Effective Competition Depends on Continuous Delivery of Quality Software In today’s application economy every company is a software company, no matter what industry it is in:
• Shipping companies depend on logistics software to efficiently route packages, arrange drivers and automate warehouses.
• Retail companies rely on software to manage inventory, engage with customers online and to give in-store associates the tools they need to answer customer questions on the spot.
• Marketing firms lean on applications to gather consumer data and parse it, automate communication with prospects and effectively manage advertising campaigns. The examples are endless.
The point is that in order to compete today, every business must be able to quickly build and tweak software to adjust to always evolving market demands. Ultimately, business success depends on faster development iterations while still maintaining the high quality of service expected by customers, stakeholders and end users.
This IDC whitepaper (including the Dell PC Deployment Optimization Model) contains research that helps CIOs map out a plan to reduce their organizations' PC deployment costs while deploying more quickly and with less disruption to staff.
As the pace of business continues to accelerate, forward-looking organizations are beginning to
realize that it is not enough to analyze their data; they must also take action on it. To do this, more
businesses are beginning to systematically operationalize their analytics as part of a business process.
Operationalizing and embedding analytics is about integrating actionable insights into systems and
business processes used to make decisions. These systems might be automated or provide manual,
actionable insights. Analytics are currently being embedded into dashboards, applications, devices,
systems, and databases. Examples run from simple to complex and organizations are at different
stages of operational deployment. Newer examples of operational analytics include support for
logistics, customer call centers, fraud detection, and recommendation engines to name just a few.
Embedding analytics is certainly not new but has been gaining more attention recently as data
volumes and the freq