structured data

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Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
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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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: Mar 29, 2019
The vast increase in dark data—all of the unstructured data from the Internet, social media, voice and information from connected devices—is overwhelming many executives and leaving them completely unprepared for the challenges their businesses face.
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Group M_IBM Q2'19
Published By: TIBCO Software APAC     Published Date: Feb 14, 2019
Digital business initiatives have expanded in scope and complexity as companies have increased the rate of digital innovation to capture new market opportunities. As applications built using fine-grained microservices and functions become pervasive, many companies are seeing the need to go beyond traditional API management to execute new architectural patterns and use cases. APIs are evolving both in the way they are structured and in how they are used, to not only securely expose data to partners, but to create ecosystems of internal and/or third-party developers. In this datasheet, learn how you can use TIBCO Cloud™ Mashery® to: Create an internal and external developer ecosystem Secure your data and scale distribution Optimize and manage microservices Expand your partner network Run analytics on your API performance
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TIBCO Software APAC
Published By: TIBCO Software APAC     Published Date: Feb 14, 2019
Digital business initiatives have expanded in scope and complexity as companies have increased the rate of digital innovation to capture new market opportunities. As applications built using fine-grained microservices and functions become pervasive, many companies are seeing the need to go beyond traditional API management to execute new architectural patterns and use cases. APIs are evolving both in the way they are structured and in how they are used, to not only securely expose data to partners, but to create ecosystems of internal and/or third-party developers. In this datasheet, learn how you can use TIBCO Cloud™ Mashery® to: Create an internal and external developer ecosystem Secure your data and scale distribution Optimize and manage microservices Expand your partner network Run analytics on your API performance
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TIBCO Software APAC
Published By: Sage EMEA     Published Date: Jan 29, 2019
Transform your finance operations into a strategic, data-driven engine Data inundation and information overload have burdened practically every largescale enterprise today, providing great amounts of detail but often very little context on which executives can act. According to the Harvard Business Review,1 less than half of an organisation’s structured data is actively used in making decisions. The burden is felt profoundly among finance executives, who increasingly require fast and easy access to real-time data in order to make smart, timely, strategic decisions. In fact, 80% of analysts’ time is spent simply discovering and preparing data, and the average CFO receives information too late to make decisions 24% of the time.2
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Sage EMEA
Published By: Gigamon     Published Date: Dec 13, 2018
Read "Understanding the State of Network Security Today" to learn why ESG recommends consolidating security tools through a structured, platform-based approach. Data, analytics and reports from multiple tools can be aggregated and consumed in one control panel, reducing network vulnerabilities. Learn more about challenges, changes and best practices for today’s network security operations and tools. Read now.
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Gigamon
Published By: Pure Storage     Published Date: Dec 05, 2018
With the growth of unstructured data and the challenges of modern workloads such as Apache Spark™, IT teams have seen a clear need during the past few years for a new type of all-flash storage solution, one that has been designed specifically for users requiring high levels of performance in file- and object-based environments. With FlashBlade™, it addresses performance challenges in Spark environments by delivering the consistent performance of all-flash storage with no caching or tiering, as well as fast metadata operations and instant metadata queries.
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Pure Storage
Published By: TIBCO Software     Published Date: Nov 12, 2018
The insurance industry stands on the precipice of change, with waves of innovation and disruption driving new possibilities across all departments, including pricing, underwriting, claims, and fraud. This webinar recording of a live panel debate is ideal for insurance professionals wanting to understand how best to unlock the possibilities created by advanced analytical techniques such as Artificial Intelligence (AI), Machine Learning (ML), and others. This TIBCO and Marketforce webinar on “The Fourth Industrial Revolution in Insurance” includes speakers Ian Thompson, chief claims officer at Zurich; David Williams, chief underwriting officer at AXA; and Clare Lunn, GI fraud director at LV=. The panel discusses: Moving towards the algorithmic insurer: the opportunities created by AI and ML How insurers can become more agile in the face of new innovations and disruptive technologies How the industry can turn structured and unstructured data into insights
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agile insurance, customer experience, digital initiatives, analytical techniques
    
TIBCO Software
Published By: Druva     Published Date: Nov 09, 2018
The rise of virtualization as a business tool has dramatically enhanced server and primary storage utilization. By allowing multiple operating systems and applications to run on a single physical server, organizations can significantly lower their hardware costs and take advantage of efficiency and agility improvements as more and more tasks become automated. This also alleviates the pain of fragmented IT ecosystems and incompatible data silos. Protecting these virtualized environments, however, and the ever-growing amount of structured and unstructured data being created, still requires a complex, on-prem secondary storage model that imposes heavy administrative overhead and infrastructure costs. The increasing pressure on IT teams to maintain business continuity and information governance are changing how businesses view infrastructure resiliency and long-term data retention—they are consequently looking to new solutions to ensure immediate availability and complete protection of the
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Druva
Published By: Druva     Published Date: Nov 09, 2018
The rise of virtualization as a business tool has dramatically enhanced server and primary storage utilization. Protecting these virtualized environments, however, as well as the ever-growing amount of structured and unstructured data being created, still requires a complex, on-prem secondary storage model that imposes heavy administrative overhead and infrastructure costs.
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Druva
Published By: AWS     Published Date: Oct 26, 2018
Today’s organisations are tasked with analysing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organisations are finding that in order to deliver analytic insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store enormous amounts of data in a central location, so it’s readily available to be categorised, processed, analysed, and consumed by diverse groups within an organisation? Since data—structured and unstructured—can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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data, lake, amazon, web, services, aws
    
