Cloud, mobile and distributed software services have made simulating “true” production impossible while production and release cycles have become more frequent. At the same time, communication and collaboration between development and operations has become a focal point for process improvement, spawning a trend in software development expressed by the term Development Operations (DevOps).
This is especially important as the focus shifts from long QA/user acceptance testing cycles to rapid identification and resolution of issues in production, and deployment of the fixed application back into production. This rapid identify-fix-deploy loop requires adoption of new tools and processes to be successful.
It will be increasingly important to have sharp insight into applications running in production. Without it, you will miss quality goals, have higher maintenance costs, and lower customer satisfaction. With it, you can prioritize work based on actual usage patterns, identify, triage and resolve problems before your customers are seriously impacted. You can also test changes to see how they affect user behavior and intended outcome, and drive both hard and soft costs to a minimum.
Collecting, analyzing and acting on application runtime data poses unique challenges both in terms of the types of data that need to be gathered and the metrics that measure success. Effective application analytics implementations must accommodate the diversity of today’s applications and the emergence of cloud, mobile and distributed computing platforms. Narrower analytics technologies such as standard reports provided in a cloud service will never fully satisfy development and management objectives for corporations.
Existing analytic solutions have almost exclusively resided in the cloud. This makes perfect sense from a technological implementation standpoint for the analytics vendor. However, for companies with sensitive data or that are constrained by government regulation, storing your data “in the cloud” is simply not an option. The only appropriate application analytic solution is one where data can be surfaced on a variety of endpoints (on premise and/or off premise) according to client-specific rules for compliance with relevant industry standards and regulations.
Comprehensive application analytics must support enterprise, B2B and B2C use cases including cloud, servers, web-based, traditional PC and mobile apps – and the data should stream within a private network or across public networks as well.
Our application analytics solutions achieve that objective. Let’s look at the pieces:
- PreEmptive Analytics for TFS is a “Client-premises” or on-premises incident response solution that connects production incidents to development and operations via automated, intelligent, rule-driven creation and management of work items to decrease the mean time to fix an application.
- PreEmptive Analytics Runtime Intelligence Service is a managed, multi-tenant service providing broad analytics and archival services – it’s a hassle-free, always up, analytics platform ideally suited to measure the most common metrics and KPIs.
- PreEmptive Application Analytics Workbench is an on-premises solution that provides critical insight into the adoption, usage, performance, and impact of production applications to facilitate feedback-driven development and enhance software quality, user experience, and decrease the mean time to improve an application.
At this point you might be wondering which of these tools might be most useful to you now. That is where the Data Hub shines brightly.
The PreEmptive Analytics Data Hub is a client-premises endpoint that can be installed internally, on a “DMZ” server, or in the cloud - and it serves as the “one endpoint” for all of your applications, across all of our services – even as you expand and adjust your analytics strategies and implementations. The Data Hub monitors runtime data and routes that data to any/all other PreEmptive Analytics software and services (including other Data Hubs). The Data Hub is an enterprise-scale runtime data management and distribution service providing resilience (caching, retry and commit) and flexibility across architectures and platforms.
So you can instrument your apps, send them to the one endpoint you need, the Data Hub, and then slide in one or more of the available analytics solutions (including 3rd party solutions) that best meet your requirements. If your analytics toolset changes, you can make any necessary adjustment without having to re-instrument or redistribute your applications. Applications that do not have privacy or regulatory concerns could have runtime data forwarded to the “cloud”. And, Analytics for Applications that touch more sensitive data can be kept internally only. Runtime data can be sent to more than one place, providing a set of checks and balances. Flexible, powerful, secure, actionable… You can have your cake and eat it too.