Five tenets for innovation and sustained competitive advantage through application development

April 15th, 2015 by Sebastian Holst

I’m privileged to spend most of my working days in front of smart people doing interesting work across a wide spectrum of industries - and in the spirit of “ideas don’t have to be original - they just have to be good©” (the copyright is my attempt at humor RE other people’s good ideas versus my silly aphorism) - anyhow, back to my central point - mobile, cloud, the rise of big data, etc. are all contributing to a sense that business (and the business of IT) is entering an entirely new phase fueled by technology, globalization, etc… and with this scale of change comes confusion …but in spite of all this background noise, I’m witnessing many of our smartest customers and partners converge on the following five tenets - tenets that I know are serving some of the smartest people in the coolest organizations extremely well - cheers.

  1. Organizations must innovate or be rendered obsolete.
    • Challenge: Applications now serve as a hub of innovation and a primary means of differentiation – across every industry and facet of our modern economy.
    • Response: Innovative organizations use applications to uniquely engage with their markets and to streamline their operations.
  2. Genuine innovation is a continuous process – to be scaled and sustained.
    • Challenge: Development/IT must internalize evolving business models and emerging technologies while sustaining ongoing IT operations and managing increasingly complex regulatory and compliance obligations.
    • Response: Leading IT organizations imagine and deliver high-value applications through agile feedback-driven development practices and accelerated development cycles that place a premium on superior software quality and exceptional user experiences.
  3. Modern applications bring modern risks.
    • Challenge: In order to sustain competitive advantage through application innovation, organizations must effectively secure and harden their application asset portfolios against the risks of revenue loss, Intellectual Property theft, denial of service attacks, privacy breaches, and regulatory and compliance violations.
    • Response: Successful organizations ensure that security, privacy, and monitoring requirements are captured and managed throughout the application lifecycle from design through deployment and deprecation – as reflected in appropriate investments and upgrades in processes and technologies.

  4. Every organization is a hybrid organization – every IT project starts in the middle.
    • Challenge: Organizations must balance the requirement to innovate with the requirement to operate competitively with existing IT assets.
    • Response: Mature organizations do not hard-wire development, security, analytics, or DevOps practices to one technology generation or another. The result is materially lower levels of technical debt and the capacity to confidently embrace new and innovative technologies and the business opportunities they represent.
  5. Enterprise IT requirements cannot be satisfied with consumer technologies – shared mobile platforms and BYOD policies do not alter this tenet.
    • Challenge: Enterprise security, compliance, and integration requirements cannot (and will not) be satisfied by mobile/web development and analytics platforms designed for consumer-focused, standalone app development (and the business models they support).
    • Response: Mature IT organizations drive mobile app innovation without compromising core enterprise ALM, analytics, or governance standards by extending proven practices and enterprise-focused platforms and technologies.

Darwin and Application Analytics

April 7th, 2015 by Sebastian Holst

Survival of the fittest

Technological 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.

PreEmptive Analytics

Core Analytics functionality

Specialized: 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.

So what are you waiting for? Put down your brontosaurus burger and move your development out of the stone age.

Application Analytics: measure twice, code once

March 23rd, 2015 by Sebastian Holst

Microsoft recently announced the availability of Visual Studio 2015 CTP 6 – included with all of the awesome capabilities and updates was the debut of Dotfuscator Community Edition (CE) 2015. …and, in addition to updates to user functionality (protection and analytics instrumentation capabilities), this is the first version of Dotfuscator CE to include it’s own analytics (we’re using PreEmptive analytics to anonymously measure basic adoption, usage, and user preferences). Here’s some preliminary results… (and these could all be yours too of course using the very same capabilities from PreEmptive Analytics!)

Users by day comparing new and returning users shows extremely low returning users – this indicates that users are validating that the functionality is present, but not actually using the technology as part of a build process – this makes sense given that this is the first month of a preview release – users are validating the IDE – not building real products on that IDE.

Feature usage and user preferences including timing of key activities like what % of users are opting in (of course opt in policy exists and is enforced), what runtimes they care about (including things like Silverlight and ClickOnce and Windows Phone…), the split between those who care about protection and/or analytics, and timing of critical activities that can impact DevOps are all readily available.

Broad geolocation validates international interest and highlights unexpected synergies (or issues) that may be tied to localized issues (language, training, regulation, accessibility, etc.)

This is an example of the most general, aggegrated, and generic usage collection - of course the same analytics plumbing can be used to capture all flavor of exception, user behavior, etc. - but ALWAYS determined by your own design goals and the telemetry is ALWAYS under your control and governance - from “cradle to grave.”

BOTTOM LINE: the faster you can iterate – the better your chances for a successful, agile, application launch – building a feedback driven, continuous ALM/DevOps organization cries out for effective, secure, and ubiquitous application analytics – how is your organization solving for this requirement?

Re-imagined Applications Demand Re-imagined Application Analytics

February 2nd, 2015 by Sebastian Holst

Traditional applications are being replaced with the many-to-many pairing of Apps to Services where core functionality is supplied via cloud-based software and delivered via a multitude of apps running across devices and runtimes. Beyond the obvious runtime combinatory complexity, the apps and services are typically developed by different organizations with independent release cycles under disparate business models. As a consequence, an application’s scope – the sum total of its software and content – has shifted from concrete to ethereal where ingredients can change or evolve from session to session.

