From Red Hat Agile Day Oct 20, 2015 Session 4 Advanced Track:
You know “collaboration over contract negotiation’, right? However, metrics often drive a wedge between management and the team, none more so than forecasting metrics. However, when you give a probability distribution as the answer to the question, “When will we get it?” instead of a single date, an amazing transformation happens. Suddenly, the team and management start working together to manage tradeoffs and risk.
You need two things to take advantage of this paradigm shift: 1) How do you start to think probabilistically?; and 2) How do you generate a probabilistic forecast or analysis? This talk provides mindset shifts necessary for #1 and lots of worked out practical examples for #2.
"What?", "So what?", "NOW WHAT?" How to influence people and accomplish changeLarry Maccherone
The evening before the space shuttle Challenger explosion, scientists at NASA caught what they thought was a potentially catastrophic risk with the o-rings considering the unusually cold temperature expected for the morning’s launch. They brought the issue to management attention but failed to influence the final decision enough to stop the launch. Your failure to influence may not cost lives but it could be “catastrophic” for your business. This talk presents my top tips for using data to influence others toward better decisions.
Learning outcomes:
- The do's and don'ts of visualization
- How others lie with data
- What makes an effective dashboard
- How to communicate uncertainty
Using metrics to influence developers, executives, and stakeholdersLarry Maccherone
The evening before the space shuttle Challenger explosion, engineers at NASA caught what they thought was a potentially catastrophic risk with the o-rings considering the unusually cold temperature expected for the morning’s launch. They brought the issue to management attention but failed to influence the final decision enough to stop the launch. As a leader in your organization, your failure to influence may not cost lives but it could be “catastrophic” for your business.
You want it when? Probabilistic forecasting and decision makingLarry Maccherone
Before the space shuttle Challenger explosion, a group of engineers identified a potentially catastrophic risk. They brought the issue to NASA management attention but failed to influence the final decision enough to stop the launch. As a leader in your organization, your failure to influence may not cost lives but it could be “catastrophic” for your business.
Learn how to get action and behavior change from your analysis. Steer the emotional elephant of your organization and appeal to the risk tolerance level of your stakeholders. Avoid your own cognitive biases and those of your executives.
We often hear about gender inequities in the workplace. There are many contributing factors, but could our technology be partly to blame? A recent study reveals how undetected bias in training data can reinforce gender stereotypes to, “a disturbing degree.” In this talk, we’ll discuss the pitfalls of assuming AI is inherently unbiased and the steps being taken to address such biases.
Watch an extended recording of this talk, which was also given at Scenic City Summit here: https://www.verypossible.com/resources/video/is-ai-sexist
Jason Yee - Chaos! - Codemotion Rome 2019Codemotion
As applications become more distributed and complex, so do our failure modes. In this presentation, I’ll share why you shouldn’t just embrace failure, but why you should induce it to intentionally cause and learn from failure. Together with the audience, I'll run a Chaos experiment to show how they can start their own Chaos engineering and make their systems more resilient.
Some consider measurement in agile development destructive—or at the very least useless. Larry Maccherone disagrees and offers eight tools to slay the dragons of agile measurement. The #1 Dragon slayer—Use measurement for feedback rather than as a lever. What's the difference? Feedback is used to improve your own behavior; a lever is employed to change someone else's behavior. The distinction is subtle but critical. If you think what gets measured gets done, you are already venturing into “thar be dragons” territory. But it's not too late. Larry shows how to create a culture where measurement is an insight amplification and feedback mechanism rather than a club to beat people up; where your teams seek out—rather than dread—the use of quantitative insight; and where metrics bring stakeholders and teams closer together, not drive them apart. Leave with a list of good practices to follow and examples from companies whose metrics regimens have already slain the dragons.
"What?", "So what?", "NOW WHAT?" How to influence people and accomplish changeLarry Maccherone
The evening before the space shuttle Challenger explosion, scientists at NASA caught what they thought was a potentially catastrophic risk with the o-rings considering the unusually cold temperature expected for the morning’s launch. They brought the issue to management attention but failed to influence the final decision enough to stop the launch. Your failure to influence may not cost lives but it could be “catastrophic” for your business. This talk presents my top tips for using data to influence others toward better decisions.
