To improve safety and mobility across its 5,600km road network, the City of Toronto forged a partnered with HERE Technologies for the provision of traffic, incident, and historical traffic data.
Access to this data allows the city authority to see exactly what’s happening on its roads and more easily and affectively run studies on improvement projects.
This case study details how HERE Technologies enabled the City of Toronto’s transportation team to:
Work smarter with comprehensive network coverage and accurate data to aid analysis
Examine the impact of city projects without significant forward planning or expenditure
Ensure travel volume models used to drive decision making are calibrated to represent real-world truths
Technology plays a key role in online shopping, where online retailers gain a greater understanding of their customers through data from their browsing and purchasing habits. Today, when consumers shop in brick-and-mortar stores, they expect the same personalized and responsive service.
To help retailers achieve this level of service, a combination of hardware and software—Intel® Vision Accelerator Design products, cameras, AI deep learning video analysis technology— do the work for you.
Uncover how Advantech system uses the Intel Vision Accelerator Design with Intel Movidius VPU to drive
• Overall store performance such as the number of visitors and transactions, point-of-sale data, sales per shopper and the store’s ranking, and can distinguish traffic patterns by weather and time of day
• Traffic and sales analysis for better staff allocation and marketing-event planning
• Store heatmap analysis for more precise merchandise placement and product promotion
Advanced image analysis and computer vision are key components of today’s AI revolution and is becoming critical for a wide range of industry applications, including healthcare, where this technology is being used to detect anomalies and improve patient care. Due to a lack of integrated tools and experience with these cutting-edge technologies, however, deploying complete systems is difficult.
Applications that utilize deep learning approaches often require large amounts of highly parallel compute power, storage, and networking capabilities, along with performance optimizations for faster data analysis. The Intel and QNAP/IEI solution combines all these elements in one complete system for scalable data management for hospitals and clinics of all sizes.
Read more on Intel’s and QNAP/IEI’s real-world use case on macular degeneration analysis through high-performance computing, vision capabilities, storage, and networking in a single solution.
Published By: BMC ASEAN
Published Date: Dec 18, 2018
Big data projects often entail moving data between multiple cloud and legacy on-premise environments. A typical scenario involves moving data from a cloud-based source to a cloud-based normalization application, to an on-premise system for consolidation with other data, and then through various cloud and on-premise applications that analyze the data. Processing and analysis turn the disparate data into business insights delivered though dashboards, reports, and data warehouses - often using cloud-based apps.
The workflows that take data from ingestion to delivery are highly complex and have numerous dependencies along the way. Speed, reliability, and scalability are crucial. So, although data scientists and engineers may do things manually during proof of concept, manual processes don't scale.
Published By: Hitachi
Published Date: Jun 15, 2017
This paper provides data and analysis to offer perspective on how digital transformation could be impacting businesses and their infrastructure choices, and to better understand the key requirements enterprise IT decision-makers should consider as they begin or continue their digital transformation journey.
Today, when you make decisions about information technology (IT) security priorities, you must often strike a careful balance between business risk, impact, and likelihood of incidents, and the costs of prevention or cleanup. Historically, the most well-understood variable in this equation was the methods that hackers used to disrupt or invade the system.
The Business Case for Data Protection, conducted by Ponemon Institute and sponsored by Ounce Labs, is the first study to determine what senior executives think about the value proposition of corporate data protection efforts within their organizations. In times of shrinking budgets, it is important for those individuals charged with managing a data protection program to understand how key decision makers in organizations perceive the importance of safeguarding sensitive and confidential information.
The SAP HANA platform provides a powerful unified foundation for storing, processing, and analyzing structured and unstructured data. It funs on a single, in-memory database, eliminating data redundancy and speeding up the time for information research and analysis.
Published By: Cisco EMEA
Published Date: Mar 05, 2018
The operation of your organization depends, at least in part, on its data.
You can avoid fines and remediation costs, protect your organization’s reputation and employee morale, and maintain business continuity by building a capability to detect and respond to incidents effectively.
The simplicity of the incident response process can be misleading. We recommend tabletop exercises as an important step in pressure-testing your program.
Today’s leading-edge organizations differentiate themselves through analytics to further their competitive advantage by extracting value from all their data sources. Other companies are looking to become data-driven through the modernization of their data management deployments. These strategies do include challenges, such as the management of large growing volumes of data. Today’s digital world is already creating data at an explosive rate, and the next wave is on the horizon, driven by the emergence of IoT data sources. The physical data warehouses of the past were great for collecting data from across the enterprise for analysis, but the storage and compute resources needed to support them are not able to keep pace with the explosive growth. In addition, the manual cumbersome task of patch, update, upgrade poses risks to data due to human errors. To reduce risks, costs, complexity, and time to value, many organizations are taking their data warehouses to the cloud. Whether hosted lo
Published By: Cisco EMEA
Published Date: Mar 14, 2018
What if defenders could see the future? If they knew an attack was coming, they could stop it, or at least mitigate its impact and help ensure what they need to protect most is safe. The fact is, defenders
can see what’s on the horizon. Many clues are out there—and obvious.
