Could I have chosen a title with less meaning and greater hype? I seriously doubt it.
We have all heard that you can gauge how important a thing or concept is to a community by the number of names and terms used to describe that thing (the cliche is Eskimos and ice) - and I proposed a corollary; you can gauge how poorly a community understands a thing or concept by how heavily it overloads multiple meanings onto a single name or term. ...and "analytics," "platform," and even "application" all fall into this latter category.
In this post, I'm going to take a stab at narrowing the meaning behind these terms just long enough to share some "tribal knowledge" on what effectively monitoring and measuring applications can mean - especially as the very notion of what an application can and should be is evolving even as we deploy the ones we've just built.
Application Analytics: If you care about application design and the development, test, and deployment practices that drive adoption – and if you have a stake in both the health of your applications in production and their resulting impact – then you’ll also care about the brand of application analytics that we’ll be focusing on here.
Cross Platform: If your idea of “an application” is holistic and encompasses every executable your users touch (across devices and over time) AND includes the distributed services that process transactions, publish content, and connect users to one another (as opposed to the myopic perspective of treating each of these components as standalone) – then you already understand what “a platform” really means and why, to be effective, application analytics must provide a single view across (and throughout) your application platform.
PreEmptive AnalyticsAt PreEmptive, we’d like to think that we've fully internalized this worldview where applications are defined less by any one instance of an executable or script and more meaningfully treated as a collection of components that, when taken together, address one or more business or organizational needs. …and this perspective has translated directly into PreEmptive Analytics’ feature set.
Because PreEmptive Analytics instrumentation runs inside a production application (as any application analytics instrumentation must), we find it helpful to divide our feature set into two buckets;
- Desired, e.g. those that bring value to our users like feature tracking and
- Required, e.g. those features that, if they do not behave, damage the very applications they are designed to measure.
How do you decide for yourself what’s desired versus required for your organization?
The list of “desired features” can literally be endless – and a missing “desired feature” can often be overlooked and forgiven because the user can be compensated with some other awesome feature that still makes implementing PreEmptive Analytics worthwhile. On the other hand, miss ANY SINGLE “required feature,” and the project is dead in the water – Violate privacy? Negatively impact performance or quality? Complicate application deployment? Generate regulatory, audit, or security risk? Any one of these issues is a deal breaker.
PreEmptive Analytics “required” cross platform feature set
Here’s a sampling of the kinds of features that our users often rely upon to hit their “required” cross platform feature set:
Platform, runtime, and marketplace coverage: will PreEmptive Analytics instrumentation support client, middle-tier, and server-side components?
PreEmptive Analytics instruments:
- Further, our instrumentation passes Apple, Microsoft, Amazon, and Google marketplace acceptance criteria.
Network connectivity and resilience: will PreEmptive Analytics be able to capture, cache, and transport runtime telemetry across and between my users’ and our own networks?
PreEmptive instrumentation provides:
PreEmptive Analytics endpoints can provide:
- Longer-term data management for networks that are completely isolated from outside networks allowing you to arrange for alternative data access or transport while respecting privacy, security, and other network-related constraints.
Privacy and security at runtime and over time: will PreEmptive Analytics provide the flexibility to enforce your current and evolving security and privacy obligations?
PreEmptive Analytics instrumentation
- Only collects and transmits data that has been explicitly requested by development. There is no unintended “over communication” or monitoring.
- When data is transmitted, telemetry is encrypted over the wire.
- Includes an extensible Opt-in switch that can be controlled by end users or through web-service calls allowing your organization to adjust and accommodate shifting opt-in and privacy policies without having to re-instrument and redeploy your applications.
PreEmptive Analytics endpoints can:
- Reside and be managed entirely under your control – either on-premises or inside a virtual machine hosted in a cloud under your direct control.
- They can be reconfigured, relocated, and dynamically targeted by your applications – even after your applications have been deployed.
Performance and bandwidth: will PreEmptive Analytics instrumentation impact my application’s performance from my users’ experience or across the network?
- Runs inside your applications’ process space in a low priority thread – never competing for system resources.
- Utilizes an asynchronous queue to further optimize and minimize the collection and transmission of telemetry once captured inside your application.
- Has “safety valve” logic that will automatically begin throwing away data packets and ultimately shut itself down when system resources are deemed to be too scarce – helping to ensure that your users’ experiences are never impacted.
- Employs OS and device-specific flavors of all of the above ensuring that – even with injection post-compile – every possible step is taken to ensure that PreEmptive Analytics’ system and network footprint remains negligible.
What about the PreEmptive Analytics “desired” cross platform feature set? (The features that make analytics worth doing) As I’ve already said, this list is literally an endless one – If I were to list only the categories (let alone the features in each category), this would make an already long post into very very long post. So, the desired feature discussion will have to come later…
What’s the bottom Line for “Cross Platform Application Analytics?”
Be consistent – make sure your application analytics technology and practice are aligned with your definition of what an application actually is – and this is especially true when evaluating “cross-platform” architectures and semantics. A mismatch here will likely wipe out any chance of a lasting analytics solution, increase the cost of application analytics over time, and add to your technical debt.
Separate “needs” from “wants” – take every action possible to ensure that your application analytics implementation does no harm to the applications being measured and monitored either directly (performance, quality, …) or indirectly (security, reputation, compliance).
Want to put us through our paces? Visit www.preemptive.com/pa and request an eval...