Learning outcomes:
- The do's and don'ts of visualization
- How others lie with data
- What makes an effective dashboard
- How to communicate uncertainty
Using metrics to influence developers, executives, and stakeholdersLarry Maccherone
The evening before the space shuttle Challenger explosion, engineers at NASA caught what they thought was a potentially catastrophic risk with the o-rings considering the unusually cold temperature expected for the morning’s launch. They brought the issue to management attention but failed to influence the final decision enough to stop the launch. As a leader in your organization, your failure to influence may not cost lives but it could be “catastrophic” for your business.
You want it when? Probabilistic forecasting and decision makingLarry Maccherone
Before the space shuttle Challenger explosion, a group of engineers identified a potentially catastrophic risk. They brought the issue to NASA management attention but failed to influence the final decision enough to stop the launch. As a leader in your organization, your failure to influence may not cost lives but it could be “catastrophic” for your business.
Learn how to get action and behavior change from your analysis. Steer the emotional elephant of your organization and appeal to the risk tolerance level of your stakeholders. Avoid your own cognitive biases and those of your executives.
We often hear about gender inequities in the workplace. There are many contributing factors, but could our technology be partly to blame? A recent study reveals how undetected bias in training data can reinforce gender stereotypes to, “a disturbing degree.” In this talk, we’ll discuss the pitfalls of assuming AI is inherently unbiased and the steps being taken to address such biases.
Watch an extended recording of this talk, which was also given at Scenic City Summit here: https://www.verypossible.com/resources/video/is-ai-sexist
Jason Yee - Chaos! - Codemotion Rome 2019Codemotion
As applications become more distributed and complex, so do our failure modes. In this presentation, I’ll share why you shouldn’t just embrace failure, but why you should induce it to intentionally cause and learn from failure. Together with the audience, I'll run a Chaos experiment to show how they can start their own Chaos engineering and make their systems more resilient.
Some consider measurement in agile development destructive—or at the very least useless. Larry Maccherone disagrees and offers eight tools to slay the dragons of agile measurement. The #1 Dragon slayer—Use measurement for feedback rather than as a lever. What's the difference? Feedback is used to improve your own behavior; a lever is employed to change someone else's behavior. The distinction is subtle but critical. If you think what gets measured gets done, you are already venturing into “thar be dragons” territory. But it's not too late. Larry shows how to create a culture where measurement is an insight amplification and feedback mechanism rather than a club to beat people up; where your teams seek out—rather than dread—the use of quantitative insight; and where metrics bring stakeholders and teams closer together, not drive them apart. Leave with a list of good practices to follow and examples from companies whose metrics regimens have already slain the dragons.
Did you know that dedicating team members to a single team doubles productivity? Do you know what levers will cut your time to market in half? Did you know that teams that most aggressively control their simultaneous work in process (WiP) have as much as a 75% reduction in released defects? How much of an impact do the simple things like holding regular retrospectives have on performance?
Groundbreaking research on the performance impact of software engineering practices derived from tens of thousands of teams across a wide variety of industries and contexts!
This session answers the questions your organization is asking.
Influx/Days 2017 San Francisco | Baron SchwartzInfluxData
WHAT GOOD IS ANOMALY DETECTION?
Static thresholds on metrics have been falling out of fashion for a while, and for good reason. Modern tooling lets you analyze and monitor a lot more data points than you used to be able to, resulting in lots more noise. The hope is that anomaly detection answers some of this, by replacing static thresholds (anomalies) with dynamic ones. But it doesn’t work as well as most people think it will. In this talk I’ll explain how anomaly detection works, so you can understand why it isn’t a good general-purpose solution, and which specific cases it’s good at.
Your Agile Leadership Journey: Leading People, Managing ParadoxesPaul Boos
This is the session given at March's AgileNoVA meet-up and is intended for Agile & Beyond 2019. It was also submitted for Agile2019 in the Leadership track.
