data and analytics

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Published By: Splunk     Published Date: Nov 29, 2018
From protecting customer experience to preserving lines of revenue, IT operations teams face increasingly complex responsibilities and are responsible for preventing outages that could harm the organization. As a Splunk customer, your machine data platform empowers you to utilize machine learning to reduce MTTR. Discover how six companies utilize machine learning and AI to predict outages, protect business revenue and deliver exceptional customer experiences. Download the e-book to learn how: Micron Technology reduced number of IT incidents by more than 50% Econocom provides better customer service by centralizing once-siloed analytics, improving SLA performance and significantly reducing the number of events TransUnion combines machine data from multiple applications to create an end-to-end transaction flow
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predictive it, predictive it tools, predictive analytics for it, big data and predictive analytics
    
Splunk
Published By: Splunk     Published Date: Dec 11, 2018
Predictive IT is a powerful new approach that uses machine learning and artificial intelligence (AI) to predict incidents before they impact customers and end users. By using AI and predictive analytics, IT organizations are able to deliver seamless customer experiences that meet changing customer behavior and business demands. Discover the critical steps required to build your IT strategy, and learn how to harness predictive analytics to reduce operational inefficiencies and improve digital experiences. Download this executive brief from CIO to learn: 5 steps to an effective predictive IT strategy Where AI can help, and where it can’t How to drive revenue and exceptional customer experiences with predictive analytics
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predictive it, predictive it tools, predictive analytics for it, big data and predictive analytics
    
Splunk
Published By: Cisco Umbrella EMEA     Published Date: Sep 02, 2019
"We live and surf in a cyber world where attacks like APT, DDOS, Trojans and Ransomware are common and easy to execute. Domain names are an integral part of any business today and apparently an integral part of an attacker's plan too. Domain names are carriers of malwares, they act as Command and Control servers and malware's ex-filtrate data too. In today's threat landscape - predicting threats, spotting threats and mitigating them is super crucial.. This is called Visibility and Analytics. Watch this on demand session with our Cisco cloud security experts Shyam Ramaswamy and Fernando Ferrari as they talk about how Cisco Umbrella and The Umbrella Research team detect anomalies, block threats and identify compromised hosts. The experts also discuss how effectively Cisco spot, react, filter out IOC, block the network communications of a malware; identify and stop a phishing campaign (unknown ones too). "
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Cisco Umbrella EMEA
Published By: Amazon Web Services     Published Date: Oct 09, 2017
As easy as it is to get swept up by the hype surrounding big data, its just as easy for organisations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt. However, given big data's power to transform business, it's critical that organisations overcome these challenges and realise the value of big data. The cloud can help organisations to do so. Drawing from IDG's 2015 Big Data and Analytics Survey, this white paper analyses the top five challenges companies face when undergoing a big data initiative and explains how they can effectively overcome them.
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amazon, web services, intel, migration, data warehousing, organization optimization, security, software
    
Amazon Web Services
Published By: Amazon Web Services     Published Date: Oct 09, 2017
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 analyse data that addresses many of these challenges. Data Lakes allow an organization to store all of their data, structured and unstructured, in one, centralized repository.
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cost effective, data storage, data collection, security, compliance, platform, big data, it resources
    
Amazon Web Services
Published By: Sage People     Published Date: Jan 26, 2018
Do you know your people as well as you know your customers? Your people’s expectations and the way they work is changing. Employees are more diverse, mobile and technologically-savvy than ever before. HR processes are changing from focusing on transactions to knowing and engaging people. Just as sales and marketing teams use data to develop actionable and informed insights about their customers, you need to do the same in HR to know your people. Everything, from attracting and keeping the best talent, to creating better workplace experiences and increasing employee engagement and productivity, depends on smarter decisions. These in turn rely on more actionable insights. These are only possible through accurate HR data and analytics. They are vital to address the people challenges you face, so you can make smarter decisions.
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Sage People
Published By: Teradata     Published Date: Jan 20, 2015
This Neil Raden and Teradata webinar explores: The business values gained from an integrated view of SAP® and non-SAP® data; Existing solutions and challenges; Requirements for the optimal BI and analytics platform, and; A new solution that simplifies and enhances BI analytics for SAP® ERP data.
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data warehouse, teradata, business value, analytics platform, erp data, data management
    
Teradata
Published By: Teradata     Published Date: Jan 16, 2015
This Neil Raden paper describes the current need for data warehousing, why SAP® BW is an incomplete choice and how Teradata Analytics for SAP® Solutions is a superior option. Download now!
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teradata, sap solutions, data warehouse, extracted data, data management
    
Teradata
Published By: Dassault Systèmes     Published Date: Jul 21, 2017
Obtaining a first-mover competitive advantage or faster time-to-market requires a new wave in analytics. Dassault Systèmes remains a leading innovator in Product Lifecycle Management (PLM) and has invested heavily in analytical technologies to further drive business benefits for its customers in the related areas of planning, simulation, insight and optimization. This white paper examines the challenges peculiar to PLM and why Dassault Systèmes’ EXALEAD offers the most appropriate solution. It also clearly positions EXALEAD PLM Analytics alongside related technologies like BI, data-warehousing and Big Data solutions. Understand and implement PLM Analytics to access actionable information, support accurate decision-making, and drive performance.
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product solutions, lifecycle management, tech products, data management tools, pdm, plm, process automation, product development speed, manufactures
    
