My Five Favorite Data Points

  • Posted by: Will Degener

Anyone who has witnessed an Asset Vision product demonstration delivered by me will have heard the phrase “My Five Favorite Data Points.” I can’t recall exactly when I first fostered this phrase, but the sentiment has been around from the moment I engaged with my first customer on behalf of Scalable Software. These preferred pieces of information (with examples) are:

  • Who Used (“Will Degener”)
  • What Software (“MS Word”)
  • From Where (“His Laptop”)
  • For How Long (“32 Minutes”)
  • How Intensely (“557 Keystrokes”)

That’s them, the favored few. These are the tiny bits of information that I have leveraged to help many customers save money, mitigate risk, understand hardware and software consumption, monitor real estate attendance, measure web application usage and assess adoption trends to name but a few. The use cases for this simple information are obviously not infinite but I have to admit that they keep coming, just as I think I’ve been asked them all or reached the last one, a new requirement arrives that teases more value out of this core foundational information.

“Give some examples” I hear you cry, of course but first, let’s mention the elephant in the room. On the face of it, and looking at my example, you could be forgiven for thinking “Big Brother” is watching. Well, it certainly is true that using this data, my manager could check to see what I was doing between meetings on a sunny afternoon (in this case writing a blog post from the train), but it’s pretty unlikely he would, and actually my management (and more importantly my customers) is significantly more interested in determining:

  • “How much expensive software is lying around unused?” or
  • “Is our hot desk office floor over-subscribed?” or even
  • “How can I prove we need budget for more computers in our Computer Science College Lab?” or more generically,
  • “How can I save money, mitigate risk, understand how my application portfolio is used?” than
  • “What was Will Doing?”

To be honest, if it’s really a problem we can always hide the “User” in this data and the overall outcome would be the same, but then they’d only be my “Four” favorite data points. Check out the video for discussion of this problem and continue reading below to learn more about these data points.

Now, that’s out of the way. Let me now try to describe how these in the real world, taking some assumptions into account:

  • We have a full hardware and software inventory of each machine (Also supplied by Asset Vision).
  • We have this data for all users of our estate for an appropriate period (Let’s assume 90 days).
  • We have a Configurable Software Recognition Catalogue that identifies costed software and allows customers to add costs as required (Also supplied by Asset Vision).

We could then confidently ask the questions:

  • How many machines have “MS Project” (or other costly software title) installed and unused in the last 90 days?
  • How many costly software titles are lying around unused?”
  • How much is the cost of unused software in my estate?

Let’s add another assumption and see what that does: My hardware location / department / business unit can be identified by naming convention, IP subnet, data feed and I know how much the replacement costs are.

Then ask the questions:

  • How many machines are lying around with no software use (i.e. dormant) by location (building / floor / classroom / training room), business unit / department?
  • What is the cost of my dormant hardware?

Yes! I could go on (and on) and, as you can tell, I am seriously passionate about this information and have plenty of other use cases to share and hopefully plenty more to discover, feel free to contact us and perhaps I’ll share.

Author: Will Degener

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