Agile Metrics: Make Better Decisions with DataTechWell
Some consider measurement in agile development destructive—or at the very least useless. Larry Maccherone disagrees and offers insight into how you can use metrics in an agile environment to make life better. How do you know when you are ready to introduce metrics into the environment? What are the sources for these metrics? What tools and techniques are necessary to make decisions probabilistically? What are the mindset shifts necessary for metrics to help you making better decisions? How do teams and organizations avoid the anti-patterns that so often derail a metrics program? Larry answers these questions and shows how to create a culture where measurement is an insight amplification and feedback mechanism—not a club to beat people up; where your teams seek out—rather than dread—the use of quantitative insight; and where metrics bring stakeholders and teams closer together—not drive them apart. Leave with the vision and understanding necessary to implement your own metrics regimen and make better decisions with data.
Your Agile Leadership Journey: Leading People-Managing Paradoxes - Agile Char...Paul Boos
This is the workshop Nicole and I gave at Agile Charm 2020 on Leading people through paradoxes, some of which are described directly in the Manifesto for Agile Software Development. It helps you understand how to use Polarity Maps as leaders for a thinking tool to understand your system.
Applied Data Science for monetization: pitfalls, common misconceptions, and n...DevGAMM Conference
This talk guides us through modern twists on classic user-oriented data science tasks, such as churn prediction, clusterization, calculating user metrics, and others. We will discuss unusual angles for solving these tasks; how and why they can be used to improve player experience and monetization; the intuition behind these methods, and insights into inner machinery; and why conventional methods work poorly. Finally, I'll show you how you can apply this knowledge to improve your users' playing experience, and streamline analytics; and we'll talk general situation of applied data science and analytics in the industry.
Beyond A/B: Tips and Tech for Optimizing to the OpenLiveIntent
The subject line is the most important piece of copy in every email you send. It has a direct impact on opens and engagement and presents a potent opportunity for increasing brand awareness in the inbox.
But finding the right subject line requires the endless application of A/B tests, segmentation and just plain dumb luck…or does it?
In this webinar, Dela Quist (CEO, AlchemyWorx), Parry Malm (CEO, Phrasee) and Dave Hendricks (President, LiveIntent) tell you:
• Why everything you know about subject lines is wrong
• Tips for crafting subject lines that increase awareness and opens
• New technologies that revolutionize the A/B testing process and make it possible to generate and optimize your email subject lines
Presented at Agile Bath & Bristol (21st March 2017)
If software development is a co-operative game, as Alistair Cockburn observed, then what kind of game is Scrum? Lots of people are playing it — or say they are — but there seems to be some disagreement about what the point of the game is, how to play it and even, in many cases, what the rules are. This talk looks at Scrum and other agile approaches through the lens of nomic games, hypothesis-driven development and fun.
Emily Greer at GDC 2018: Data-Driven or Data-Blinded?Kongregate
In the last decade of data analysis, A/B testing and predictive modeling have transitioned from an afterthought to a given in the game industry. Data can be invaluable in understanding the player and making decisions, but it can just as easily lead the industry astray, or worse, narrow the way the industry thinks. When should you be driven by data, and when should you let your imagination roam free? This session will expose common mistakes and pitfalls, both technical and emotional, as well as provide practical guidance on how to improve the rigorousness of your tests and the quality of your data, and how to make sure you don't lose the forest for the trees.
You are the ultimate data wrangler. The polyglot master of python and R. You know all about the differences of linear versus logistic regression. You know when to use a dimensionality reduction algorithm and when to use a neural net. You have petabytes of data taking structural-form at your command, and you have the R-squared score to prove it!
But all of your data wrangling and number crunching won't matter if the decision makers ignore your data.
The tools to communicate the message in your data are simple, yet they can be a hard to learn. So, let’s talk about the five critical communication tools you need to master "The Art of Speaking Data."
Deja vu Security CEO Adam Cecchetti was invited to present the keynote speech at this year's (sold-out!) Hushcon in Seattle. Rich in humorous anecdotes and practical analysis, Test For Echo explores the relationship between time, ken, and the future of computer security.
The final great presentation at MKGO3 in Milton Keynes recently. This one went right over my head but, if you're cleverer than me you will learn something useful
Research traditionally uncovers known complaints and desires in terms of what
people will tell you. However it is via contextual or ethnographic observation
that you can witness “real world” behaviors, influences, scenarios,
technologies, and actors all of which help you get the sense for what will
truly delight someone or alleviate frustration.
