In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. But data scientists can only go so far without support.
Data science platforms are engines for creating machine-learning solutions. Innovation in this market focuses on cloud, Apache Spark, automation, collaboration and artificial-intelligence capabilities. We evaluate 16 vendors to help you make the best choice for your organization.
This Magic Quadrant evaluates vendors of data science platforms. These are products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions.
Today, all consumers can obtain any
piece of data at any point in time. This
experience represents a significant
cultural shift: the beginning of the
democratization of data.
However, the data landscape is increasing
in complexity, with diverse data types
from myriad sources residing in a mix of
environments: on-premises, in the cloud or
both. How can you avoid data chaos?
Massive shifts within the digital business landscape are sparking immense opportunities and reshaping every sector.
In some cases, complete upheaval is happening at lightning-fast speed. In other instances, digital undercurrents are stirring beneath the surface as organizations scramble to monetize vast volumes and variety of data in an effort to sharpen their competitive edge and not be blindsided by unforeseen events that completely upend existing business models.
While long-standing industry leadership might be no match for the next cool app, agility, speed and the ability to harness more data than was ever imagined is fueling powerful possibilities for reinvention among companies of every size.
Data is following rapidly from mobile devices and social networks, as well as from every connected product, machine and infrastructure. This data holds the potential for deep insights that can replace guesswork and approximations as to locations, behaviors, patterns and preferences. As the w
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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
As of May 25, 2018, organizations around the world—not just
those based in the EU—need to be prepared to meet the
requirements outlined within the EU General Data Protection
Regulation (GDPR). Those requirements apply to any
organization doing business with any of the more than 700
million EU residents, whether or not it has a physical presence
in the EU.
IBM® Security can help your organization secure and protect
personal data with a holistic GDPR-focused Framework that
includes software, services and GDPR-specific tools. With
deep industry expertise, established delivery models and key
insights gained from helping organizations like yours navigate
complex regulatory environments, IBM is well positioned to
help you assess your needs, identify your challenges and get
your GDPR program up and running
Published By: IBM APAC
Published Date: Mar 06, 2019
The Forrester Study on cost savings and business benefits enabled by Watson Studio and Watson Knowledge Catalog.
Watson Studio provides a suite of tools for data scientists, application developers, and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy machine learning models at scale. The Forrester provides readers a framework to evaluate the potential financial impact of the Watson Studio and Watson Knowledge Catalog investment on their organizations.
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
Published By: IBM APAC
Published Date: Sep 30, 2019
Digital technology is changing the financial services industry rapidly with automated process, AI insights, customized experiences, new operating models and next generation applications. In such a scenario how should banks innovate and stay ahead of the game?
This e-book will provide you the best strategies and recommendations for modernizing your IT infrastructure and operations. You’ll learn how to lead disruption and manage rapid change for your bank, its operations and its customers.
Here are the five key takeaways:
• Personalize customer experiences by maximizing your data
• Borrow strategies from open banking and new business models
• Step up your security game
• Drive innovation from the inside out
• Design an agile infrastructure to support participation in new digital marketplaces
Find out more in the e-book.
In the world of value-based healthcare, your data is the key to extracting the most actionable insights that provide real value to your organization. But getting to those insights can prove difficult, especially if you have to connect disparate data sources. You need transparency into key insights that can help your team make more informed decisions for the success of your organization.
In this listicle, we explore five ways an analytics solution can help you transform your organization through the power of insight. From risk modeling to predictive analytics, utilizing the right mix of analytics can improve patient outcomes and ultimately move your organization closer to your ideal value-based care model
To address the volume, velocity, and variety of data necessary for population health management, healthcare organizations need a big data solution that can integrate with other technologies to optimize care management, care coordination, risk identification and stratification and patient engagement. Read this whitepaper and discover how to build a data infrastructure using the right combination of data sources, a “data lake” framework with massively parallel computing that expedites the answering of queries and the generation of reports to support care teams, analytic tools that identify care gaps and rising risk, predictive modeling, and effective screening mechanisms that quickly find relevant data. In addition to learning about these crucial tools for making your organization’s data infrastructure robust, scalable, and flexible, get valuable information about big data developments such as natural language processing and geographical information systems. Such tools can provide insig
Gaps in care in health systems cause higher mortality rates and inflate costs. Download this case study for a closer look at how one health system used IBM CareDiscovery data to prove to their board that an outpatient palliative care service line was viable in both cost savings and quality of care improvement.
Published By: iKnowtion
Published Date: Nov 17, 2011
This highly successful dot-com brand leveraged its customer information assets to understand the broad range of customers attracted to its product offering, as well as how to evaluate each customer's future value potential.
