advanced data analytics

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Manufacturing Process Optimization

Published By: TIBCO Software     Published Date: Jul 22, 2019
Today, you can improve product quality and gain better control of the entire manufacturing chain with data virtualization, machine learning, and advanced data analytics. With all relevant data aggregated, analyzed, and acted on, sensors, devices, people, and processes become part of a connected Smart Factory ecosystem providing: •? Increased uptime, reduced downtime •? Minimized surplus and defects •? Better yields •? Reduced cost due to better quality •? Fewer deviations and less non-conformance
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TIBCO Software

Critical Capabilities for Analytics and Business Intelligence Platforms

Published By: MicroStrategy     Published Date: Jun 06, 2019
A&BI platforms are transitioning from delivering simple, manual self-service to supporting more advanced, automated analytic use cases via growing, augmented, ML-driven capabilities. Data and analytics leaders should enable broader use cases to increase their investments’ business impact.
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MicroStrategy

Critical Capabilities for Analytics and Business Intelligence Platforms

Published By: MicroStrategy     Published Date: Jun 06, 2019
A&BI platforms are transitioning from delivering simple, manual self-service to supporting more advanced, automated analytic use cases via growing, augmented, ML-driven capabilities. Data and analytics leaders should enable broader use cases to increase their investments’ business impact.
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MicroStrategy

Critical Capabilities for Analytics and Business Intelligence Platforms

Published By: MicroStrategy     Published Date: Apr 11, 2019
A&BI platforms are evolving beyond data visualization and dashboards to encompass augmented and advanced analytics. Data and analytics leaders should enable a broader set of users with new expanded capabilities to increase the business impact of their investments.
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MicroStrategy

DataOps: DevOps for Data Integration

Published By: StreamSets     Published Date: Feb 13, 2019
Enterprise analytics has quickly evolved from a centralized business intelligence function for historical reporting and dashboards to a democratized capability where anyone can access, analyze and act on all available information, often in real-time while employing advanced techniques. But the complex, dynamic and urgent nature of modern data analytics demands a new approach to data integration.
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StreamSets

Critical Capabilities for Analytics and Business Intelligence Platforms

Published By: MicroStrategy     Published Date: Jan 23, 2019
A&BI platforms are evolving beyond data visualization and dashboards to encompass augmented and advanced analytics. Data and analytics leaders should enable a broader set of users with new expanded capabilities to increase the business impact of their investments.
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MicroStrategy

ADLINK and Touch Cloud deliver AI solutions powered by Intel® Vision Products

Published By: Intel     Published Date: Dec 13, 2018
In today’s world, advanced vision technologies is shaping the next era of Internet of Things. However, gathering streaming video data is insufficient. It needs to be timely and accessible in near-real time, analyzed, indexed, classified and searchable to inform strategy—while remaining cost-effective. Smart cities and manufacturing are prime examples where complexities and opportunities have been enabled by vision, IoT and AI solutions through automatic meter reading (AMR), image classification and segmentation, automated optical inspection (AOI), defect classification, traffic management solution—just to name a few. Together, ADLINK, Touch Cloud, and Intel provide a turnkey AI engine to assist in data analytics, detection, classification, and prediction for a wide range of use cases across a broad spectrum of sectors. Learn more about how the Touch Cloud AI brings cost savings, operational efficiency and a more reliable, actionable intelligence at the edge with transformative insi
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Intel

Moving finance forward with advanced data analytics.

Published By: Workday APAC     Published Date: Dec 11, 2018
Our latest global survey revealed a number of barriers between finance and analytics. See what separates finance leaders from data intelligence.
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Workday APAC

DataOps: DevOps for Data Integration

Published By: StreamSets     Published Date: Dec 05, 2018
Enterprise analytics is quickly evolving into a democratized capability where anyone can access and act on all available information, often in real-time employing advanced techniques. But the complex, dynamic and urgent nature of modern data analytics demands a new approach to data integration. This paper proposes that DataOps, the application of DevOps practices to data analytics, is the best way to overcome these challenges to create an iterative build-operate process for data movement. Read this white paper to: Understand how modern data analytics create data integration challenges due to architectural complexity, operational blindness and data drift. Learn how DevOps pillars of automation and monitoring can create higher developer productivity, operational efficiency and business confidence in data. See specific examples of DataOps functionality being applied to data integration across modern architectures.
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StreamSets

Data Lakes, Purposes, Practices, Patterns and Platforms

Published 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

Harvard Business Review Analytic Services: Real-Time Analytics

Published By: SAS     Published Date: Jun 06, 2018
Today’s consumers expect immediate, personalized interactions. To meet these expectations, companies must differentiate their brands through timely, targeted and tailored customer experiences based on real-time data analytics. This report, sponsored by SAS, Intel and Accenture and conducted by Harvard Business Review Analytic Services, looks at how businesses are using advanced customer data analytics, along with real-time analytics and real-time marketing, to enhance their customers’ experiences. Learn why organizations that place a high value on real-time capabilities still struggle to achieve them, what companies can do to ensure success as they adopt and implement real-time analytics solutions, and what benefits successful companies are already seeing.
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SAS