Noticing where people spend their time doing things they “have to” and
don’t “want to” will lead to inspiration of what would make their life
more convenient and less frustrating. An observation of what people want to do,
enjoy doing, or look forward to doing, will lead to inspiration around what
will make them shout from the rooftops in glee.
In this presentation we will discuss how research inspires design and how
reality inspires creativity.
If you simply ask users about what would make life better, you will rarely get
meaningful answers. They are just not good at envisioning revolutionary
solutions. It is really easy trap to fall into during a traditional usability
test to ask “what would the ideal experience be for you?” Unfortunately, if
you base your design on those responses, you won’t get a breakthrough.
Instead of relying on divine intervention for new ideas, we will focus on
activities such as Laddering, Game play, Storytelling and Triading that can
help expose opportunities for radical innovation and designing products that
people can’t live without.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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Groundbreaking research on the performance impact of software engineering practices derived from tens of thousands of teams across a wide variety of industries and contexts!
This session answers the questions your organization is asking.
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Static thresholds on metrics have been falling out of fashion for a while, and for good reason. Modern tooling lets you analyze and monitor a lot more data points than you used to be able to, resulting in lots more noise. The hope is that anomaly detection answers some of this, by replacing static thresholds (anomalies) with dynamic ones. But it doesn’t work as well as most people think it will. In this talk I’ll explain how anomaly detection works, so you can understand why it isn’t a good general-purpose solution, and which specific cases it’s good at.
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Some consider measurement in agile development destructive—or at the very least useless. Larry Maccherone disagrees and offers insight into how you can use metrics in an agile environment to make life better. How do you know when you are ready to introduce metrics into the environment? What are the sources for these metrics? What tools and techniques are necessary to make decisions probabilistically? What are the mindset shifts necessary for metrics to help you making better decisions? How do teams and organizations avoid the anti-patterns that so often derail a metrics program? Larry answers these questions and shows how to create a culture where measurement is an insight amplification and feedback mechanism—not a club to beat people up; where your teams seek out—rather than dread—the use of quantitative insight; and where metrics bring stakeholders and teams closer together—not drive them apart. Leave with the vision and understanding necessary to implement your own metrics regimen and make better decisions with data.
Your Agile Leadership Journey: Leading People-Managing Paradoxes - Agile Char...Paul Boos
This is the workshop Nicole and I gave at Agile Charm 2020 on Leading people through paradoxes, some of which are described directly in the Manifesto for Agile Software Development. It helps you understand how to use Polarity Maps as leaders for a thinking tool to understand your system.
Applied Data Science for monetization: pitfalls, common misconceptions, and n...DevGAMM Conference
This talk guides us through modern twists on classic user-oriented data science tasks, such as churn prediction, clusterization, calculating user metrics, and others. We will discuss unusual angles for solving these tasks; how and why they can be used to improve player experience and monetization; the intuition behind these methods, and insights into inner machinery; and why conventional methods work poorly. Finally, I'll show you how you can apply this knowledge to improve your users' playing experience, and streamline analytics; and we'll talk general situation of applied data science and analytics in the industry.
Beyond A/B: Tips and Tech for Optimizing to the OpenLiveIntent
The subject line is the most important piece of copy in every email you send. It has a direct impact on opens and engagement and presents a potent opportunity for increasing brand awareness in the inbox.
But finding the right subject line requires the endless application of A/B tests, segmentation and just plain dumb luck…or does it?
In this webinar, Dela Quist (CEO, AlchemyWorx), Parry Malm (CEO, Phrasee) and Dave Hendricks (President, LiveIntent) tell you:
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If software development is a co-operative game, as Alistair Cockburn observed, then what kind of game is Scrum? Lots of people are playing it — or say they are — but there seems to be some disagreement about what the point of the game is, how to play it and even, in many cases, what the rules are. This talk looks at Scrum and other agile approaches through the lens of nomic games, hypothesis-driven development and fun.
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In the last decade of data analysis, A/B testing and predictive modeling have transitioned from an afterthought to a given in the game industry. Data can be invaluable in understanding the player and making decisions, but it can just as easily lead the industry astray, or worse, narrow the way the industry thinks. When should you be driven by data, and when should you let your imagination roam free? This session will expose common mistakes and pitfalls, both technical and emotional, as well as provide practical guidance on how to improve the rigorousness of your tests and the quality of your data, and how to make sure you don't lose the forest for the trees.