For years, Cisco has been warning defenders about escalating cybercriminal activity around the globe.
In this, our latest annual cybersecurity report, we present data and analysis from Cisco threat researchers and several of our technology partners about attacker behavior observed over the past 12 to 18 months.
Powered by data from 451 Research, the Right Mix web application benchmarks your current private vs public cloud mix, business drivers, and workload deployment venues against industry peers to create a comparative analysis. See how your mix stacks up, then download the 451 Research report for robust insights into the state of the hybrid IT market.
Published By: Pentaho
Published Date: Nov 04, 2015
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.
The demands on IT today are staggering. Most organizations depend on their data to drive everything from product development and sales to communications, operations, and innovation. As a result, IT departments are charged with finding a way to bring new applications online quickly, accommodate massive data growth and complex data analysis, and make data available 24 hours a day, around the world, on any device. The traditional way to deliver data services is with separate infrastructure silos for various applications, processes, and locations, resulting in continually escalating costs for infrastructure and management. These infrastructure silos make it difficult to respond quickly to business opportunities and threats, cause productivity-hindering delays when you need to scale, and drive up operational costs.
Patients are going digital — and taking the healthcare system with them. Learn how in the 2017 Digital Trends in Healthcare and Pharma report.
Download it now to learn:
Why two-thirds of healthcare companies are investing in data analysis.
How they’re building content marketing programs to boost patient knowledge.
What they plan to do with virtual and augmented reality this year and beyond.
Published By: Oracle CX
Published Date: Oct 19, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions
of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by
the aggressive build-out for cloud computing. Big data and machine learning applications that perform
tasks such as fraud and intrusion detection, trend detection, and click-stream and social media
analysis all require forward-thinking solutions and enough compute power to deliver the performance
required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of
business up, and organizations need to support their customers with real-time data. The task of
managing sensitive information while capturing, analyzing, and acting upon massive volumes of data
every hour of every day has become critical.
These challenges have dramatically changed the way that IT systems are architected, provisioned,
and run compared to the past few decades. Most companies
Published By: Oracle CX
Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s
databases, accessing and using the right information at the right time has
become increasingly critical. Real-time access and analysis of operational
data is key to making faster and better business decisions, providing
enterprises with unique competitive advantages. Running analytics on
operational data has been difficult because operational data is stored in row
format, which is best for online transaction processing (OLTP) databases,
while storing data in column format is much better for analytics processing.
Therefore, companies normally have both an operational database with data
in row format and a separate data warehouse with data in column format,
which leads to reliance on “stale data” for business decisions. With Oracle’s
Database In-Memory and Oracle servers based on the SPARC S7 and
SPARC M7 processors companies can now store data in memory in both
row and data formats, and run analytics on their operatio
Published By: Oracle CX
Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been
top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar
database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as
quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data
from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure
1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP
database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks
can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data.
In-memory databases have helped address p
Published By: Delphix
Published Date: May 03, 2016
Data security is a top concern these days. In a world of privacy regulation, intellectual property theft, and cybercrime, ensuring data security and protecting sensitive enterprise data is crucial.
Only a data masking solution can secure vital data and enable outsourcing, third-party analysis, and cloud deployments. But more often than not, masking projects fail. Some of the best data masking tools bottleneck processes and once masked, data is hard to move and manage across the application development lifecycle.
Published By: Dell EMC
Published Date: Oct 08, 2015
Download this whitepaper for:
• An overview of how manufacturing can benefit from the big data technology stack
• A high-level view of common big data pain points for manufacturers
• A detailed analysis of big data technology for manufacturers
• A view as to how manufacturers are going about big data adoption
• A proven case study with: Omneo
Forrester presents the relevant endpoint security data from their most recent surveys, with special attention given to those trends affecting SMBs (firms with 20 to 999 employees) and enterprises (firms with 1,000+ employees), along with analysis that explains the data in the context of the overall security landscape. As organizations prepare for the 2015 budget cycle, security and risk (S&R) professionals should use this annual report to help benchmark their organization’s spending patterns against those of their peers — while keeping an eye on current trends affecting endpoint security — in order to strategize their endpoint security adoption decisions. Please download this Forrester Research report, offered compliments of Dell, for more information.
The explosion of Big Data represents an opportunity to leverage trending attitudes in the marketplace to better segment and target customers, and enhance products and promotions. Success requires establishing a common business rationale for harnessing social media and determining a maturity model for sentiment analysis to assess existing social media capabilities.
With sophisticated analytics, government leaders can pinpoint
the underlying value in all their data. They can bring it together
in a unified fashion and see connections across agencies to
better serve citizens.