We have written extensively about data-driven optimization scenarios in this blog. Everybody has some kind of methodology or pre-defined workflow when it comes to reacting to sharp peaks or falls in crucial metrics. Making it work is another story.
At the most basic level you will find yourself in a meeting room once the alarms go off.
Purpose of the meeting? Most likely finding someone to blame.
Outcome? Most likely asking *that* someone to fix it as of yesterday, perhaps accompanied by a basic agreement on what caused the sharp peak or fall, and how the metric was actually obtained.
We could call this a “top-down” optimization process 🙂 Somebody at the top sees the red light and others will try to “optimize” whatever it is that they were measuring.
Things seem much easier when they work the other way around. An analyst should be able to see a pattern coming from afar, as it first becomes evident in metrics that may not just yet make it to the executive scorecard. And he could at that point suggest a very specific action to correct it. This is the very essence of the Digital Insight Management methodology many times discussed here in past months (and built into the Sweetspot Intelligence offering).
The value of this methodology cannot be denied: it encourages you to evaluate the impact of each and every one of the actions stemming from a particular insight.
But it also presents an important limitation: Can an analyst really propose an action of company-wide relevance?
Most likely not. An analyst or, for that matter, anyone in charge of a very specific process (think PPC campaigns or website content) will bring great value through insights and recommendations associated to his fields of expertise. But, unfortunately the buck often stops here – these individuals rarely have the opportunity to make real direct changes based on their conclusions.
Bottom line: Data will have little impact on strategic decisions unless we can find a more efficient top-down approach to data-driven management.
Quite simply, putting strategic goals at the center of a top-down optimization workflow makes the difference.
Doing so ensures that tactical and operational goals “report” to higher purposes. With this system of accountability in place, a KPI on a clear path to missing its target can provoke optimization workflow scenarios in any or all of metrics contributing to its performance. These workflow scenarios can then involve as many departments and specific processes as required.
So, how can we articulate our goal-driven strategy? Three steps for starters:
Would you like to share additional thoughts?
Let’s keep on pushing forward to try and get companies to transform Big Data into Useful Data.
It is high time!
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