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.
Whilst businesses of all kinds are utilizing data analytics, many are still only using it to make simple changes that lead to a set of rigid processes. Whereas the more customer-focused organizations are realizing that to deliver exceptional experiences, they need to be able to react to customer data in real-time and predict what might happen next. And that means going beyond simple analytics.
Read our whitepaper to discover what analyst firm Forrester has identified as the Enterprise Insight Platform, technology designed to enable companies to transform into truly data-driven businesses.
With data and analytics the new competitive battleground, businesses that take advantage will be the leaders; those that do not will fall behind. But with data so distributed, gaining this advantage is a huge challenge. Unless you have data virtualization, a better, faster way to meet your analytic data needs. Read this white paper to learn who needs data virtualization and what kinds of benefits others have achieved.
Despite being knowledgeable about their industry and experienced in running their organizations, the majority of business users lack expertise in analytics and visualization techniques—but that doesn't stop them from wanting to have a go. But making tools easier and more widely accessible is only part of the answer. A better approach is to work both sides of the gap. To make tools that can empower business users to discover and unlock value in their data—and that extend capabilities for experts, so they can share the analytics workload, improve efficiency, and focus on higher level work.
TIBCO Spotfire is the premier data discovery and analytics platform, which provides powerful capabilities for our customers, such as dimension-free data exploration through interactive visualizations, and data mashup to quickly combine disparate data to gain insights masked by data silos or aggregations.
As organisations increasingly leverage data, sophisticated analytics, robotics and AI in their operations, we ask who should be responsible for trusted analytics and what good governance looks like.
Read this report to discover:
• the four key anchors underpinning trust in analytics – and how to measure them
• new risks emerging as the use of machine learning and AI increases
• how to build governance of AI into core business processes
• eight areas of essential controls for trusted data and analytics.
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly
every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile,
social business, cloud, and big data analytics as the pillars. In this new environment, business leaders
are facing the challenge of lifting their organization to new levels of competitive capability, that of
digital transformation — leveraging digital technologies together with organizational, operational, and
business model innovation to develop new growth strategies. One such challenge is helping the
business efficiently reap value from big data and avoid being taken out by a competitor or disruptor
that figures out new opportunities from big data analytics before the business does.
From an IT perspective, there is a fairly straightforward sequence of applications that businesses can
adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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
In today's big data digital world, your organization produces large volumes of data with great velocity. Generating value from this data and guiding decision making require quick capture, analysis and action. Without strategies to turn data into insights, the data loses its value and insights become irrelevant. Real-time data inegration and analytics tools play a crucial role in harnessing your data so you can enable business and IT stakeholders to make evidence-based decisions
Published By: Mindfire
Published Date: May 07, 2010
In this report, results from well over 650 real-life cross-media marketing campaigns across 27 vertical markets are analyzed and compared to industry benchmarks for response rates of static direct mail campaigns, to provide a solid base of actual performance data and information.
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.
The competitive advantages and value of BDA are now widely acknowledged and have led to the shifting of focus at many firms from “if and when” to “where and how.” With BDA applications requiring more from IT infrastructures and lines of business demanding higher-quality insights in less time, choosing the right infrastructure platform for Big Data applications represents a core component of maximizing value. This IDC study considered the experiences of firms using Cisco UCS as an infrastructure platform for their BDA applications. The study found that Cisco UCS contributed to the strong value the firms are achieving with their business operations through scalability, performance, time to market, and cost effectiveness. As a result, these firms directly attributed business benefits to the manner in which Cisco UCS is deployed in the infrastructure.
If your business is like most, you are grappling with data storage. In an annual Frost & Sullivan survey of IT decision-makers, storage growth has been listed among top data center challenges for the past five years.2 With businesses collecting, replicating, and storing exponentially more data than ever before, simply acquiring sufficient storage capacity is a problem.
Even more challenging is that businesses expect more from their stored data. Data is now recognized as a precious corporate asset and competitive differentiator: spawning new business models, new revenue streams, greater intelligence, streamlined operations, and lower costs. Booming market trends such as Internet of Things and Big Data analytics are generating new opportunities faster than IT organizations can prepare for them.
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.
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.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Forrester conducted in-depth surveys with 330 global BI decision-makers and found strong correlations between overall company success and adoption of innovative BI, analytics, and big data tools. In this paper, you will learn what separates the leading companies from the rest when it comes to exploiting innovative technologies in BI and analytics, and what steps you can take to either stay a leader or join their ranks.
Leading companies and technology providers are rethinking the fundamental model of analytics, and the contours of a new paradigm are emerging. The new generation of analytics goes beyond Big Data (information that is too large and complex to manipulate without robust software), and the traditional narrow approach of analytics which was restricted to analysing customer and financial data collected from their interactions on social media. Today companies are embracing the social revolution, using real-time technologies to unlock deep insights about customers and others and enable better-informed decisions and richer collaboration in real-time.
In the age of the customer, businesses realize the need to take their big data insights further than they have before, in order to win, serve, and retain their customers. Today’s modern company has more data than ever before and is now looking to derive insights from the data that will help propel it forward. As firms move data analytics to the cloud, there is a new set of challenges and barriers to overcome, but with the help of insights-platforms-as-a-service, companies will be able to innovate with data and drive business forward.
Understand how infrastructure complexity slows down data delivery and why flash storage alone addresses less than half of slow downs.
HPE makes the data center of the future available today. There are thousands of customers that can benefit from InfoSight predictive analytics within the HPE Nimble Storage and HPE 3PAR platforms. Enterprises depend on data to improve customer interaction, accelerate product development and run the back office.
The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence.
This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
The Connected Customer is an individual who is intimately connected
to the data, outcomes, decisions, and staff associated with any
relationship to an organization. This intensely personal connection is
not just a matter of the most recent transaction, but represents a
combination of connected data, connected analytics, and collaborative
decisions associated with improving the customer’s relationship with
the organization over time.
In this report, Blue Hill explores the key traits associated with
supporting the Connected Customer through the Internet of Things,
and provides guidance on why the Internet of Things will be essential
across the general business landscape.
We have conditioned patients not only to expect opioids for pain relief, but to utilize more and more of them, and the addiction is both psychological and physical. To remedy the situation, a lot of policies and practices and behaviors must change around how the health care system approaches pain. But we do not yet have the data and
analytics we need to determine what specifically to do at the patient level or the policy level. Download this whitepaper to learn more about the resources available and how we can fix this issue.
Insurers have long been plagued by fraud, error, waste, and abuse in health care payments. The costs are huge – amounting to as much as 25 percent of payments made. Today’s data management and
analytics platforms promise breakthroughs by incorporating comparative and behavioral data to predict as well as detect loss in all its forms. To explore the opportunities and how insurers can capitalize on them, IIA spoke with Ben Wright, Sr. Solutions Architect in SAS’s Security Intelligence Global Practice.