Predictive analytics, sometimes called
advanced analytics, is a term used to describe
a range of analytical and statistical
techniques to predict future actions or
behaviors. In business, predictive analytics
are used to make proactive decisions
and determine actions, by using statistical
models to discover patterns in historical
and transactional data to uncover
likely risks and opportunities.
Predictive analytics incorporates a range
of activities which we will explore in this
paper, including data access, exploratory
data analysis and visualization, developing
assumptions and data models, applying
predictive models, then estimating
and/or predicting future outcomes.
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.
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities.
Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes.
Download now to read on.
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities.
Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes.
Download now to read on.
It is important to understand the different business situations where traditional predictive analytics can be best applied and where anticipatory analytics may be a more appropriate approach to solve the business problem. One is not necessarily superior to the other – it’s about which methodology is best utilized in solving the specific business problem. Conventional response modeling, revenue potential modeling, wallet share analysis, traditional risk scoring and other analytic practices will always be important tools for businesses far-and-wide, but as more companies focus on analytics to inform growth, they’ll have employ the right team and identify the right partners to work with in order to successfully leverage anticipatory analytics to gain a competitive advantage.
Looking forward, we anticipate anticipatory analytics playing an important role in your future. Download this white paper to learn more.
