Published By: Cisco EMEA
Published Date: Mar 26, 2019
Most organizations have invested, and continue to invest, in people, processes, technology, and policies to meet customer privacy requirements and avoid significant fines and other penalties. In addition, data breaches continue to expose the personal information of millions of people, and organizations are concerned about the products they buy, services they use, people they employ, and with whom they partner and do business with generally. As a result, customers are asking more questions during the buying cycle about how their data is captured, used, transferred, shared, stored, and destroyed. In last year’s study (Cisco 2018 Privacy Maturity Benchmark Study), Cisco introduced data and insights regarding how these privacy concerns were negatively impacting the buying cycle and timelines. This year’s research updates those findings and explores the benefits associated with privacy investment.
Cisco’s Data Privacy Benchmark Study utilizes data from Cisco’s Annual Cybersecurity Benchma
Published By: IBM APAC
Published Date: May 14, 2019
Clients can realize the full potential of artificial intelligence (AI) and analytics with IBM’s deep industry expertise, technology solutions and capabilities and start to infuse intelligence into virtually every business decision and process. IBM’s AI & Analytics Services organization is helping enterprises get their data ready for AI and ultimately achieve stronger data-driven decisions; access deeper insights to provide improved customer care; and develop trust and confidence with AI-powered technologies focused on security, risk and compliance.
Artificial intelligence (AI) is moving beyond the hype cycle, as more and more organizations seek to adopt AI-related technologies. These organizations are focusing on prioritizing functional areas and use cases, placing a stronger emphasis on topline growth, taking up a renewed interest in their data infrastructure and articulating greater unease about the skills of their knowledge workers. This report explores how they are approaching str
Published By: IBM APAC
Published Date: May 14, 2019
Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future.
You will find topics like:
- What is machine learning?
- Explaining the business imperative
- The key machine learning algorithms
- Skills for your data science team
- How businesses are using machine learning
- The future of machine learning
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
As the information age matures, data has become the most
powerful resource enterprises have at their disposal. Businesses
have embraced digital transformation, often staking their
reputations on insights extracted from collected data. While
decision-makers hone in on hot topics like AI and the potential of
data to drive businesses into the future, many underestimate the
pitfalls of poor data governance. If business decision-makers can’t
trust the data within their organization, how can stakeholders and
customers know they are in good hands? Information that is not
correctly distributed, or abandoned within an IT silo, can prove
harmful to the integrity of business decisions.
The MicroStrategy 2018 Global State of Enterprise Analytics Report, which surveyed 500 decision-makers from around the globe, reveals that leading organizations create a competitive edge through their use of data, with 63% experiencing improved efficiency and productivity, 57% realizing faster, more effective decision making, and 51% achieving better financial performance. Other top benefits include improved customer experiences, improved customer acquisition and retention, and the identification and creation of new revenue streams—all keys to digital transformation.
What else did the 2018 report reveal? Read all the insights with both global and geo-specific views here. This infographic shares a few highlights from the new 44-page report which serves as a benchmarking resource for all data-driven organizations.
This year, businesses will increasingly turn to AI to power their business transformation. Machine learning and deep learning workloads are quickly becoming a mission-critical workload in the enterprise data center. As an IT leader, are you ready for the impending wave of AI applications that will demand new approaches to computing, storage and networking? Do you have the right strategy for scaling AI workload in your data center? We’ll introduce you to the IT visionaries who have made it happen. In this webinar we’ll explore how one IT leader accelerated his company’s success with an AI infrastructure strategy, sharing their best practices and insights.
By watching this webinar, you'll learn:
- Why it’s now critical for enterprise IT to have an AI infrastructure strategy that supports the business
- Explore one IT leader’s experience developing and implementing a best-of-breed platform for scaling AI workload in the data center
- Gain insights that can drive your AI infrastructur
Published By: Verisign
Published Date: May 31, 2017
Verisign has a unique view into distributed denial of service (DDos) attack trends, including attack statistics, behavioral trends and future outlook. The below data contains observations and insights about attack frequency and size derived from mitigations enacted on behalf of customers of Verisign DDoS Protection Services from January through March 2017.
