Survival of the fittestTechnological evolution is more than a figure of speech.
Survival, e.g. adoption (technology proliferation and usage) favors the species (technology) that adapts most effectively to environmental changes and most successfully competes for limited resources required for day-to-day sustenance. In other words, the technology that is most agile wins in this winner take all Darwinian world.
You might think you know where I’m headed – that I’m going to position application analytics and PreEmptive Analytics in particular as being best able to ensure the agility and resilience applications need to survive – and while that’s true – that’s not the theme of today’s post.
A rose by any other name… and applications are (like) people too!Today’s theme is on properly classifying application analytics (and PreEmptive Analytics in particular) among all of the other related (and in some cases, competing) technologies – are they fish or fowl? Animal, vegetable, or mineral? Before you can decide if application analytics is valuable – you have to first identify what it is and how it fits into your existing ecosystem – food chain - biosphere.
In biology, all life forms are organized into a hierarchy (taxonomy) of seven levels (ranks) where each level is a super set of the levels below. Here, alongside people and roses, is a proposed “taxonomic hierarchy” for application analytics.
What’s the point here?
What does this tell us about the species “PreEmptive Analyticus”? The hierarchy (precedence of the levels) and their respective traits are what ultimately gives each species their identity. ...and this holds true for application analytics (and PreEmptive Analytics in particular) too.
Commercial Class software is supported by a viable vendor (PreEmptive Solutions in this case) committed to ensuring the technology’s lasting Survival (with resources and a roadmap to address evolving requirements).
Homegrown solutions are like mules – great for short term workloads, but they’re infertile with no new generations to come or capacity to evolve.
Analytics is the next most significant rank (Order) – PreEmptive Analytics shares a common core of functionality (behavior) with every other commercial analytics solution out there today (and into the future)
HOWEVER, while common functionality may be shared, it is not interchangeable.
Hominids are characterized as Primates with “relatively flat faces” and “three dimensional vision” – both humans and chimpanzees obviously qualify, but no one would confuse the face of a human for that of a chimpanzee. Each species uniquely adapts these common traits to compete and to thrive in its own way.
The Family (analytics focused more specifically on software data) and the Genus (specifically software data emitted from/by applications) each translate into increasingly unique and distinct capabilities – each of which, in turn, drive adoption.
In other words, in order to qualify as a Species in its own right, PreEmptive Analytics must have functionality driving its own proliferation and usage (adoption) distinct from other species e.g. profilers, performance monitors, website monitoring solutions, etc. while also establishing market share (successfully competing).
How do you know if you've found a genuine new species?
According to biologists and zoologists alike, the basic guidelines are pretty simple, you need a description of the species, a name, and some specimens.
In this spirit, I offer the following description of PreEmptive Analytics – for a sampling of “specimens” (case studies and references) - contact me and I’m more than happy to oblige…
The definition enumerates distinguishing traits and the "taxonomic ranking" that each occupies - so this is not your typical functional outline or marketecture diagram.
CAUTION – keep in mind that common capabilities can be shared across species, but they are not interchangeable - each trait is described in terms of its general function, how it's been specialized for PreEmptive Analytics and how/why its adaptable to our changing world (and therefore more likely to succeed!) - I’m not going to say who’s the monkey in my analytics analogy here, but I do want to caution against bringing a chimp to a do a (wo)man’s job.
Core Analytics functionalitySpecialized: The ingestion, data management, analytics computations, and the visualization capabilities include “out of the box” support for application analytics specific scenarios including information on usage, users, feature usage patterns, exceptions, and runtime environment demographics.
Adaptable: In addition to these canned analytics features, extensibility points (adaptability) ensure that whatever unique analytics metrics are most relevant to each application stakeholder (product owner, architect, development manager, etc.) can also be supported.
Software Data (Family traits)
Incident Detection: PreEmptive Analytics (for TFS) analyzes patterns of application exceptions to identify production incidents and to automatically schedule work items (tasks).
Data transport: The PreEmptive Analytics Data Hub routes and distributed incoming telemetry to one or more analytics endpoints for analysis and publication.
Specialized: “Out of the box” support for common exception patterns, automatic offline-caching and common hybrid network scenarios are all built-in.
Adaptable: User-defined exception patterns and support for on-premises deployments, isolated networks, and high volume deployments are all supported.
Application Data (Genus traits)Application instrumentation (collecting session, feature, exception, and custom data): PreEmptive Analytics APIs plus Dotfuscator and DashO (for injection of instrumentation without coding) support the full spectrum of PC, web, mobile, back-end, and cloud runtimes, languages, and application types.
Application quality (ensuring that data collection and transmission does not compromise application quality, performance, scale…): PreEmptive Analytics runtime libraries (regardless of the form of instrumentation used) are built to “always be on” and to run without impacting the service level of the applications being monitored.
Runtime data emission and governance (opt-in policy enforcement, offline-caching, encryption on the wire…): The combination of the runtime libraries and the development patterns supported with the instrumentation tools ensure that security, privacy and compliance obligations are met.
Specialized: the instrumentation patterns support every scale of organization from the entrepreneurial to the highly regulated and secure.
Adaptable: Application-specific data collection, opt-in policy enforcement, and data emission is efficiently and transparently configurable supporting every class of application deployment from consumer to financial, to manufacturing, and beyond…
PreEmptive Analytics (Species traits)
Every organization must continuously pursue differentiation in order to remain relevant (to Survive). In a time when almost all business that organizations do is digitized and runs on software, custom applications are essential in providing this differentiation.
Specialized: PreEmptive Analytics has integrated and adapted all of these traits (from instrumentation to incident detection) to focus on connecting application usage and adoption to the business imperatives that fund/justify their development. As such, PreEmptive Analytics is built for the non-technical business manager, application owners, and product managers as well as development managers and architects.
Adaptable: Deployment, privacy, performance, and specialized data requirements are supported across industries, geographies, and architectures providing a unified analytics view on every application for the complete spectrum of application stakeholder.
So what are you waiting for? Put down your brontosaurus burger and move your development out of the stone age.