You are the ultimate data wrangler. The polyglot master of python and R. You know all about the differences of linear versus logistic regression. You know when to use a dimensionality reduction algorithm and when to use a neural net. You have petabytes of data taking structural-form at your command, and you have the R-squared score to prove it!
But all of your data wrangling and number crunching won't matter if the decision makers ignore your data.
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Research traditionally uncovers known complaints and desires in terms of what
people will tell you. However it is via contextual or ethnographic observation
that you can witness “real world” behaviors, influences, scenarios,
technologies, and actors all of which help you get the sense for what will
truly delight someone or alleviate frustration.
Noticing where people spend their time doing things they “have to” and
don’t “want to” will lead to inspiration of what would make their life
more convenient and less frustrating. An observation of what people want to do,
enjoy doing, or look forward to doing, will lead to inspiration around what
will make them shout from the rooftops in glee.
In this presentation we will discuss how research inspires design and how
reality inspires creativity.
If you simply ask users about what would make life better, you will rarely get
meaningful answers. They are just not good at envisioning revolutionary
solutions. It is really easy trap to fall into during a traditional usability
test to ask “what would the ideal experience be for you?” Unfortunately, if
you base your design on those responses, you won’t get a breakthrough.
Instead of relying on divine intervention for new ideas, we will focus on
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5. @LMaccherone @TheAgileCraft
Bias eats good decisions
for breakfast, lunch, and
dinner
By understanding probabilistic
decision making, we learn to
trust and overcome bias
11. @LMaccherone @TheAgileCraft
1. Different Models
2. Different Values
3. Different Risk Tolerance
Why do people disagree?
favor different alternatives
Fear-based decision making
12. @LMaccherone @TheAgileCraft
Models and Values
§ Models calculate probability in terms of proxy variables
§ Values translate those probabilities into money
§ Different models example:
§ Joe forecasts that alternative A will make the most money
§ Sally forecasts that alternative B will make the most money
§ Different values example:
§ Betty favors the alternative with higher quality
§ George favors the alternative that will get to market faster
13. @LMaccherone @TheAgileCraft
So…
quality of decision depends upon:
1. alternatives considered, and
2. models used to forecast the
outcome of those alternatives.
Probabilistic models are superior
15. @LMaccherone @TheAgileCraft
For a given alternative, let:
Pg = Probability of good thing happening
Vg = “Value” of good thing happening
Then:
Value of the alternative = Pg × Vg
18. @LMaccherone @TheAgileCraft
If you get only 1 project then
strategy 2 is better
75% of the time
If you get ∞ projects then
strategy 1 is better
100% of the time
How many projects do you need for
strategy 1 to be better
more often than not?
23. @LMaccherone @TheAgileCraft
Did any of you get emotional
about the $1M loss?
Did any of you want to
question the $8M number?
We’ve totally…
…eliminated fear from the equation
…changed the nature of the conversation
26. @LMaccherone @TheAgileCraft
Trained/Calibrated
Untrained/Uncalibrated
Statistical Error
“Ideal” Confidence
30%
40%
50%
60%
70%
80%
90%
100%
50% 60% 80% 90% 100%
25
75 71 65 58
21
17
68 152
65
45
21
70%
Assessed Chance Of Being Correct
Percent Correct
99 # of Responses
We are inaccurate when assessing probabilities
Copyright HDR 2007
dwhubbard@hubbardresearch.com
But, training can “calibrate” people so that of all the times they
say they are X% confident, they will be right X% of the time
27. @LMaccherone @TheAgileCraft
Equivalent Bet calibration
What year did Newton published the Universal Laws of
Gravitation?
Pick year range that you are 90% certain it would fall within.
Win $1,000:
1. It is within your range;; or
2. You spin this wheel and it lands green
Adjust your range until 1 and 2 seem equal.
Even pretending to bet money works.
90%
10%
31. @LMaccherone @TheAgileCraft
Getting even more sophisticated
1. Only use slopes after it stabilizes. Discard the first N.
(Lumenize has v-optimal algorithm for finding this inflection point)
2. Weight later slopes more heavily.
3. Markov chain pattern reproduction. Accomplishes 1
and 2 above automatically.
