Analytics and Lean/Agile Practices Are Revolutionizing Application Development.
Meeting the challenges of the digital economy requires organizations of all sizes to increase their responsiveness to business needs. For application development (AD) leaders, this means accelerating the development and maintenance of applications without sacrificing quality. IT departments as a whole must deliver more value, more quickly, with less risk.
“While this is a big challenge, new technologies and techniques offer significant improvements in helping application development leaders to develop and maintain applications,” said Mike West, research director at Gartner.
“In addressing the challenges of a fast-paced digital world, old application development methods have become a constraint. As a result, awareness and adoption of DevOps and enterprise agile frameworks are growing rapidly,” added Mr. West.
Agile is important for overcoming the development challenges posed by digital business
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Applying agile development methods means that applications can be released faster, albeit in a basic form, and then developed further, if they are successful. This faster development life cycle means that fewer resources are put into projects before they begin to yield benefits. This lowers the life cycle risk of an application and ensures that AD resources are focused where they benefit the business most.
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“Although “agile” is important for overcoming the development challenges posed by digital business, without the collaboration of infrastructure and operations teams, getting an application into production quickly is not assured,” Mr. West said. “DevOps practices are crucial to facilitate collaboration between development and operations teams.”
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Advanced analytics is another vital area to address in order to improve AD practices. Two main types of analytics, prescriptive and predictive, are seeing rapid adoption in AD and production processes.
By using advanced analytics in their processes, AD leaders can predict problems the software might cause, and proactively guide testing teams to focus their time in the right areas. Beyond that, predictive analytics enables predictive maintenance of existing applications, meaning that AD teams can receive automated predictions on potential defects that are likely to cause the biggest problems.
”Applying analytics to customer/user feedback and application performance statistics can make quality control far more streamlined and efficient,” Mr. West said. “This helps ensure that AD teams spend their time on the things most likely to deliver business value.”
Gartner predicts that, by 2020, 50% of IT organizations will apply advanced analytics to AD in order to improve application quality and speed of delivery. Therefore, It’s certainly a good time for all AD leaders and CIOs to assess how these new technologies and approaches can benefit their organizations.
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