have been impeding business success *How the financial services industry can make great strides in security, compliance and IT *Common machine data sources in financial services firms
Tags : cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
     Splunk
By: Splunk     Published Date: Sep 10, 2018
One of the biggest challenges IT ops teams face is the lack of visibility across its infrastructure — physical, virtual and in the cloud. Making things even more complex, any infrastructure monitoring solution needs to not only meet the IT team’s needs, but also the needs of other stakeholders including line of business (LOB) owners and application developers. For companies already using a monitoring platform like Splunk, monitoring blindspots arise from the need to prioritize across multiple departments. This report outlines a four-step approach for an effective IT operations monitoring (ITOM) strategy. Download this report to learn: How to reduce monitoring blind spots when creating an ITOM strategy How to address ITOM requirements across IT and non-IT groups Distinct layers across ITOM Potential functionality gaps with domain-specific products
Tags : cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
     Splunk
By: AWS     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
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     AWS
By: Amazon Web Services     Published Date: Sep 05, 2018
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time. This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
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     Amazon Web Services
By: Amazon Web Services     Published Date: Sep 05, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set: • Enterprise-class relational database query and management system • Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools • Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
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     Amazon Web Services
By: Amazon Web Services     Published Date: Sep 05, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technolog
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     Amazon Web Services
By: AWS     Published Date: Sep 04, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
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     AWS
By: AWS     Published Date: Sep 04, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technology
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     AWS
By: SAS     Published Date: Aug 28, 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. 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
By: SAS     Published Date: Aug 28, 2018
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever. Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
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     SAS
By: SAS     Published Date: Aug 28, 2018
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
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     SAS
By: SAS     Published Date: Aug 28, 2018
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
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     SAS
By: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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     SAS
By: Google     Published Date: Aug 23, 2018
Today’s smart computers can beat board game champions, master video games, and learn to recognize cats. No wonder artificial intelligence has captured the imaginations of business and IT leaders. And indeed, AI is starting to transform processes in established industries, from retail to financial services to manufacturing. Read this guide from Google Cloud and learn how you can unlock the transformational power of information and get useful insights from a vast and complex landscape of data.
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     Google
By: Splunk     Published Date: Aug 21, 2018
SIEM (security information and event management) software offers a lot of promise, but legacy SIEMs simply can't keep up with the rate and sophistication of today's cyberattacks. Organizations today require access to analytics-driven SIEMs that combine a big data platform that is optimized for machine data with advanced analytics, threat detection, monitoring tools, incident response tools and multiple forms of threat intelligence. Download your complimentary copy of “The Six Essential Capabilities of an Analytics-Driven SIEM” and learn how to dramatically improve your security posture, advanced threat detection and incident response.
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     Splunk
By: Splunk     Published Date: Aug 21, 2018
Alice Bluebird, a quirky security analyst for hire, is hunting down a nation state of hackers thirsty for the recipes of Frothly, a cutting-edge craft brewery. Follow Alice as she works to solve the mystery of the breach. Did she catch the incident before the hackers managed to steal Frothly’s super secret formulas? Did the hackers act alone or — scarier yet In this light hearted graphic novel “Through the Looking Glass Table”, discover how machine data, as well as an analytics-driven platform, log management, SIEM, UEBA and SOAR solutions, can help anyone — IT managers and sophisticated SOC analysts — better understand and respond to incidents, breaches, phishing attempts, insider threats and more.— did they have help from the inside?
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     Splunk
By: AWS     Published Date: Aug 20, 2018
A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights. Key elements of a modern data warehouse: • Data ingestion: take advantage of relational, non-relational, and streaming data sources • Federated querying: ability to run a query across heterogeneous sources of data • Data consumption: support numerous types of analysis - ad-hoc exploration, predefined reporting/dashboards, predictive and advanced analytics
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     AWS
By: Splunk     Published Date: Aug 17, 2018
IT organizations are now responsible for delivering seamless customer experiences while preventing outages and managing an increasing number of systems. With growing responsibility placed on IT, there is an opportunity to drive strategy for company-wide business processes and operations. Companies using machine data powered platforms like Splunk collect disparate data types to quickly troubleshoot and monitor systems. By adding predictive capabilities, IT can glean critical insights for the business and develop strategic initiatives on issues that matter. Download the white paper “Embracing the Strategic Opportunity of IT” to learn how to: Enable a business aware IT organization Unlock operational efficiencies Solve problems with predictive analytics
Tags : it event management, it event management tool, event logs, aiops platform, what is aiops, aiops vendor, market guide for aiops platforms, guide for aiops platforms, monitor end to end, itoa, aiops, predictive analysis, machine learning, event correlation, event management, it operations analytics, it analytics, ibm watson, hp monitoring, hp operations manager
     Splunk
By: Splunk     Published Date: Aug 17, 2018
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs. Download “The Essential Guide to Infrastructure Machine Data” for: The benefits of machine data for network, remote, web, cloud and server monitoring IT infrastructure monitoring data sources to include in your machine data platform Machine data best practices
Tags : cloud monitoring, aws, azure, gcp, cloud, aws monitoring, hybrid infrastructure, distributed cloud infrastructures, reduce mttr/mtti, cloud monitoring free, cloud monitoring tools, cloud monitoring service, cloud billing monitoring, cloud monitoring architecture, cloud data monitoring, host monitoring, *nix, unix, linux, servers
     Splunk
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