4. Simulate the movement of each individual work item
through the system. Can find bottlenecks and help
optimize your role balance.
Troy Magennis has the expertise and tools for this.
34. @LMaccherone @TheAgileCraft
… but for those brave enough to journey
into the dangerous world of
agile measurement
there are great riches to be had.
The trick is to slay the dragons.
35. @LMaccherone @TheAgileCraft
The Dragons of Agile Measurement
If you do metrics wrong, you will harm your agile transformation
1. Dragon: Measurement as a lever
Slayer: Measurement as feedback
2. Dragon: Unbalanced metrics
Slayer: 1 each for Do it
fast/right/on-time, and Keep doing it
3. Dragon: Metrics can replace
thinking
Slayer: Metrics compliment thinking
4. Dragon: Expensive metrics
Slayer: 1st work with the data you
are already passively gathering
5. Dragon: Using a convenient
metric
Slayer: Outcomes ß Decisions ß
Insight ß Metric (ODIM)
6. Dragon: Bad analysis
Slayer: Simple stats and
simulation
7. Dragon: Single outcome forecasts
Slayer: Forecasts w/ probability
8. Dragon: Human emotion and bias
Slayer: Tricks to avoid your own
biases and overcome those of
others
41. @LMaccherone @TheAgileCraft
Dragon slayer #5
ODIM
O U T C O M E
D E C I S I O N
I N S I G H T
M E A S U R E
THINK
EFFECT
like Vic Basili’s
Goal-Question-Metric (GQM)
but without
ISO/IEC 15939 baggage
42. @LMaccherone @TheAgileCraft
The Dragons of Agile Measurement
If you do metrics wrong, you will harm your agile transformation
1. Dragon: Measurement as a lever
Slayer: Measurement as feedback
2. Dragon: Unbalanced metrics
Slayer: 1 each for Do it
fast/right/on-time, and Keep doing it
3. Dragon: Metrics can replace
thinking
Slayer: Metrics compliment thinking
4. Dragon: Expensive metrics
Slayer: 1st work with the data you
are already passively gathering
5. Dragon: Using a convenient
metric
Slayer: Outcomes ß Decisions ß
Insight ß Metric (ODIM)
6. Dragon: Bad analysis
Slayer: Simple stats and
simulation
7. Dragon: Single outcome forecasts
Slayer: Forecasts w/ probability
8. Dragon: Human emotion and bias
Slayer: Tricks to avoid your own
biases and overcome those of
others
43. @LMaccherone @TheAgileCraft
Top 10 criteria for great visualization
1. Answers the question,
"Compared with what?”
(SO What?)
2. Shows causality, or is at least
informed by it.
(NOW WHAT?)
3. Tells a story with whatever it
takes.
4. Is credible.
5. Has business value or impact in
its social context.
6. Shows
differences
easily.
7. Allows you to see the forest
AND the trees.
8. Informs along multiple
dimensions.
9. Leaves in the numbers where
possible.
10. Leaves out glitter.
Credits:
• Edward Tufte
• Stephen Few
• Gestalt
(School of Psychology)
44. @LMaccherone @TheAgileCraft
Now what?
• Questions?
• Day-long seminar on agile metrics
• Workshop to design your own
metrics regimen
• AgileCraft Demo - LJ Alefantis
• Contact me on LinkedIn
https://linkedin.com/in/larrymaccherone
45. @LMaccherone @TheAgileCraft
“They” say…
Nobody knows what’s gonna happen
next: not on a freeway, not in an
airplane, not inside our own bodies
and certainly not on a racetrack with
40 other infantile egomaniacs.
– Days of Thunder
Trying to predict the future is like
trying to drive down a country road
at night with no lights while looking
out the back window.
– Peter Drucker
Never make predictions, especially
about the future.
– Casey Stengel
47. @LMaccherone @TheAgileCraft
Now what?
• Questions?
• Day-long seminar on agile metrics
• Workshop to design your own
metrics regimen
• AgileCraft Demo - LJ Alefantis
• Contact me on LinkedIn
https://linkedin.com/in/larrymaccherone