Outdated Analytics Patterns Can Only Offer Limited Insight

Analytics solutions built to focus on a single stack (or analyze stacks side-by-side), e.g. mobile apps or web sites or internal servers - or focus on a single stakeholder or persona, e.g. IT ops or web commerce – are poorly positioned to capture the dynamic, interoperable nature of modern application deployments or the increasingly diverse community of application stakeholders.

Shared Runtime Data is the Tie That Binds Components into an Organic Application

Apps track the services they call and services track the apps they serve through tokens and other shared parameters. Not every argument exchanged plays this role – consequently, a working knowledge of the components and their interfaces is required to effectively piece together individual sessions, users, and activities.

PreEmptive Analytics: Built for Modern Deployments and Diverse Stakeholder Requirements

PreEmptive Analytics has been built from the ground up to offer an instrument-once and distribute-many approach supporting a portfolio of analytics endpoints as dynamic as the application components it monitors.

The following working sample illustrates how PreEmptive Analytics can instrument client and cloud components to provide unprecedented insight into app design, user behavior, and IT operations. The latest version of this app, the instrumentation, and the extensions to the PreEmptive Analytics Workbench can be found at GitHub PreEmptive Analytics Use Case Example.

The Sample App and The Sample Service

The sample app lets users submit anticipated expenses for pre-approval. The user identifies the expense category and estimated expense and submits the record to a managed service for centralized approval or rejection. The approval policies reside in the hosted service as do the historical records.

Every user session has an associated organization and unique user ID associated with it – this drives both policy and provides the “hook” to connect the client’s activity with the supporting software services.

The sample app is written in C#, instrumented with the PreEmptive Analytics API and then Xamarin is used to generate both Android and iOS instances.

The sample service is written in C#, also instrumented with PreEmptive Analytics and runs in an Azure Windows VM.

PLEASE NOTE – the analytics functionality demonstrated here is in no way dependent upon or specific to C#, .NET, Xamarin, Azure, Android, or iOS – this is one specific example to illustrate the general principles and capabilities of PreEmptive Analytics that can just as readily be applied to any WPF, Java, or C++ component – running on-premises and/or distributed across cloud services and devices.

Sample App Functionality

The app allows a user to time arbitrary workflows, throw exceptions, express preferences, and – last but not least – submit an anticipated expense for pre-approval.

The sample managed service

A user selects the expense category and estimated expense and submits the information for approval.

Based on the amount and other factors, the remote software service either approves or rejects the request. The client app informs the user in one of two ways.

As mentioned above, users can track arbitrary work flows that span (or work within) page and/or method boundaries by starting and stopping the following timer.


Also, as mentioned above – each user can observe the department and ID they are working under in each session.

PreEmptive Analytics Results

The following dashboards illustrate the cross-section of analytics supporting the full spectrum of application stakeholders from dev to DevOps to business owners.


The overview page requires no special configuration and is immediately populated (a latency measured in seconds) as runtime telemetry comes in from production. All versions of all components are available for inspection across client devices and cloud-services.

Even the vanilla overview page offers insights across component and stakeholder domains as illustrated by these four “feature” stats - all relate to the “expense approval” activity - but each represents a different perspective - providing insights into all of the moving pieces that come together to create the integrated user experience.

Timing May be Everything but all Time is Relative

User behavior, user experience, application service level, managed service service levels

Even without any special configuration, PreEmptive Analytics automatically breaks out usage and timing of:

  1. The Azure-based approval service (item 1 above) – IT operations cares about this perspective,
  2. The client-side call up to the Azure-based approval service (item 2 above) – dev and devops cares about this perspective,
  3. The time spent on the “mobile page” for expense approval (item 3 above) – UX design/dev care about this perspective, and
  4. The time inside the larger workflow that leads a user to the mobile page (item 4 above) – app owner cares about this perspective.

The close up of the feature tracking panel shows that 688ms of the client request is outside of the time actually consumed by the Azure service itself (690ms – 2ms). It also shows that once a user lands on the expense page, they spend almost 40 seconds filling it out and lastly, that the true workflow that takes the user into and out of this page is just over 50 seconds on average.

Application Service Levels

Deeper analysis is readily available as well – here the max, min, and average times that clients need to fulfill a client request are shown over time – alongside a “threshold” indicating a service level goal for the client-side service.

Business Activity

PreEmptive Analytics combines the multi-tiered instrumentation outlined above with application-specific data capture and analysis – enabling powerful business activity insights. The following chart shows the volume, ratios, and trending of expense request approvals versus rejections over time. This particular instrumentation is generated from the cloud-based service – ensuring an enterprise-wide view across applications, platforms, and users.

Server record of expense requests


PreEmptive Analytics goes far beyond counting occurrences of Application-specific data – any data point can be used to segment runtime telemetry as well – providing powerful, contextual insights as illustrated below.

