business intelligence

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Published By: IBM     Published Date: Sep 11, 2017
This book is written for readers who have varying levels of familiarity with ODM. It doesn’t focus on any particular vendor’s offering; instead, it talks about the features of ODM as a model for managing operational decision-making. This book isn’t about offline business intelligence systems. While those systems are very valuable, the focus of this book is on automated decisions that can be executed in real time in conjunction with your business applications and processes.
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odm, management, operation-decision making, intelligence systems
    
IBM
Published By: Lookout     Published Date: Aug 28, 2017
The world has changed. Yesterday everyone had a managed PC for work and all enterprise data was behind a firewall. Today, mobile devices are the control panel for our personal and professional lives. This change has contributed to the single largest technology-driven lifestyle change of the last 10 years. As productivity tools, mobile devices now access significantly more data than in years past. This has made mobile the new frontier for a wide spectrum of risk that includes cyber attacks, a range of malware families, non-compliant apps that leak data, and vulnerabilities in device operating systems or apps. A secure digital business ecosystem demands technologies that enable organizations to continuously monitor for threats and provide enterprise-wide visibility into threat intelligence.
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data protection, mobile risks, productivity tools, cyber attacks, device vulnerabilities
    
Lookout
Published By: IBM     Published Date: Aug 24, 2017
Data governance is all about managing data, by revising that data to standardize it and bring consistency to the way it is used across numerous business initiatives. What’s more, data governance ensures that critical data is available at the right time to the right person, in a standardized and reliable form. A benefit that fuels better organization of business operations, resulting in improved productivity and efficiency of that organization. Thus, the importance of proper data governance cannot be understated. The concepts of data governance have evolved, where the first iteration of data governance, often referred to as version 1.0, focused on three simplistic elements: objectives, structure and processes; having a limited focus and scope due to its tactical usage. The opportunity from the growing value of data in the realm of analytics, business intelligence, and generating insights was left unrealized. Today, organizations are moving towards what can be called Data Governance 2.0,
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ibm, unified governance strategy, data management, data governance
    
IBM
Published By: IBM     Published Date: Aug 23, 2017
To compete in today’s fast-paced business climate, enterprises need accurate and frequent sales and customer reports to make real-time operational decisions about pricing, merchandising and inventory management. They also require greater agility to respond to business events as they happen, and more visibility into business activities so information and systems are optimized for peak efficiency and performance. By making use of data capture and business intelligence to integrate and apply data across the enterprise, organizations can capitalize on emerging opportunities and build a competitive advantage.
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ibm, data replication, inventory management, competitive advantage
    
IBM
Published By: CA Technologies     Published Date: Aug 22, 2017
Privileged user accounts—whether usurped, abused or simply misused—are at the heart of most data breaches. Security teams are increasingly evaluating comprehensive privileged access management (PAM) solutions to avoid the damage that could be caused by a rogue user with elevated privileges, or a privileged user who is tired, stressed or simply makes a mistake. Pressure from executives and audit teams to reduce business exposure reinforces their effort, but comprehensive PAM solutions can incur hidden costs, depending on the implementation strategy adopted. With multiple capabilities including password vaults, session management and monitoring, and often user behavior analytics and threat intelligence, the way a PAM solution is implemented can have a major impact on the cost and the benefits. This report provides a blueprint for determining the direct, indirect and hidden costs of a PAM deployment over time.
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CA Technologies
Published By: Akamai Technologies     Published Date: Aug 17, 2017
The cloud is a network of servers housing data, software, and services. Cloud services are commonly accessed via the Internet, instead of locally in a data center. Businesses are increasingly relying on the cloud for cybersecurity for two key reasons: 1. Due to a changing threat landscape, there’s a need for more scale, accuracy, experience, and collective intelligence. These resources are out of reach internally for most organizations. 2. There are fundamental limits with on-premises hardware mitigation appliances and enterprise data centers for Distributed Denial of Service (DDoS) and web attack protection.
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cloud security, scale, data center, ddos
    
Akamai Technologies
Published By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
Productivity gains are related to the rapid adoption and optimization of applications and software. HP helps small and midsize businesses increase the productivity of their application investments through the use of the HP ProLiant Gen9 servers.
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best practices, business optimization, business management, application management, enterprise application, business intelligence, productivity
    
Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
Mobility, social media, analytics and the cloud are revolutionizing how data is accessed, used, and secured for small to midsize businesses. With data security threats are on the rise, keep your business running with Hewlett Packard Enterprise.
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best practices, business optimization, business management, application management, enterprise application, business intelligence, productivity
    
Hewlett Packard Enterprise
Published By: IBM     Published Date: Jul 26, 2017
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. 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-centric 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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customer analytics, data matching, big data, competitive advantage, customer loyalty
    
IBM
Published By: IBM     Published Date: Jul 26, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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scalability, data warehousing, resource planning
    
IBM
Published By: IBM     Published Date: Jul 26, 2017
To compete in today’s fast-paced business climate, enterprises need accurate and frequent sales and customer reports to make real-time operational decisions about pricing, merchandising and inventory management. They also require greater agility to respond to business events as they happen, and more visibility into business activities so information and systems are optimized for peak efficiency and performance. By making use of data capture and business intelligence to integrate and apply data across the enterprise, organizations can capitalize on emerging opportunities and build a competitive advantage. The IBM® data replication portfolio is designed to address these issues through a highly flexible one-stop shop for high-volume, robust, secure information replication across heterogeneous data stores. The portfolio leverages real-time data replication to support high availability, database migration, application consolidation, dynamic warehousing, master data management (MDM), service
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ibm, infosphere, data replication, security, data storage
    
IBM
Published By: Netsuite     Published Date: Jul 24, 2017
Baseball has always collected in-game data. However, until recently, fans didn’t have easy access to the various statistics that coaches used for key decisions important for the development of the players and success on the baseball field. It’s not unlike how traditional business intelligence is delivered. Data and reports are set aside for a few experts who determine what is important for you. Today, baseball statistics are widely available during broadcasts on TV, PCs and mobile devices. Basic data displays like inning and score are enhanced with metrics meaningful to students of the game, such as pitch speed, strikeout percentages and hit zones. It’s a good example of vital information being delivered in real-time, on demand and in context.
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Netsuite
Published By: Dassault Systčmes     Published Date: Jul 21, 2017
Project management relies primarily on past performance to predict future results, however many companies still lack forward-looking capabilities to predict project outcomes and ensure success. Enhancing project management with PLM analytics offers the opportunity to switch from task-based activities to performance-driven ones to improve success rates. Use PLM Analytics to: • Gain actionable insight and valuable intelligence • Dramatically boost business value and improve project management performance • Reduce error-prone behavior like manual data collection • Leverage big-data capabilities and project intelligence Learn how to extend the value of your PLM investment and improve business performance for your company.
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product solutions, lifecycle management, tech products, data management tools, pdm, plm, process automation, product development speed, manufactures
    
Dassault Systčmes
Published By: FICO - APAC     Published Date: Jul 10, 2017
Historically, manufacturers have “looked to the past” to help predict what they need to do in the future. This would include basic business intelligence, powered by spreadsheets, and even manual processes.
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FICO - APAC
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