AWS
Published By: Group M_IBM Q418     Published Date: Sep 10, 2018
LinuxONE from IBM is an example of a secure data-serving infrastructure platform that is designed to meet the requirements of current-gen as well as next-gen apps. IBM LinuxONE is ideal for firms that want the following: ? Extreme security: Firms that put data privacy and regulatory concerns at the top of their requirements list will find that LinuxONE comes built in with best-in-class security features such as EAL5+ isolation, crypto key protection, and a Secure Service Container framework. ? Uncompromised data-serving capabilities: LinuxONE is designed for structured and unstructured data consolidation and optimized for running modern relational and nonrelational databases. Firms can gain deep and timely insights from a "single source of truth." ? Unique balanced system architecture: The nondegrading performance and scaling capabilities of LinuxONE — thanks to a unique shared memory and vertical scale architecture — make it suitable for workloads such as databases and systems of reco
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Group M_IBM Q418
Published 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
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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
Published 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
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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
Published 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
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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
Published By: Amazon Web Services     Published Date: Jul 25, 2018
What is a Data Lake? Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. Download to find out more now.
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Amazon Web Services
Published By: Amazon Web Services     Published Date: Jul 25, 2018
Defining the Data Lake “Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
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Amazon Web Services
Published By: Microsoft     Published Date: Jul 20, 2018
This guide presents a structured approach for designing cloud applications that are scalable, resilient, and highly available. The guidance in this ebook is intended to help your architectural decisions regardless of your cloud platform, though we will be using Azure so we can share the best practices that we have learned from many years of customer engagements. In the following chapters, we will guide you through a selection of important considerations and resources to help determine the best approach for your cloud application: 1. Choosing the right architecture style for your application based on the kind of solution you are building. 2. Choosing the most appropriate compute and data store technologies. 3. Incorporating the ten high-level design principles to ensure your application is scalable, resilient, and manageable. 4. Utilizing the five pillars of software quality to build a successful cloud application. 5. Applying design patterns specific to the problem you are trying to
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Microsoft
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
Tags : 
    
IBM
Published By: IBM     Published Date: Jul 05, 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
Tags : 
    
IBM
Published By: IBM     Published Date: Jun 29, 2018
LinuxONE from IBM is an example of a secure data-serving infrastructure platform that is designed to meet the requirements of current-gen as well as next-gen apps. IBM LinuxONE is ideal for firms that want the following: ? Extreme security: Firms that put data privacy and regulatory concerns at the top of their requirements list will find that LinuxONE comes built in with best-in-class security features such as EAL5+ isolation, crypto key protection, and a Secure Service Container framework. ? Uncompromised data-serving capabilities: LinuxONE is designed for structured and unstructured data consolidation and optimized for running modern relational and nonrelational databases. Firms can gain deep and timely insights from a "single source of truth." ? Unique balanced system architecture: The nondegrading performance and scaling capabilities of LinuxONE — thanks to a unique shared memory and vertical scale architecture — make it suitable for workloads such as databases and systems of reco
Tags : 
    
IBM
Published By: IBM     Published Date: Jun 25, 2018
Vast resources of data are increasingly available, but the sheer volume can overwhelm human capability. By implementing the cognitive system of IBM Watson Discovery into their infrastructure, businesses can extract deeper and more accurate insights by efficiently identifying, collecting and curating structured and unstructured data. Watson Discovery, also capable of creating content collections and custom cognitive applications, can transform organizational processes to extend proprietary content and expert knowledge faster and at greater scales. Read more to learn how Watson Discovery can keep your organization evolving ahead of the competition. Click here to find out more about how embedding IBM technologies can accelerate your solutions’ time to market.
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IBM
Published By: Sage Software     Published Date: Jun 20, 2018
Data inundation and information overload have burdened practically every largescale enterprise today, providing great amounts of detail but often very little context on which executives can act. According to the Harvard Business Review, less than half of an organization’s structured data is actively used in making decisions. The burden is felt profoundly among finance executives, who increasingly require fast and easy access to real-time data in order to make smart, timely, strategic decisions. In fact, 80% of analysts’ time is spent simply discovering and preparing data, and the average CFO receives information too late to make decisions 24% of the time.
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sage enterprise management, bms, erp, bom, enterprise accounting, enterprise intelligence, drp, enterprise, mrp
    
Sage Software
Published By: Infosys     Published Date: Jun 12, 2018
In the wake of data hacks and privacy concerns, enterprises are working extra hard to make sure they secure customer data from external threats. But what about securing data internally? Organizations unknowingly leave a big security hole in their own systems when they fail to have structured internal processes to handle access requests for employees, which could have disastrous implications for data security. A leading US bank sought to move its internal applications to a secure system for a standard and consistent access rights experience. See how Infosys helped and the five key takeaways from the project.
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internal, applications, data, hacks, privacy, enterprises
    
Infosys
Published By: Infosys     Published Date: Jun 12, 2018
Customers today are far more concerned about the contents and origin of a product than ever before. in such a scenario, granting them easy access to product information, via digital initiatives such as SmartLabel™, goes a long way in strengthening customer trust in a brand. But it also means expending several man-hours of effort processing unstructured data, with the possibility of human error. Intelligent automation can help save effort and time, with virtually error-free results. A consumer products conglomerate wanted a smart solution to implement SmartLabel™ compliance. See how Infosys helped and the five key takeaways from the project.
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automation, brand, information, digital, customer
    
Infosys
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