To help enterprises create trusted insight as the volume, velocity and variety of data continue to explode, IBM offers several solutions designed to help organizations uncover previously unavailable insights and use them to support and inform decisions across the business.
Automating for Digital Transformation: Tools-driven DevOps and Continuous Software Delivery in the Enterprise. Learn why companies are adopting DevOps to speed software delivery and innovation - Understand why “speed with quality” is the real challenge of Continuous Delivery and the importance of automation - Explore DevOps best practices and automation tools in place at high-performing companies
Across industries, customers today wield more power and greater choice than ever before – a power that they exercise by shifting their attention and spending (across multiple channels/devices in near-real time) from provider to provider. In this environment of hyper-connectedness and diminishing customer loyalty and fleeting human attention spans, the battle for holding customer interest and engaging him/her meaningfully requires, first and foremost, an everlasting commitment to "relevance. "Being relevant and demand-driven is of paramount importance to the media industry, but it is no less important to any industry that operates in a B2C world. However, adapting to the ever-rising expectations and changing buying behaviors of customers is becoming a Herculean task, requiring both business and technology transformation.
The data-driven organization is the new benchmark for success. Firms that harness data to dictate strategic and tactical decisions companywide make more informed business plans, better optimize operations, improve customer interactions, and provide competitive edge. To achieve these benefits, organizations increasingly see data refinement - transforming raw data from various sources into relevant and actionable information and delivering it through self - service access to any user who needs it - as the path toward success by helping break though immature processes and legacy systems. However, data refinement only functions as well as the strategies and approaches behind it. Organizations that do not understand the right way to embrace refinement will fail to catch up to competitors that have mastered the correct approach.
What is the total economic impact of IBM Datacap? This Forrester paper examines the total economic impact and potential ROI that enterprises may realize and provides a framework for how to measure financial impact in their organizations.
In our 21-criteria evaluation of the dynamic case management (DCM) market, we identified the 14 most significant software vendors — Appian, bpm’online, Column Technologies, DST Systems, Eccentex, IBM, Isis Papyrus, Lexmark Enterprise Software, MicroPact, Newgen Software, OnBase by Hyland, OpenText, Pegasystems, and TIBCO Software — and researched, analyzed, and scored them. The evaluation focused on providers’ adaptive, analytics, and mobile features, all critical to helping enterprises tackle increasing volumes of varied and unstructured work. This report helps enterprise architecture (EA) professionals select the best providers to meet their unique needs.
In June 2014, IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying the IBM PureApplication System. The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of the IBM PureApplication System on their organizations.
IBM commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by migrating from open source Java EE application servers to WebSphere Application Server (WAS) Liberty. The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of a WAS Liberty migration on their organizations.
This video demonstrates how IBM’s Behavior Based Customer Insight for Banking leverages predictive analytics to help you personalize customer engagement and deliver customized actions. The solution leverages advanced predictive models to analyze customer transactions and spending behavior to more deeply understand customer needs and propensities, anticipate life events, and help provide a unique customer experience.
Learn new ways of analyzing digitally connected customers-from dynamic segmentation to the use of advanced analytics. With predictive tools, banks can analyze transactions and spending behavior to better understand customer needs, anticipate life events, and provide a unique experience.
This e-book explores the many uses of client insights for banking and wealth management. By using sophisticated analytics and cognitive capabilities, your organization can gain deep understanding of what matters most to your clients. Knowing them well helps to provide targeted, personalized service that they value and increases their loyalty. It’s a smart pathway for reducing churn and generating new revenue models through meaningful cross-selling opportunities in today’s customer-centric world.
Cloud-based data presents a wealth of potential information for organizations seeking to build and maintain competitive advantage in their industries.
However, as discussed in “The truth about information governance and the cloud,” most organizations will be challenged to reconcile their legacy on-premises data with new third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.
The growing need for organizations to treat information as an asset is making metadata management strategic, driving significant growth for metadata management solutions. We evaluate nine vendors to help data and analytics leaders find the solution that best suits the needs of their organization.
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?