Calculating Time To Information

The key metric for any knowledge work – IT deliveries and testing in particular – is more than Mean Time to Repair (MTTR). While fixing fast matters – timing is everything. Timing in getting information to the people who needs it to make decisions. It’s no use if you can turn the ship around on a plate now, if you needed it yesterday. Key elements in calculating time to information is how far away the information is and how evolved the information is.

The concept of Time to Information recently came up in the Scale Test Automation with Your Developers in Mind panel with among others Dr. Nicole Forsgren, author of Accelerate and similar research on high performing delivery teams. In the recording you can hear me talk about how Timing is Everything for working on bids and tenders. And contemplating the lessons from social enterprise networks – where 77% use it to find information faster. But there is more to the topic than my ideas on the matter. Recently I found this quote, that puts it in a broader perspective:

“The only sustainable competitive advantage is an organization’s ability to learn faster than the competition.”

Peter Senge, The Fifth Discipline: The Art & Practice of The Learning Organization

All companies there days are learning organizations – it’s just that the Westrum Generative are more effective. But how to calculate time to information besides the simple stop watch version of the difference between request and response.

The original Wardley Map had costs assigned to each node, enabling the calculation of the cost of the user needs. In the following I will build on that framework to calculate time to information.

Who has the Information?

Come to think of it, Liz Keogh’s awesome primer on Cynefin came to an aid here. We are so used to information at our fingertips that we forget it’s even possible for information not to be around. But working with collecting enterprise knowledge we can clearly see the cost of finding information depends on who has it.

“Who’s done this before?”

5. Nobody’s done this before.

4. Someone’s done this, but not in this context.

3. Someone in our organization has done this (or we have access to expertise some other way).

2. Someone in our team’s done this.

1. We all know how to do this.

Cynefin for Everyone! by Liz Keogh

Based on this I can map the visibility / proximity to the information based on the following steps: Everyone, Team, Organization, Someone else and Nobody. With the last item being the relatively more expensive.

How easy is it to get the information?

Not all information requests are equal. Some are expensive one-offs others free to pick up. As solutions evolve so does the information evolve from being gathered once to being freely available.

A simple example could be from my recent test management project. I can create data for reporting very cumbersome, then repeat the learnings. Then just create the graphs using a test management tool and lastly giving away control and letting the stakeholder generate the graphs on demand.

Contemplating the evolution of knowledge I find inspiration the the Learn Wardley Mapping Landscape description to create this evolution legend. Including my personal Darlings-Pets-Cattle-GUID’s mnemonic:

CostlyFree(ish)
DarlingsPetsCattleGUID’s
One-OffRepeatedRegularCommodity
GenesisCustom builtProductCommodity

Time to Information = Visibility x Evolution

Applying the findings above to a modified* Wardley Map I get the following map, where the distance from Request to information is based on the proximity and evolution of the information. It might be a right-angled triangle (thus following Pythagoras) – but the key is really the relative position of the elements on the map and the discussion around what the map is in this specific context.

Next up: What trends emerges as information evolves?

Information = Visibility x Evolution

*: I could reorganize the map to have the axis pointing differently. The observation would be the same.

4 thoughts on “Calculating Time To Information

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