Self-service analytics has caught on in the enterprise like wildfire, with more organizations deploying end-user reporting tools designed to maximize the number of company data crunchers. The myriad potential benefits of this approach include a more productive workforce, enhanced customer engagement strategies and much-improved internal management capacity. However, success all hinges on the application portfolio and its power to enable end users to distill data.
An increasing number of organizations are adopting enterprise applications - 79 percent have implemented or are considering deploying business software, according to Tech Pro Research. Enterprise end-user utilization of apps and analytics software will have a direct impact on productivity and the bottom line. This obviously puts pressure on programmers to change up their approach to application development. The self-service end user is a far cry from the trained data statistician, not only in terms of skill set but in light of the ways they develop and apply insights.
A changing paradigm
When they arrived, technologies such as big data, agile programming and even mobile were often viewed with skepticism and caution. Would the interruption to the status quo be too pronounced? Were enterprise environments equipped to handle such fundamental shifts in ways of doing things? Could these technologies be effectively applied for an increase in business profit potential? As The Washington Post editor Dan Beyers recently observed, companies have started to transition from the theoretical realm to the actionable one.
"It used to be much of the discussion centered on just understanding the concepts and figuring out how they might apply to a business," he wrote. "Now, it seems, industries big and small are taking that knowledge and weaving the technologies into their business practices. As a result, we are beginning to see glimmers of where all this might lead."
How to make the transition
Enterprise programmers will need to focus on technologies that speed up the software development life cycle. Accomplishing this requires investment in report designer tools that prioritize interoperability and cross-platform, self-service use, such as ActiveReports and Active Analysis. It also demands adoption of next-gen programming strategies such as agile and DevOps, which decrease risks in complexity in testing, deployment and management.
Techworld contributor Sumit Mehrotra stated that a productive roll out of DevOps or agile development requires attention to every step of the app development process. If successful, however, it can increase visibility, ease of intervention and faster testing.
"Implementing agile development requires a disciplined approach to managing the high amounts of automation involved," Mehrotra wrote. "Along with agile processes, agile infrastructure is also needed to provide the elasticity and dynamism needed to scale resources for various stages of the continuous delivery pipeline, namely continuous build, continuous integration, automated functional testing, stress, and performance testing."
Programming and testing is only half of the battle. Rolling out an application - and getting end users on board - requires attention to the whole programming process as a holistic ecosystem. As enterprise software environments expand and investment in data analytics rises, such a comprehensive view can help developers design, deploy and manage applications in the most effective way possible.