Infinidat has developed a storage platform that provides unique simplicity, efficiency, reliability, and extensibility that enhances the business value of large-scale OpenStack environments. The InfiniBox® platform is a pre-integrated solution that scales to multiple petabytes of effective capacity in a single 42U rack. The platform’s innovative combination of DRAM, flash, and capacity-optimized disk, delivers tuning-free, high performance for consolidated mixed workloads, including object/Swift, file/Manila, and block/Cinder. These factors combine to cut direct and indirect costs associated with large-scale OpenStack infrastructures, even versus “build-it-yourself” solutions. InfiniBox delivers seven nines (99.99999%) of availability without resorting to expensive replicas or slow erasure codes for data protection. Operations teams appreciate our delivery model designed to easily drop into workflows at all levels of the stack, including native Cinder integration, Ansible automation pl
Infrastructure considerations for IT leaders
By 2020, deep learning will have reached a fundamentally different stage of maturity. Deployment and adoption will no longer be confined to experimentation, becoming a core part of day-to-day business operations across most fields of research and industries.
Why? Because advancements in the speed and accuracy of the hardware and software that underpin deep learning workloads have made it both viable and cost-effective. Much of this added value will be generated by deep learning inference – that is, using a model to infer something about data it has never seen before. Models can be deployed in the cloud or data center, but more and more we will see them on end devices like cameras and phones.
Intel predicts that there will be a shift in the ratio between cycles of inference and training from 1:1 in the early days of deep learning, to well over 5:1 by 2020¹. Intel calls this the shift to ‘inference at scale’ and, with inference also taking up
Today 3D CAD models are driving the world's product development processes. Finite Element Analysis, Rapid Prototyping, NC programming, Data Exchange, and other downstream applications rely to a growing extent on the direct use of CAD models to streamline processes saving time and money.
Java applications have been a central technology for enterprises for two decades. This wealth of data, functionality, and knowledge are critical to enterprises. With Java-based applications, modern development can build on a platform that enables cloud-native architectures while simultaneously supporting existing applications. This combination of traditional enterprise-wide monoliths and cloud-based application deployment allows organizations to take advantage of existing knowledge and resources while actively moving toward newer application models.
Digital transformation (DX) is the continuous process by which enterprises adapt to or drive disruptive change by leveraging digital competencies, such as harnessing sensor data or using location, customer profile, and a mobile app to make shopping recommendations. DX reshapes the organization's culture where required; leverages existing processes, systems, and assets; and creates net-new digital capabilities as needed.
With DX, there is the need to embrace new business models and new architectures and technologies that will help an enterprise with customer-facing innovation as well as transition existing systems, processes, organization structure, and relationships to support the transformation.
Cloud, social, big data, and the Internet of Things (IoT) are increasingly central to business decisions as the pace of digitization accelerates. The impact of software-defined networking (SDN), virtualization, and converged and hyperconverged infrastructure within the datacenter is substantial. These technologies add complexity but offer enticing opportunities for new business models, revenue streams, operating efficiencies, and agility that organizations must pursue if they want to remain competitive and viable. This pursuit requires businesses to keep up with current and emerging technologies and applications and transform the ways in which they conduct business. At the core of "keeping up" is an organization's datacenter strategy — with an associated technology and services strategy that will either create industry laggards or accelerate innovators.
Resistance to change is futile. Financial services are becoming more embedded in the banking customer’s everyday life, driving unprecedented levels of change across the industry. The unfolding digital economy is ushering a new era of technology adoption in banking. From cloud to open banking APIs, these play a defining role in enabling banks to create new digital products and services, refresh the bank branch, find new customer segments, and monetize underutilized data and information assets.
Are you ready for the digital revolution? Digital transformation is fundamentally reshaping the way insurers do business.
From automated data integration, mobile implementation and analytics, digital transformation spans across your enterprise operations This white paper provides you with the roadmap you need to build a comprehensive enterprise-wide digital transformation plan that makes your insurance organization not only competitive, but differentiated.
• Understand the forces behind digital disruption: customers, competitors, costs and compliance
• Learn how to map, prioritize and identify opportunities for digital transformation
• Leverage a process maturity model to advance your digital position
The real value of i4.0 comes from the integration of automation, data, analytics, manufacturing and products in a way that unlocks new business and operating models. Are you ready for the next industrial revolution?
Read this report to find out:
• why deep pockets alone won’t ensure i4.0 success
• how to scale up projects and capabilities to drive enterprise-level value
• what capabilities, controls and culture are required to support i4.0 success
• how to unlock value by integrating smart processes and smart products
• how to bring suppliers and value chain players into the i4.0 environment.
DatacenterDynamics is a brand of DCD Group, a global B2B media and publishing company that develops products to help senior professionals in the world's most ICT dependent organizations make risk-based infrastructure and capacity decisions.
Our portfolio of live events, online and print publishing, business intelligence and professional development brands are centred on the complexities of technology convergence. Operating in 42 different countries, we have developed a unique global knowledge and networking platform, which is trusted by over 30,000 ICT, engineering and technology professionals.
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