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
In this era of digital transformation, business and IT leaders across all industries are looking for ways to easily and cost-effectively unlock the value of enterprise data and use it to deliver new customer experiences while fueling business growth. The digital economy is changing the way organizations gather information, gain insights, reinvent their businesses and innovate both quickly and iteratively.
Every day, torrents of data inundate IT organizations and overwhelm
the business managers who must sift through it all to
glean insights that help them grow revenues and optimize
profits. Yet, after investing hundreds of millions of dollars into
new enterprise resource planning (ERP), customer relationship
management (CRM), master data management systems (MDM),
business intelligence (BI) data warehousing systems or big data
environments, many companies are still plagued with disconnected,
“dysfunctional” data—a massive, expensive sprawl of
disparate silos and unconnected, redundant systems that fail to
deliver the desired single view of the business.
To meet the business imperative for enterprise integration and
stay competitive, companies must manage the increasing variety,
volume and velocity of new data pouring into their systems from
an ever-expanding number of sources. They need to bring all
their corporate data together, deliver it to end users as quickly as
possible to maximize
Published By: Patsnap
Published Date: Jul 25, 2016
IP data can be used for a lot more than traditional patent searching. In this white paper we look at how the insights from IP can be used to identify and mitigate risks. And we used virtual reality as our guide.
Making key decisions that improve business performance requires more than simple insights. It takes deep data discovery and a keen problem solving approach to think beyond the obvious. As a business leader, you ought to have access to information most relevant to you that helps you anticipate potential business headwinds and craft strategies which can turn challenges into opportunities finally leading to favorable business outcomes.
WNS DecisionPoint , a one-of-its kind thought leadership platform tracks industry segments served by WNS and presents thought-provoking original perspectives based on rigorous data analysis and custom research studies. Coupling empirical data analysis with practical ideas around the application of analytics, disruptive technologies, next-gen customer experience, process
transformation and business model innovation; we aim to arm you with decision support frameworks based on points of fact.
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions.
Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
Traditional project management techniques are not working as well as expected. According to research, an average organization has at least $74 million USD (yearly) in at-risk projects. And, even when projects are executed successfully, all too often they still fail to deliver business benefits.
So, how do you get started? How do you prioritize which issues to address first? What are the risks and potential rewards? What can you do yourself and where can you need help?
This paper helps you answer those questions. It provides important insights into the root causes of project failures and misalignment with business unit expectations, along with possible solutions. It illustrates the path to optimized project and portfolio management from four different starting points, and provides step-by-step advice to help you reach new milestones quickly. It is intended to help you make the transition from managing IT projects to managing business outcomes.
Despite strong wages and steady job growth, without enough skilled employees to fill an onset of retirees, manufacturers face hiring challenges in the days to come. That's why investing in better strategies and technologies to quickly hire available talent is key.
Published By: LogLogic
Published Date: Mar 15, 2012
Garnering critical IT insight helps organizations and individuals make the right decisions to better serve customers, partners, regulatory bodies and internal employees and answer many important business challenges. This whitepaper describes LogLogic's philosophy and evolution of IT Data Management.
Published By: Teradata
Published Date: Jan 30, 2015
Big Data analytics are a top priority at many companies today. Most hope to derive new insights from all available data to improve productivity, cut costs, reduce churn, enhance the customer experience, and seize new business opportunities.
Published By: Teradata
Published Date: Jan 30, 2015
This report is about two of those architectures: Apache™ Hadoop® YARN and Teradata® Aster® Seamless Network Analytical Processing (SNAP) Framework™. In the report, each architecture is described; the use of each in a business problem is illustrated; and the results are compared.
Published By: Teradata
Published Date: Jan 30, 2015
It is hard for data and IT architects to understand what workloads should move, how to coordinate data movement and processing between systems, and how to integrate those systems to provide a broader and more flexible data platform. To better understand these topics, it is helpful to first understand what Hadoop and data warehouses were designed for and what uses were not originally intended as part of the design.
Published By: Genesys
Published Date: Dec 11, 2013
Gartner recently released its Magic Quadrant for Contact Center Workforce Optimization, the annual report analyzing the workforce optimization industry. Gartner positions vendors based on Completeness of Vision and Ability to Execute.
Genesys improved its 2013 position moving from Niche Player to the Challengers Quadrant. This report provides you with insights you need to determine the Workforce Optimization Solution that is right for your organization. Read Now.
The Tenth Annual State of the Network Global Study focuses on a lens on the network team's role in security investigations. When it comes to technology adoption, both cloud and 100 GbE deployment continue to grow aggressively. VoIP adoption is closing in on 60% and software-defined networking is projected to cross the halfway mark, indicating compounding network complexity amidst the ongoing struggle to ID security threats.
Study questions were designed based on interviews with network professionals and IT analysts. Results were compiled from the insights of 1,035 respondents, including network engineers, IT directors and CIOs around the world.
Published By: Steelwedge
Published Date: May 12, 2014
Supply Chain Insights in their study found that more than 80% of the companies they surveyed had an S&OP process. However, most companies have varying levels of maturity with it, and hence, very mixed results. The starting point to building a roadmap for a mature and successful S&OP process is to assess your current process. Gartner’s Maturity Model provides a very good mechanism for it.
Download this whitepaper and learn:
- The six macro trends driving the need for application, network and Internet performance management solutions
- Key insights you've been missing when it comes to your portfolio of online assets, and how they interact with the public Internet
- How you can receive a true "end-to-end" view into Internet performance and ensure availability and implement advanced traffic steering capabilities to your existing infrastructure
Published By: Infomatica
Published Date: Mar 05, 2014
It’s no secret—healthcare is transforming. The transition to value-based care is well underway; healthcare players are feeling the impact and each has a role to play, including you! Moving to a value-driven model demands agility from every person, process and technology. These changes are generating more data than ever, there is a lot of data, in fact IDC Global Health Insights predicted that over the next 10 years, the amount of digital healthcare data created annually will grow 44 fold. Organizations that lead the pack and succeed will be those where clinicians, business leaders and patients are empowered with access to clean, safe and connected data. Learn more about your role in putting information to work…