As organizations continue to produce vast quantities of data, they increasingly need platforms that allow them to analyze, store, and extract meaningful insights from that data. Gartner helps data and analytics leaders evaluate 19 vendors in an increasingly split market.
Download the Gartner Magic Quadrant report and find out more.
Published By: Gigamon
Published Date: Jun 21, 2019
Accelerate your digital transformation journey by giving teams and tools the application visibility needed to monitor and secure modern digital applications.
Download this whitepaper to learn how you can
Isolate and extract application and component traffic across multiple tiers for monitoring,
Provide application metadata to analytics tools, enabling faster detection of customer experience, application performance and security-related issues and send only relevant traffic to the appropriate tools to reduce load and increase effectiveness.
"Malicious cryptomining lets cybercriminals profit at your organization’s expense. No industry is safe from malicious cryptomining - a browser or software-based threat that enables attackers to secretly use an organization's computing power to mine digital currency. This fast-growing threat can lead to degraded system performance, soaring electricity usage, regulatory problems, and vulnerability to future attacks.
View our infographic to find out who they’re targeting and how to protect your network.
Published By: Flexential
Published Date: Jul 17, 2019
This webinar recording led by Flexential V.P. of Network Services Tim Parker and 451 Research Senior. V.P. of Research Kelly Morgan examines how companies are using edge facilities to control and manage content. Parker and Morgan present strategies for solving latency and security concerns, along with use cases from companies using facilities at “the edge” to get the most from their data – without breaking the bank.
The Spice House has a long history of pioneering how spices are
sold. Since 1957, its flagship brick-and-mortar location has been a
go-to hub for grabbing a cup of coffee and taking in the full spice
experience—smelling, grinding, tasting, touching, and talkin’ spices
and their backstories with the owners.
Today, The Spice House sells more than 400 spices, blends, rubs, and
extracts across several retail locations and the ecommerce store. See
how Drip ecommerce CRM helps The Spice House bring a great instore
experience to their online space.
"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. "
Artificial Intelligence (AI) has already begun to improve targeting, segmentation, media buying and planning in the advertising industry. AI algorithms can extract complex patterns from vast numbers of data points, and in so doing, are able to self-correct and learn patterns. The revenue potential that improved personalization, segmentation and targeting that AI provides to marketers is huge.
At HERE Technologies, we are placing AI and machine learning at the center of our products and services. We see the opportunity in automated machine learning to enrich the targeting and effectiveness of mobile advertising campaigns in real time. But the outcome of implementing such technology depends on the quality of data being fed into it from the outset. AI wouldn’t be as helpful if it’s being used alongside questionable location data or audience data.
HERE’s location data provides a strong thread that can be woven throughout every stage of the media buying process, offering more context and
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.
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: OpenText
Published Date: Mar 02, 2017
Watch this webinar with IDC supply chain experts to learn how embedded analytics can provide deeper supply chain intelligence and help you extract maximum value from data for your supply chain operations.
Big data alone does not guarantee better business decisions. Often that data needs to be moved and transformed so Insight Platforms can discern useful business intelligence. To deliver those results faster than traditional Extract, Transform, and Load (ETL) technologies, use Matillion ETL for Amazon Redshift. This cloud- native ETL/ELT offering, built specifically for Amazon Redshift, simplifies the process of loading and transforming data and can help reduce your development time.
This white paper will focus on approaches that can help you maximize your investment in Amazon Redshift. Learn how the scalable, cloud- native architecture and fast, secure integrations can benefit your organization, and discover ways this cost- effective solution is designed with cloud computing in mind. In addition, we will explore how Matillion ETL and Amazon Redshift make it possible for you to automate data transformation directly in the data warehouse to deliver analytics and business intelligence (BI
The industrial Internet of Things (IoT) has made equipment more efficient while improving customer experiences. For the first time, it enables manufacturers to gather and analyze sensor-based data to enhance operations. One industry that is experiencing the benefits of IoT is commercial and industrial washing machine manufacturers.
OctaveTM, Sierra Wireless' new device-to-cloud solution, lets you securely extract, orchestrate, and act on data from your washing equipment at the edge, to the cloud.
Most CIOs today understand that digital transformation initiatives can help streamline business process; boost ef?ciency; increase competitiveness; and, broadly, help their organization become disruptive over the long term.
Some of the transformation initiatives under way—even in many pace-setting companies—are struggling to manage the exponential explosion of unstructured data and the associated heightened compliance and security demands, however.
Fortunately, new solutions that tap arti?cial intelligence (AI) can extract hidden insights from unstructured data, such as documents, images, videos, and audio ?les. AI is also helping automate many of the labor-intensive processes used to classify, organize, and analyze unstructured content
Adversaries and defenders are both developing technologies
and tactics that are growing in sophistication. For their part,
bad actors are building strong back-end infrastructures
with which to launch and support their campaigns. Online
criminals are refining their techniques for extracting money
from victims and for evading detection even as they continue
to steal data and intellectual property.
Forward-thinking enterprises understand what it takes to be successful in this data-rich, increasingly automated economy. According to the Harvard Business Review Analytic Services research report The Rise of Intelligent Automation: TurningComplexity into Profit, sponsored by Oracle, at least 7 in 10 executives understand that predictive analytics (80%) and AI and machine learning (68%) are important for the future of the business.
Even as executives recognize the vital role data plays in their businesses, many are unable to take advantage of the value residing in their data. The old ways of collecting, managing, storing, and analyzing data are no longer effective, and are preventing businesses from extracting potential value. Many simply can’t execute on a data-driven vision.
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes.
This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
Defining the Data Lake
“Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
In today’s markets, customer identities and the personal data associated with them are among the most critical and valuable assets of any enterprise. Managing these digital identities — from first registration and login to the later stages of the customer relationship — and extracting business value from the associated data are complex tasks, commonly referred to as customer identity and access management (CIAM).
When implementing a system to collect, manage, and utilize digital identity and customer data, companies have two basic choices: in-house development or buying a dedicated solution from a vendor specialized in CIAM (i.e., build vs. buy).
Read this white paper for an in-depth analysis of CIAM implementation options, including:
? Must-haves for a successful, enterprise-grade CIAM system
? Pros and cons of implementation options, ranging from in-house software development to commercial off-the-shelf solutions
? A real-world case study that illustrates the ROI of an effective CI
Published By: HPE APAC
Published Date: Feb 23, 2017
Read this extract and the full reports to find out people's perceptions regarding hyperconverged infrastructure and includes overall IT challenges, sentiment around the technology and an in-depth look at key uses cases that are supported by the technology.
Published By: IBM APAC
Published Date: Aug 22, 2017
While working to maintain tactical control of the mobile environment, IT managers often find themselves drowning in minutiae. Overwhelmed by the number of moving parts, they’re unable to stay abreast of the latest threats, let alone extract meaning from or make decisions based on the mountains of data now being collected. With limited IT resources dedicated to mobile technology tools that facilitate reactive rather than proactive management—and limited visibility into mobile intelligence across the organization—many managers have had to choose between security and productivity as the focus of their efforts.
Predictive analytics have been used by different industries for years to solve difficult problems that range from detecting credit card fraud to determining patient risk levels for medical conditions. It combines data mining and machine-learning technologies to create statistical models based on historical data. It then uses these models to predict future events. Extracting the power from the data requires powerful algorithms behind predictive analytics.
As organizations seek to extract more strategic value from their data, many are starting to view data governance as an enabler of insight rather than an impediment to its creation. This report explores a leading approach to data governance and the impact it can have on today’s most data-rich organizations.
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around