IT is in the midst of one of its major transformations. IDC has characterized this paradigm shift as the “third platform,” driven by innovations in cloud, big data, mobility and social technologies. Progressive enterprises are seeking to leverage third-platform technologies to create new business opportunities and competitive differentiation through new products and services, new business models and new ways of engaging customers.
Effectively using and managing information has become critical to driving growth in areas such as pursuing new business opportunities, attracting and retaining customers, and streamlining operations. In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before.
These needs create a daunting array of workload challenges and place tremendous demands on your underlying IT infrastructure and database systems. This e-book presents six reasons why you should consider a database change, including opinions from industry analysts and real-world customer experiences. Read on to learn more.
IBM DB2 with BLU Acceleration helps tackle the challenges presented by big data. It delivers analytics at the speed of thought, always-available transactions, future-proof versatility, disaster recovery and streamlined ease-of-use to unlock the value of data.
We have been awash in the Financial Services over the past several years of hearing of the next big technology that will change the face of the industry, and Transfer Agency has been no different. In the year 2017 it is the turn of Distributed Consensus Ledger Technology (also known as ‘Blockchain’) to set pulses racing and generate many reams of papers from various parties. Whatever happened to Big Data from 2016?
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Innovative data-driven strategies are enabling organizations to connect with customers and increase operational efficiency as never before. These new initiatives are built on a multitude of applications, such as big-data analytics, supply chain, and factory automation. On average, organizations are now 53% digital as they create new ways of operating and growing their businesses, according to the Computerworld 2017 Forecast Study.
As part of this transformation, enterprises rely increasingly on multivendor, multicloud environments that mix on-premise, private, and public cloud services and workloads. This shift is causing enterprises to increase network capacity; 55% of enterprises in the Computerworld study expect to add network bandwidth in the next 12 months.
The demand for databases is on the rise as organizations build next-generation business applications. NoSQL offers enterprise architecture (EA) pros new choices to store, process, and access new data formats, deliver extreme web-scale, and lower data management costs. Forrester’s 26-criteria evaluation of 15 big data NoSQL solutions will help EA pros understand the choices available and recommend the best for their organization.
This report details our findings about how each vendor fulfills our criteria and where they stand in relation to each other to help EA.
Improved availability of data and new technologies that use it are disrupting our lives, influencing the way we interact with other, and the way we gather and consume information to make decisions. Businesses too are living in a time of continuous technological upheaval. The application of key technologies such as Machine Learning and Artificial Intelligence and Optimization, are fundamentally changing the manner in which businesses make decisions.
This paper is your first step in understanding:
• how you can leverage and operationalize analytics in your everyday business processes
• improve customer relationships
• grow revenue in an increasingly competitive world
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated
drive as you read the morning news. We are getting very close to that kind of
scenario, and companies like Ford expect to have production vehicles in the latter part
Driverless cars are just one popular example of machine learning. It’s also used in
countless applications such as predicting fraud, identifying terrorists, recommending
the right products to customers at the right time, and correctly identifying medical
symptoms to prescribe appropriate treatments.
The concept of machine learning has been around for decades. What’s new is that
it can now be applied to huge quantities of data. Cheaper data storage, distributed
processing, more powerful computers and new analytical opportunities have dramatically
increased interest in machine learning systems. Other reasons for the increased
momentum include: maturing capabilities with methods and algorithms refactored to
run in memory; the
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
A Java application that will successfully be able to retrieve, insert & delete data from our database which will be implemented in HBase along with.Basically the idea is to provide much faster, safer method to transmit & receive huge amounts of data
There’s strong evidence organizations are challenged by the opportunities presented by external information sources such as social media, government trend data, and sensor data from the Internet of Things (IoT). No longer content to use internal databases alone, they see big data resources augmented with external information resources as what they need in order to bring about meaningful change. According to a September 2015 global survey of 251 respondents conducted by Harvard Business Review Analytic Services, 78 percent of organizations agree or strongly agree that within two years the use of externally generated big data will be “transformational.” But there’s work to be done, since only 21 percent of respondents strongly agree that external data has already had a transformational effect on their firms.
Learn how CIOs can set up a system infrastructure for their business to get the best out of Big Data. Explore what the SAP HANA platform can do, how it integrates with Hadoop and related technologies, and the opportunities it offers to simplify your system landscape and significantly reduce cost of ownership.
Published By: HPE APAC
Published Date: Jun 16, 2017
The bar has been raised higher than ever, and the role of IT is evolving to meet it. As a result, IT must support applications and services that make it possible for the business to provide new, diverse customer experiences while generating expanding revenues via the emergent crown jewels of business: big data, cloud, and mobility.
Read on to find out more.
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.
Marketing as you know it will never be the same. There’s a fundamental shift in relationships between brands and customers—fueled by smartphones, social media, and today’s
always-on, always-connected mentality. Marketers have access
to more customer data (big data) than ever before. But the quantity of data only matters if you’re smart about using it—to power 1:1 customer journeys.
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.
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