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Published By: Box     Published Date: Nov 06, 2019
Unstructured business content contains critical information that can be tough to extract, manage, and analyze. Artificial intelligence capabilities can automate many manual processes and help unlock this valuable resource. To find out more download this whitepaper today.
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Box
Published By: Group M_IBM Q3'19     Published Date: Sep 04, 2019
In the last few years we have seen a rapid evolution of data. The need to embrace the growing volume, velocity and variety of data from new technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) has been accelerated. The ability to explore, store, and manage your data and therefore drive new levels of analytics and decision-making can make the difference between being an industry leader and being left behind by the competition. The solution you choose must be able to: • Harness exponential data growth as well as semistructured and unstructured data • Aggregate disparate data across your organization, whether on-premises or in the cloud • Support the analytics needs of your data scientists, line of business owners and developers • Minimize difficulties in developing and deploying even the most advanced analytics workloads • Provide the flexibility and elasticity of a cloud option but be housed in your data center for optimal security and compliance
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Group M_IBM Q3'19
Published By: Automation Anywhere APAC     Published Date: Aug 15, 2019
Bancolombia is an award winning, full-service financial institution that provides banking services to customers in 12 different countries and is one of the 10th largest financial groups in Latin-America.With bots from Automation Anywhere, Bancolombia sifts through structured, semi-structured, and unstructured customer data to transform their BPM. Bots automate hundreds of processes and greatly increasing back office efficiency, saving Bancolombia a significant amount of time servicing customers. This has led to an increase in CSAT numbers and has created additional revenue streams.
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rpa, roi, digital workforce, customer story
    
Automation Anywhere APAC
Published By: Box     Published Date: Jul 29, 2019
Most CIOs today understand that digital transformation initiatives can help streamline business process; boost ef?ciency; increase competitiveness; and, broadly, help their organization become disruptive over the long term. Some of the transformation initiatives under way—even in many pace-setting companies—are struggling to manage the exponential explosion of unstructured data and the associated heightened compliance and security demands, however. Fortunately, new solutions that tap arti?cial intelligence (AI) can extract hidden insights from unstructured data, such as documents, images, videos, and audio ?les. AI is also helping automate many of the labor-intensive processes used to classify, organize, and analyze unstructured content
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Box
Published By: Expert System     Published Date: Jul 26, 2019
As companies increasingly recognize the business implications and actionable benefits of AI, the question becomes: How will you use AI for your business? Thanks to the Cogito platform based on AI algorithms, organizations can effectively support and improve unstructured information management and text analytics in order to: Leverage all information, combining internal knowledge with other information sources to extract relevant data Provide effective and real-time insight on strategic initiatives, partners and any third parties Mitigate and even completely avoid risks for operations, reputation, etc. through information analysis and monitoring Know what competitors are doing and intercept market trends Implement automation for the broader, more complex set of processes that involve data Free up teams to focus on more creative or critical activities inside the organization See the entire business through a different perspective
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Expert System
Published By: SAS     Published Date: Jul 22, 2019
Text is the largest human-generated data source. It grows every day as we post on social media, interact with chatbots and digital assistants, send emails, conduct business online, generate reports and essentially document our daily thoughts and activities using computers and mobile devices. Increasingly, organizations want to know how all of that data can be used to drive improvements. For many, unstructured text represents a massive untapped data source with great potential for producing valuable insights that could result in significant business transformations or spur incredible social innovation. This paper looks at how organizations in banking, health care and life sciences, manufacturing and government are using SAS text analytics to drive better customer experiences, reduce fraud and improve society.
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SAS
Published By: TIBCO Software     Published Date: Jul 22, 2019
Faster answers from unstructured data, improved accuracy of liability estimates, expanded service offerings
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TIBCO Software
Published By: GFT USA, Inc.     Published Date: Jun 26, 2019
Stream is GFT’s architectural framework on GCP that enables real time processing and analysis of structured and unstructured data using AI and Machine Learning (ML) to extract intelligence from data.
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GFT USA, Inc.
Published By: Automation Anywhere APAC     Published Date: Apr 18, 2019
Bancolombia is an award winning, full-service financial institution that provides banking services to customers in 12 different countries and is one of the 10th largest financial groups in Latin-America.With bots from Automation Anywhere, Bancolombia sifts through structured, semi-structured, and unstructured customer data to transform their BPM. Bots automate hundreds of processes and greatly increasing back office efficiency, saving Bancolombia a significant amount of time servicing customers. This has led to an increase in CSAT numbers and has created additional revenue streams.
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Automation Anywhere APAC
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: 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
    
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
    
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
    
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: 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
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
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