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Forecasting and Data Newsletter by Troy Magennis
Five Ways to Get More Reliable Data

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In this newsletter:

  1. Coming workshops and events you might be interested in.
  2. Article:Five Ways To Get More Reliable Data 
  3. About Focused Objective and Troy Magennis
Got Metric or Forecasting Questions? Contact Me

Coming Workshops and Events

Well, 2020 has been incredible. All of my plans to ramp up training for Metrics and Forecasting took a very different direction. I hope all of you and your loved ones are and remain healthy - this is the main goal this year. I've made some changes to make online learning more accessible.

New policy "Register once, attend many times"
All online training runs once per month, you can attend any session multiple times from the month you register onwards. Scheduling back to back 4 hour days is hard, this means you have flexibility. 

New policy "Attend online, and get a free 1-hour 1:1 with me after the training
It's hard online to get your specific questions answered. So, I don't try. After attending training, you schedule a call with me where we do some 1:1 time.

New policy: All sessions are recorded and you get those recordings
Language sometimes is a barrier. Every session is recorded and you and replay it in your own time to make sure you understand any concept. If you still don't understand what I mean, schedule a call with me.

Upcoming (online) Training

Forecasting Essentials Online - 2 x 4 hour days Zoom + Miro hands-on workshop (next one 22nd September, run monthly)

Power-session - Using the Monte Carlo Forecasting Spreadsheets 1 x 2 hour session using Zoom (next one 10th Sept, run monthly)

Power-session - Using the Team Dashboard Spreadsheet 1 x 2 hour session using Zoom (Next one 1st Oct, run monthly)

Power-session - Prioritization and Cost of Delay 1 x 2 hour session using Zoom + Miro (next one 11th Sept, run monthly)

Article: Five Ways to Get More Reliable Data

There are always gaps in the data needed to make a decision. These gaps shouldn't deter us from striving to get better data where it counts most. Here are five ways I've turned crappy data into jewels within organizations. Warning: Some emotional manipulation required.

Caption: Tip 4 is to use something eye-catching. Here is my take on cycle times data. The height of the loops is cycle time. The numbered node circles different work states. People want to make this data neater and get included.

1. Show data used: Until people see data used, they won’t care

Action speaks louder than words. When people see data used to take action, they won't feel the time spent capturing that data is wasted effort on their part.

Key points

  • Make it visible when data caused a decision that led to action (often it's used but unsaid)

  • Make it visible when the lack of data caused a decision to be made with uncertainty, "I wish we were surer what customer segment we get the most revenue from."

  • Sometimes just showing the data in a public space is enough for people to see it is useful

  • Creating a safe space for people to make observations about data trends is essential; scared people hide data.

2. Show data wrong’ ish: People critique better than create

Showing incomplete or partially incorrect data spurs action to get better data. With a blank slate, it is hard for people to get started as they worry about errors. But presented with errors, they can focus on what is needed to get the data better. Show the data with errors or gaps and let the data loose with clear warnings, “this may not be right.”.

Key points

  • Start to show the data you have or even just the targets you want to spur "that isn't right" emotions

  • Control the "this data is so bad it's useless," message by challenging, "how do you know? do you have access to alternative data we could use?"

  • Discuss decisions made using relative terms, "even if it was double this value, we should still..."

  • Restate the quote, "Perfect is the enemy of the good enough."

Story: Teams were inconsistently connecting their features to organizational strategies in the work tracking tool. A visualization was built that showed features connected to strategies with those features missing a link connected to a grey box labeled "No Strategy Assigned." This visualization was highlighted during an all-hands and displayed in the lunch-room information screens. Within a few days, every feature had a strategy assigned. Everyone saw a purpose and wanted to avoid their work being in the un-assigned bucket. 

3. Show an absence of data: Fear of Missing Out

Jealousy is a complicated emotion. People want their contribution counted as much as other people's. Making data public often spurs others to get their data captured and shown as well. A small variation of this is where people intentionally don't add data, so they can't be called out. It's also important to celebrate those who HAVE made an effort to capture clean data.

Key points

  • Seeing data visualized where their people or teams should be shown (especially in a "cool" way) creates a condition of ownership

  • Observing a decision (especially a wrong one) without complete information causes people to fill those gaps 

  • Show incomplete or yet to be captured data in grey (work on men over 40 particularly well), with captured data in a bright pastel.

  • Highlight non-participation in a non-threatening way, for example, teams where gaps in data exist. 

Story: An organization sent a survey around to teams to rate their capability in certain Agile practices. During a town-hall, a VP mentioned some teams were "below average" on this self-assessment. He didn't mention, "and thank you for being honest and ready to improve." He also didn't call out the teams who avoided filling in the survey at all. He rewarded staying in the shadows and penalized honesty. What do you think happened next survey?

4. Show it vividly: Make it eye-catching; Make it interactive (about me)

You can create engagement by making data visualization eye-catching and immersive. Typical a technique used to draw attention to infographics, but it works equally well with live data inside an organization. 

An eye-catching visual gets attention, trade usefulness for interaction to spark better data

In private, people like to see how they relate to others.  Making data interactive where people can compare or explore data of interest helps the data become correct and complete. 

Story: The work state captured in a tracking tool didn't make sense. Capturing when work moved from development to integration wasn't a priority, making it difficult to see where the real constrains in a system were versus just bad data. The work states were captured in a Jump-plot, showing a specific work item's flow (on hovering over them) through the system and versus the average for those state changes. Over the next few weeks, the state change data (and the averages) stabilized to useful comparisons with outliers being something to investigate rather than bad data.

5. Reduce the effort

People aren't lazy. Although we need data, we need features delivered more. Without seeing the use of data, it is hard for people to justify the time in keeping that data up-to-date and clean. It either has to be automatically captured as part of a "useful" process or simple enough for people to deem worthwhile effort. When people have to drop work due to an overwhelming workload, make sure it isn't essential data.

Key points

  • Do a few important metrics, not the first 100

  • Automate the capture where possible

  • If it is essential data, make sure people know why

  • Sometimes a few samples are enough to know a problem exists. For example, sampling a week or two about sources of unplanned work is enough to know the top 5 disruptive causes. 

Story: There was a suspicion unplanned work was coming from a few sources. It wasn't clear the amount of unplanned work there was, or its source. I gave each team member ten post-it notes and instructed them to capture who and what data about drive-by requests. These post-it notes gave ample data to heatmap the sources and amount of unplanned work disrupting the team without adding significant data entry workload.

About Focused Objective and Troy Magennis
I offer training and consulting on Forecasting and Metrics related to Agile planning. Come along to a training workshop or schedule a call to discuss how a little bit of mathematical and data magic might improve your product delivery flow.
See all of my workshops and free tools on the Focused Objective website.

Got Metric or Forecasting Questions? Contact Me
Copyright © 2020 Focused Objective LLC, All rights reserved.

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