Inside BIGDATA Guide to Predictive Analytics

Published By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes.
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TIBCO Software

Inside BIGDATA Guide to Predictive Analytics

Published By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes. Download now to read on.
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TIBCO Software

Inside BIGDATA Guide to Predictive Analytics

Published By: TIBCO Software     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes. Download now to read on.
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TIBCO Software

Data Lakes, Purposes, Practices, Patterns and Platforms

Published By: SAS     Published Date: Mar 06, 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. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS

5 Data management for analytics best practices  

Published By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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SAS

7 Key Questions to Ask When Selecting an Incident Response Service Provider

Published By: Secureworks ABM UK 2017     Published Date: Oct 23, 2017
SecureWorks provides an early warning system for evolving cyber threats, enabling organisations to prevent, detect, rapidly respond to and predict cyber attacks. Combining unparalleled visibility into the global threat landscape and powered by the Counter Threat Platform — our advanced data analytics and insights engine —SecureWorks minimises risk and delivers actionable, intelligence driven security solutions for clients around the world.
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cyber security, cyber security framework, data security, firewall, general data protection regulation, incident and problem management, information security, intrusion detection
    
Secureworks ABM UK 2017

TDWI Best Practices: Data Lakes, Purposes, Practices, Patterns and Platforms (customized reports)

Published By: SAS     Published Date: Oct 18, 2017
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

Achieving Program Integrity for Health Care Cost Containment Operations

Published By: SAS     Published Date: Oct 18, 2017
Health insurers have long been plagued by issues of fraud, waste, abuse, error and corruption. Taking an enterprise approach to payment integrity – one that combines advanced data management and sophisticated analytics – can help payers detect and prevent fraud; effect positive change in how providers, employees and patients behave; and substantially reduce health care costs. Payers can achieve better outcomes when software support for the core disciplines of payment integrity run on a single platform.
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SAS

Finding the Path to Security in the Big Data Landscape

Published By: IBM     Published Date: Oct 16, 2017
This white paper examines how some of the ways organizations use big data make their infrastructures vulnerable to attack. It presents recommended best practices organizations can adopt to help make their infrastructures and operations more secure. And it discusses how adding advanced security software solutions from IBM to their big-data environment can fill gaps that big-data platforms by themselves do not address. It describes how IBM® Security Guardium®, an end-to- end solution for regulatory compliance and comprehensive data security, supports entitlement reporting; user-access and activity monitoring; advanced risk analytics and real-time threat detection analytics; alerting, blocking, encryption and other data protection capabilities, as well as automated compliance workflows and reporting capabilities, to stop threats.
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security, big data, ibm, data protection
    
IBM

Pedal To The Metal Guide

Published By: Adobe     Published Date: Aug 02, 2017
With the advanced analytics capabilities in Adobe Analytics and the testing and targeting capacity of Adobe Target, it’s easier than ever to realise the potential of data-driven marketing. From creating a complete view of each customer across touchpoints and along their journey, to using predictive analytics, advanced anomaly detection and machine learning to understand behaviours and needs, you can use data to plan, create and optimise the experiences that matter to you and your customers.
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data management, data system, business development, software integration, resource planning, enterprise management, data collection
    
Adobe

The Evolving Face of Cyber Threats

Published By: IBM     Published Date: Jul 26, 2017
The headlines are ablaze with the latest stories of cyberattacks and data breaches. New malware and viruses are revealed nearly every day. The modern cyberthreat evolves on a daily basis, always seeming to stay one step ahead of our most capable defenses. Every time there is a cyberattack, government agencies gather massive amounts of data. To keep pace with the continuously evolving landscape of cyberthreats, agencies are increasingly turning toward applying advanced data analytics to look at attack data and try to gain a deeper understanding of the nature of the attacks. Applying modern data analytics can help derive some defensive value from the data gathered in the aftermath of an attack, and ideally avert or mitigate the damage from any future attacks.
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cyber attacks, data breach, advanced data analytics, malware
    
IBM

Competing Smarter with Advanced Data Analytics

Published By: SAP     Published Date: Jul 18, 2016
Companies are finding more success with advanced analytics initiatives, allowing for continued innovation and experimentation. Read the report learn key findings from the survey carried out by The Economist Intelligence Unit, with Sponsorship by SAP.
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SAP

TDWI Best Practices Report: Next-Generation Analytics and Platforms

Published By: Pentaho     Published Date: Apr 28, 2016
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
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pentaho, best practices, hadoop, next generation analytics, platforms, infrastructure, data, analytics in organizations
    
Pentaho

Using Data to Drive Profit

Published By: IBM     Published Date: Apr 14, 2016
Advanced analytics can provide extremely valuable insight into today’s media viewers. This must-read report details the top 10 best practices for successfully implementing data analytics for driving profit, attracting new viewers, and increasing viewer loyalty.
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ibm, data analytics, analytics, media, entertainment
    
IBM
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