As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them.
Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include:
Why data science should not be treated like engineering.
How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle.
Why agility and special hardware to support burst computing are so important to data science break
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform.
This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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.
Learn how predictive analytics and machine learning can help optimize application performance and meet the needs of the business with Nimble Storage Infosight.
This ESG report highlights:
• Optimal application performance and delivery is difficult to achieve in complex environments.
• Many IT infrastructure and operations teams are stretched to the breaking point.
• Predictive analytics and machine learning can be applied to great effect.
A fundamental people-process-technology transformation enables businesses to remain
competitive in today’s innovation economy. Initiatives such as advanced security, fraud detection
services, connected consumer Internet of Things (IoT) devices, augmented or virtual reality
experience, machine and deep learning, and cognitively enabled applications drive superior
business outcomes such as predictive marketing and maintenance.
Superior business outcomes require businesses to consider IT a core competency. For IT, an
agile, elastic, and scalable IT infrastructure forms the crucial underpinning for a superior service
delivery model. The more up to date the infrastructure, the more capable it is of supporting the
scale and complexity of a changing application landscape. Current-generation applications must
be supplemented and eventually supplanted with next-generation (also known as cloud-native)
applications — each with very different infrastructure requirements. Keeping infrastructure up
Published By: Dell EMC
Published Date: May 12, 2016
Businesses face greater uncertainty than ever. Market conditions, customer desires, competitive landscapes, and regulatory constraints change by the minute. So business success is increasingly contingent on predictive intelligence and hyperagile responsiveness to relentlessly evolving demands. This uncertainty has significant implications for the data center — especially as business becomes pervasively digital. IT has to support business agility by being more agile itself. It has to be able to add services, scale capacity up and down as needed, and nimbly remap itself to changes in organizational structure.
A fundamental people-process-technology transformation enables businesses to remain competitive in today’s innovation economy. Initiatives such as advanced security, fraud detection services, connected consumer Internet of Things (IoT) devices, augmented or virtual reality experience, machine and deep learning, and cognitively enabled applications drive superior business outcomes such as predictive marketing and maintenance. Superior business outcomes require businesses to consider IT a core competency. For IT, an agile, elastic, and scalable IT infrastructure forms the crucial underpinning for a superior service delivery model.
Dell EMC’s Intelligent Automation powered by Intel® Xeon® Platinum processor simplifies the management and maintenance of its PowerEdge server hardware. Designed to drive down the cost and resources associated with server lifecycle management, Intelligent Automation relies on integrated Dell Remote Access Controller(iDRAC) and OpenManage server management soft
Predictive analytics transforms organizations. Watch this video to see how predictive analytics can improve outcomes in four strategic areas critical to the success of your business:
- Customer satisfaction and retention
- More effective HR processes
- Fraud and threat detection and prevention
- Revenue growth and profitability
The synergy between predictive analytics and decision optimization is critical to good decision making. Predictive analytics offers insights into likely future scenarios, and decision optimization prescribes best-action recommendations for how to respond to those scenarios given your business goals, business dynamics, and potential tradeoffs or consequences.
Together, predictive analytics and decision optimization provide organizations with the ability to turn insight into action—and action into positive outcomes.
In this white paper, you’ll gain a better understanding of:
The difference between predictive and prescriptive analytics
How predictive and prescriptive actions complement one another to help you achieve optimized business decisions
IBM’s approach to creating a powerful end-to-end decision management system
Many companies can't predict which customer they will retain or which customers will increase their spend. With predictive analytics they can.
This knowledge brief from Aberdeeon Group highlights research findings that show organizations which apply predictive analytics are able to:
Establish timely and accurate insights into customer behavior.
Empower employees to do their jobs more effectively.
Encourage more repeat business and higher wallet share
Predictive analytics has come of age. Organizations that want to build and sustain competitive advantage now consider this technology to be a core practice.
In this white paper, author Eric Siegel, PhD, founder of Predictive Analytics World, reveals seven strategic objectives that can only be fully achieved with predictive analytics.
Read this paper to learn how your organization can more effectively:
Compete – Secure the most powerful and unique competitive stronghold
Grow – Increase sales and retain customers competitively
Enforce – Maintain business integrity by managing fraud
Improve – Advance your core business capacity competitively
Satisfy – Meet today's escalating consumer expectations
Learn – Employ today's most advanced analytics
....and finally, render your business intelligence and analytics actionable.