Usage and Experience

Recall that each client session is assigned a department (or role) and a user ID. The following panel breaks out usage, users and exceptions by organization (a server-side lookup of the user ID) AND by the role.

NOTE that these dimensions can also be used as filters allowing stakeholders to focus on the most important organizations and the most importance roles inside those organizations. Below is a view into approvals and rejections by organization and role.

Business Activity

Selecting any combination of organizations and roles sets the focus to the most important constituents to my operation – for the first time, I can segment, monitor, and optimize for the organizations and people that matter most.

Bias by Organization

A user can simply select an organization (which is indexed through a CRM look-up of the license key as the data streams in from production at runtime) and usage, stability, and quality of only users from that organization (one or more can be selected).

After selecting “Up And Away Inc.” you can view both system activity and a business activity summary.

Bias by Role

Similarly, selecting just the “VIP” role shows VIP activity across organizations.

Keep in mind that the data in these tables is a “joined” view combining client-side information (role and activity) and cloud-based computing (request approval statistics).

The same business optimization can be applied to production incidents to support DevOps and support. The following panel shows the activity by user ID drilling down into specific exceptions.

Bias for DevOps

What’s next?

If your business is (or will soon be) dependent on applications whose logic is distributed across devices and runtimes and you believe that application development should be AT LEAST as customer-centric and attuned to your business’ priorities as any other part of your organization – then upgrading application analytics needs to be a priority (not much different than building application security into the dev process and not after).

Contact us to see how we’re helping organizations develop their application analytics practice to improve quality, satisfaction, and development ROI.

Welcome Xamarin Insights (seeing the forest through the trees)

October 8th, 2014 by Sebastian Holst

First, let me state for the record that I am a huge fan of Xamarin - when I say this, I mean to include both their great technology and their people (I’ve only met a few, but they’ve never disappointed). So with that out of the way, I listened with great interest as they announced Xamarin Insights at their user group this morning. As someone with a personal stake in the broad category of application analytics, you can imagine that when a company like Xamarin enters my space, they’re going to get my undivided attention.

My first reaction was that the name “Xamarin Insights” sounded a lot like Microsoft’s “Application Insights” and as I watched the presentation and then reviewed the web content, the similarities grew even stronger.

Of course, if you’re a developer on either of the (*) Insights teams you’re going to be mildly offended by this last statement as you no doubt see STARK differences - and, at some important level, you’re probably right - but I’m not on either dev team, I’m part of the PreEmptive Analytics team and so this is the area where I see the “STARK differences.” …and so that has prompted me to populate the following table comparing all three, Xamarin Insights, Application Insights, and PreEmptive Analytics.

I’ve tried to focus on material differences that are most likely to make one approach more effective than the other two - and to make this crystal clear - there are scenarios where each option is better suited than the other two - so understanding YOUR requirements is the first and MOST IMPORTANT step in selecting your optimal analytics solution.

Xamarin Insights
Application Insights
PreEmptive Analytics
Targeted appeal
Enterprises and ISVs targeting modern platforms
Enterprises and ISVs with established app portfolios
driving large, regulated, and secure operations extending into modern/mobile
Release status
Free with pricing TBD
Free with pricing TBD
Licensed by product component
Applications supported
API for C#/F# supporting native Xamarin targets (end-user
apps only)
API for C/C#/F#, JavaScript supporting Microsoft targets
(MODERN client-side AND server-side apps/components)
All apps supported by (*)Insights PLUS C, C++,
Java, traditional .NET, middle-tier, on-premises, etc.
Endpoint/analytics engines and portal
Multi-tenant hosted by Xamarin
Multi-tenant hosted by Microsoft
On-premises or hosted – hosting can be by 3rd
party or PreEmptive.
Events: Atomic mobile & page
Exceptions: Unhandled and caught
Custom: Strings
System and performance: mobile only
Events: Atomic mobile & page
Exceptions: Unhandled
Custom: Strings
System and performance: Modern only
Events: All  (*)Insights PLUS arbitrary workflow and
in-code spans
Exceptions: Unhandled, caught, thrown
Custom: Strings, serialized data structures from multiple
System and performance: all runtimes and surfaces
Supported organizations
Xamarin devs ONLY
Microsoft-based devs ONLY
All devs supported by (*)Insights PLUS all other
enterprise, ISV, and embedded app devs.
Data dimensions
Only data originated inside an app can be analyzed
Data inside an app AND data accessible from within Azure
account can be analyzed
Any data source available within an enterprise or via
external services can be mashed up to enrich telemetry
Opt-in/out policy enforcement
Offline caching
Extensible indexing and UI on a role-by-role basis (app
owner, dev mgr, etc.)
Injection of instrumentation for managed code
User and organization metrics
Yes including integration with Enterprise credentials
Automatic creation of TFS work items based upon business
rules and patterns
Embedded inside Visual Studio
Starting with VS2013/14
Since 2010

One thing i know for sure - no one will be building applications without analytics in the next few years - figuring this out for YOUR dev requirements will be a critical requirement soon enough - it’s not a question of IF - only WHEN - so, if applications are an important part of your life - this is something that you cannot postpone for much longer (it may already be too late!) Enjoy!