Home > Learning Ecosystems, Performance analytics, Performance Support > Choosing Chain Guns & Performance Analytics

Choosing Chain Guns & Performance Analytics

Whoa…now there’s a title begging for context. You may need to consult a 12-year old gamer to draw the parallels, but there really is one, so hang with me for a few minutes. This post is an attempt to make the point that while performance data can now be exploited beyond our wildest expectations, there are tactical disadvantages that accompany decisions related to pursuing an aggressive analytics implementation without a well-thought out battle plan firmly in hand. And yes, I learned these things at the 32nd level of Destiny during a Crucible mission.

By now you can guess…I must confess to being an avid gamer, and I must also confess I now avoid playing my favorite games on-line. My game play is not too shabby, but there’s something disheartening about watching a 10-year old doing a taunting victory dance over my corpse. I don’t play on-line anymore unless the mission involves a team objective to blow up and defeat a common enemy. Even playing alone I can get destroyed, and for the same tactical reasons a punk kid earns the opportunity to do his victory dance over poor decision-making on my part. Here’s why that happens…

I especially love it when I stumble upon one of those powerful, unlimited ammunition weapons, like a chain gun, that gives you that invincible feeling of power and invincibility as you progress toward beating the level in which you are fighting; however, invincibility can be short-lived if you acquire the weapon under the wrong circumstances and with the wrong tactical intent – kind of like unleashing a new performance analytics tool without a battle plan.

A key problem with those game-based weapons is what you lose when you pick one up; in some games you can no longer run, jump, or crouch. True, you can destroy anything and everything you encounter, but should you? Is the best plan to blow everything up? The loss of agility and focus required to address smaller challenges that take more precision and surgical application can be a huge disadvantage. It is too easy to become distracted by the power of this weapon having unlimited ammunition. Finding a chain gun and choosing to pick it up is a decision point that requires tactical consideration related to the potential application of the power the weapon yields.

What you gain must be balanced with the risk represented by what you lose given the nature of engagement on the battlefield. Similarly, the explosion in performance analytics tools is one of those powerful, unlimited ammunition weapons that can cause more loss than gain if misapplied. The popularity and enthusiasm of capturing performance data is matched only by the dramatically expanded ability to do so. It’s like picking up a chain gun and begin blasting away…like becoming enamored by all the data we can imagine capturing. Nothing can stop us. However, blasting away…err…measuring everything we can measure can lead to a distraction that produces so much data that we can be crushed by the results we gather…and then somebody does a victory dance over our poor decision-making.

The combination of tools like Experience API [xAPI] and a plethora of data analytics platforms enable us to capture way too much data. And the temptation is to capture it…just because we can. But should we? We can easily and efficiently create extra work sorting through irrelevant data-points to figure out what we really need to track. To this gamer, the mission becomes one of developing a tactical plan that identifies both the battlefield conditions under which we wield this powerful weapon and clear goals for measuring performance data in the first place.

• What do we need to track?
• Why are we tracking it?
• What operational evidence do the results support?
• How do we format and present the data?
• What are we going to do with the results?

The best advice when pursuing a measurement strategy suggests we have a tactical battle plan that is lock-step with supporting the business strategy. Going back to the analytics weapons stash, we discover that it offers a robust mix of tools, and we can measure virtually any activity that creates a data record; but should we?

Sorry about video game comparison, but the parallels highlight how easily one can become distracted by acquiring a powerful capability and then lose sight of tactical advantages gained and lost. The introduction of analytics tools makes it essential to have a clearly defined strategy on how metrics and measures contribute tactically in the enablement of informed and actionable decision-making.

For me, the measures that matter most are those that provide tangible evidence of specific business impact. But there’s more. Beyond reaching success evidenced by impact, we should also consider whether or not that impact is sustainable.

I wrote a post previously about zeroing in on those metrics and measures that demonstrate evidence of sustained capability. There is a running battle over the necessity to search for return on investment [ROI], and the diverse menu of new analytics tools to choose from appears to offer a shorter path to acquire ROI. But should we?

I spent a few years of my career in the healthcare industry where a voracious appetite for proving every initiative with evidence-based outcomes prevailed. For obvious regulatory and patient safety reasons, evidence-based outcomes were minimum measurement criteria and served as standards for best practice. I get that and agree with it…but…maybe it’s just me, but the standard should expand to not only confirm attainment of performance targets, but determine if they are being sustained.

This means that we cannot treat analytics too narrowly. Sustainability is manifested over time, and proof of sustainability is found in patterns and trends of relevant data-points that must be identified and extracted. To track those patterns, we need visibility to those trend data, and we do not need the clutter of performance metrics that do not matter. We do not need to collect data points on everything the workforce does just because we can. We need only the data that matters in the context of sustainability of whatever performance targets contribute in a tangible way to meeting/exceeding business goals.

Given sustainability matures over time…or not…can be discovered in those pattern and trend data I mentioned. To have the accessibility and visibility we need to view those performance data, they should be formatted specifically for the performance evidence we seek, and those data should be accessible in a dashboard type venue from which informed, actionable business decisions can be made.

So why am I writing about chain guns and performance analytics? Frankly, it is because we have opened Pandora ’s Box o’ data, and there is no going back. The lid cannot be replaced, and in all honesty, we should not even try. The mission now is how to deal with all the new performance data that has been unleashed upon us. Don’t knee-jerk react and pick that chain gun up and start blasting away and lose your tactical advantage in the glee of shooting any data point that moves.

As an example…and you knew I had to go here eventually…as the discipline of Embedded Performance Support [EPS] and the associated EPSS technology choices we can make become more prevalent, the growth of new performance measures will expand rapidly. For argument’s sake, they already have and many are scrambling to capture and leverage the intelligence they represent. This exploding growth of performance-specific data alone should serve as a call to action for us to consider the scope of a tactical  metrics and measures plan to pin-point the performance data to be measured…and why.

This is critical because we now have a clear shot at capturing level 3 metrics…BUT…if we have not identified which metrics contribute to capturing the relevant evidence we’re after, we may be creating a lot of extra work to shift through non-relevant data points.

Adopting a Performance analytics strategy is as much of a discipline as EPS. I say this because much of the discovery accomplished in the EPS discipline serves as source material used to build the plan for capturing/tracking performance data. For all intents and purposes, your analytics discipline is contained within the EPS discipline. The parallels and the opportunities cannot be overlooked.

Targets for relevant performance data are embedded within the same Key Performance Indicators [KPIs] we are after to prioritize what our EPS solutions should address. Those same data are relevant sources for evidence of impact. The advent of EPSS software opens the door to a wealth of performance data that can populate a performance dashboard using xAPI with real-time performance indicators. Are we ready to leverage that opportunity?

So…what’s your plan? Do you yield to temptation and pick up that powerful chain gun and start blasting away at every data point that moves, or do you leverage the agility gained from a well-structured performance analytics strategy?

An EPS adoption initiative starts small and then scales from a small, validated proof of concept to a sustainable enterprise-wide integration. So too should your performance analytics initiative. Seriously, avoid the temptation to measure too much too quickly lest you are willing to endure the shame of that demeaning victory dance of a failed initiative.

Gary Wise
Workforce Performance Strategist
@gdogwise
(317) 437-2555

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