Dassault Systèmes
Published By: Teradata     Published Date: May 02, 2017
As companies consider incorporating enterprise-level cloud services offered by Amazon Web Services, Microsoft Azure, Teradata, and others, many challenges arise related to data security, integration into existing data architecture, and analytics activities themselves. Download this article to understand these and other considerations for evaluating and adopting a cloud strategy for your enterprise, and what it means for analytics.
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cloud analytics, cloud development, cloud storage, cloud management, cloud security, cloud services, storage analytics
    
Teradata
Published By: Teradata     Published Date: May 02, 2017
To understand the challenges and opportunities with the Internet of Things, MIT Sloan Management Review’s 2016 Global Executive Study conducted a comprehensive study with business executives, managers, and IT professionals globally. Through the survey results and interviews with subject matter and industry experts, MIT Sloan Management Review was able to reveal the business value and competitive issues from the Internet of Things (IoT). This comprehensive report dives into data sharing with competitors, analytic talent management, and the intersection with governing the data. Analytics is unsurprisingly is shown as the key to IoT success.
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data, sharing, analytics, iot, mit
    
Teradata
Published By: Pure Storage     Published Date: Apr 10, 2019
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-flash storage infrastructure that is built specifically to work with high-powered analytics.
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Pure Storage
Published By: IBM     Published Date: Apr 11, 2016
This paper explores why your business needs the latest operational decision management (ODM) solutions to help turn data insights into action. Discover how IBM Operational Decision Manager software and the IBM Business Process Manager platform work together to: *Recognize patterns that suggest opportunity or risk *Create and shape business events by automating decisions *Bring more dimension and precision to decision making by applying analytics to big data *Help you implement the right business processes by understanding data in context.
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ibm, smarter process, operational decision management, odm, knowledge management, enterprise applications, business technology
    
IBM
Published By: IBM     Published Date: Apr 22, 2016
The upside of disruption: Reinventing business processes, organizations and industries in the wake of the digital revolution
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ibm, ibm big data and analytics, ibm big data, trusted data, big data, data management
    
IBM
Published By: IBM     Published Date: Apr 22, 2016
This white paper explains the value of putting data and insight to work to accelerate an organization’s journey to becoming a Cognitive Business, by enhancing their investments in data and analytics and realizing new opportunities from cognitive systems that understand, reason and learn.
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ibm, ibm analytics, cognitive business, watson, think data, knowledge management, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
Today's data-driven organization is faced with magnified urgency around data volume, user needs and compressed decision time frames. In order to address these challenges, while maintaining an effective analytical culture, many organizations are exploring cloud-based environments coupled with powerful business intelligence (BI) and analytical technology to accelerate decisions and enhance performance.
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ibm, datamart on demand, analytics, cloud, hybrid cloud, business insight, knowledge management, enterprise applications, data management, business technology, data center
    
IBM
Published By: IBM     Published Date: Jul 05, 2016
In an environment where data is the most critical natural resource, speed-of-thought insights from information and analytics are a critical competitive imperative.
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ibm, data warehouse, big data, analytics, data warehouse, business intelligence, knowledge management, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 06, 2016
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
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ibm, mdm, big data, data management, data matching, customer analytics, data center
    
IBM
Published By: IBM     Published Date: Jul 08, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 14, 2016
How do you keep 130,000 guests safely entertained, fed, watered and informed in a sustainable way? Roskilde Festival knew that the critical insights lay hidden in huge volumes of real-time data. The Copenhagen Business School used IBM technologies to build a cloud big data lab that correlates information from multiple sources, delivering valuable insight for planning and running the festival. Download to learn more.
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ibm, datamart on demand, data, analytics, big data, real-time data, cloud, cloud data analytics, knowledge management, enterprise applications, data center
    
IBM
Published By: IBM     Published Date: Jan 27, 2017
High-priority big data and analytics projects often target customer-centric outcomes such as improving customer loyalty or improving up-selling. In fact, an IBM Institute for Business Value study found that nearly half of all organizations with active big data pilots or implementations identified customer-c entric outcomes as a top objective (see Figure 1).1 However, big data and analytics can also help companies understand how changes to products or services will impact customers, as well as address aspects of security and intelligence, risk and financial management, and operational optimization.
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IBM
Published By: IBM     Published Date: Sep 28, 2017
Here are the 6 reasons to change your database: Lower total cost of ownership Increased scalability and availability Flexibility for hybrid environments A platform for rapid reporting and analytics Support for new and emerging applications Greater simplicity Download now to learn more!
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scalability, hybrid environment, emerging applications, rapid reporting
    
IBM
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: Group M_IBM Q1'18     Published Date: Dec 19, 2017
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements. To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
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cloud, database, hybrid cloud, database platform
    
Group M_IBM Q1'18
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
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