Artificial intelligence (AI) and machine learning (ML) are emerging technologies that will transform organizations faster than ever before. In the digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Historically, these insights were discovered through manually intensive data analytics—but the amount of data continues to grow, as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
"Extracting value from data is central to the digital
transformation required for businesses to succeed
in the decades to come. Buried in data are insights
that reveals what your customers need and how
they want to receive it, how sales, manufacturing,
distribution, and other aspects of business operations
are functioning, what risks are arising to threaten
the business, and more. That insight empowers your
businesses to reach new customers, develop and
deliver new products, to operate more efficiently
and more effectively, and even to develop new
business models. "
The bar for success is rising in higher education. University leaders and IT administrators are aware of the compelling benefits of digital transformation overall—and artificial intelligence (AI) in particular. AI can amplify human capabilities by using machine learning, or deep learning, to convert the fast-growing and plentiful sources of data about all aspects of a university into actionable insights that drive better decisions. But when planning a transformational strategy, these leaders must prioritize operational continuity. It’s critical to protect the everyday activities of learning, research, and administration that rely on the IT infrastructure to consistently deliver data to its applications.
Increasingly sophisticated location technology makes it possible for data scientists to gain a deeper understanding of their target audiences – and how to reach them. Accurate and precise intelligence can give more timely and complete insights into audiences than ever before.
As one of the world’s leading location platforms in 2018, HERE shares insights and solutions to buying location data for better audience segmentation.
Published By: Cisco EMEA
Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
FRONTEO provides end-to-end project management, litigation consulting and eDiscovery
solutions for a global base of law firms and corporations. To meet clients’ critical
information needs, the business requires connectivity and data center solutions that can
process, access and interpret terabytes of sensitive data—reliably and at top speeds.
Get actionable insights from experts at FRONTEO and learn how switching to a
high-performance Ethernet solution from Spectrum Enterprise can deliver security
and reliability across your network and IT infrastructure.
Business leaders recognize that their industries and markets are either being disrupted or have the potential to be disrupted by nimble digital players. In order to compete, senior IT leaders need to modernize their data centers.
In this Forbes Insights Executive Briefing, learn how to embrace an agile data infrastructure, modern data protection, and intelligent operations to reap the benefits of a truly modernized data center.
Technology is a fast-moving target, and organizations need agility and flexibility to compete. By modernizing your data center and maximizing the value of your corporate data, your company can gain those qualities.
In this Forbes Insights checklist, learn how to derive value from your corporate data and embrace three elements of a modernized data center: an agile data infrastructure, a modern approach to data protection, and intelligent data center operations.
From its conception, this special edition has had a simple goal: to help SAP customers better understand SAP HANA and determine how they can best leverage this transformative technology in their organization. Accordingly, we reached out to a variety of experts and authorities across the SAP ecosystem to provide a true 360-degree perspective on SAP HANA.
This TDWI Checklist Report presents requirements for analytic DBMSs with a focus on their use with big data. Along the way, the report also defines the many techniques and tool types involved. The requirements checklist and definitions can assist users who are currently evaluating analytic databases and/or developing strategies for big data analytics.
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well thought-out architecture for designing and implementing large-scale DW environments. Since the creation of these DW architectures, there have been many technological advances making implementation faster, more scalable and better performing. This whitepaper explores these new advances and discusses how they have affected the development of DW environments.
New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose built platforms more capable of meeting the real-time needs of a more demanding end user and the opportunities presented by Big Data. Significant strategy shifts are under way to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support Big Data and real-time needs of innovative enterprises companies.
Big data and personal data are converging to shape the internet’s most surprising consumer products. they’ll predict your needs and store your memories—if you let them. Download this report to learn more.
This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.
The technology market is giving significant attention to Big Data and analytics as a way to provide insight for decision making support; but how far along is the adoption of these technologies across manufacturing organizations? During a February 2013 survey of over 100 manufacturers we examined behaviors of organizations that measure effective decision making as part of their enterprise performance management efforts. This Analyst Insight paper reveals the results of this survey.
Credit Union Times is the nation's leading independent source for breaking news and analysis for credit union leaders. For more than 20 years, Credit Union Times has set the standard for editorial excellence and ethical, straight-forward reporting.