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How to Predict Your Software Project's Probability of Success

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How to Predict Your Software Project's Probability of Success

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Ever wonder why your software projects are never delivered on time and budget? Then take five minutes to learn why and how to calculate the probability of your project's success within the estimated time and budget.

Ever wonder why your software projects are never delivered on time and budget? Then take five minutes to learn why and how to calculate the probability of your project's success within the estimated time and budget.

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How to Predict Your Software Project's Probability of Success

  1. 1. 1 Why math prevents you from completing your projects on time & budget – and how to predict your probabilities of success ©Kevin J Mireles ©Kevin J Mireles @kevinjmireles
  2. 2. 2 Are we all knowing gods? ©Kevin J Mireles @kevinjmireles
  3. 3. Do we have crystal balls filled with perfect insight? Roadmaps that tell us how long and how to get there? 3©Kevin J Mireles @kevinjmireles
  4. 4. Or is what you can see only the tip of the iceberg, with 80% waiting to be discovered? 4 ? ©Kevin J Mireles @kevinjmireles
  5. 5. It’s easy to forecast the future when you have simple systems 5 1 2 ©Kevin J Mireles @kevinjmireles
  6. 6. But simple systems become exponentially evermore complex systems, as each system connects to other systems 6 10 5 N(N-1) 2 ©Kevin J Mireles @kevinjmireles
  7. 7. 7 10 66 N(N-1) 2 But simple systems become exponentially evermore complex systems ©Kevin J Mireles @kevinjmireles
  8. 8. The reality: In an organization as big, old and complex as FedEx it’s impossible to really understand all the systems, users, implications to accurately forecast the future 8 System Map of Fortune 100 company with 2,600 applications and more than 14,000 custom interfaces Until in large organizations they become so complex that understanding how all the pieces fit together and being able to identify impacted systems, users and processes becomes virtually impossible ©Kevin J Mireles @kevinjmireles
  9. 9. That complexity is compounded by age as people move on, retire and pass on 9©Kevin J Mireles @kevinjmireles
  10. 10. 10 By creativity & the paper-clip problem of people using your product in ways you never envisioned, resulting in you discovering new use cases you need to support that you won’t discover until you try retiring the old system and users revolt ©Kevin J Mireles @kevinjmireles
  11. 11. By siloed perspectives with different groups and people only comprehending their portion of the overall system, solution and challenge, so that unless you engage everyone you’ll never discover how everything fits together. 11©Kevin J Mireles @kevinjmireles
  12. 12. By outside forces that drive inevitable requirement changes 12©Kevin J Mireles @kevinjmireles
  13. 13. And ultimately your customers and users’ acceptance based on their needs, perceptions, amount of change required by users and their willingness to change 13©Kevin J Mireles @kevinjmireles Perceived Work Required = Amount of work, e.g. additional steps, learning, change, etc. required to adopt Power, Culture & Personality Factor = The various dynamics that determine someone’s willingness to try new things, change existing habits/processes Perceived Value = Benefit customer or users expect to receive, includes expected, initial & long-term value.
  14. 14. Despite the challenges, traditional project management assumes you can accurately forecast the future. 14 Project will cost $1.47M, be completed Jan. 27, 2019 resulting in ecstatic customers & $25M in incremental revenue ©Kevin J Mireles @kevinjmireles
  15. 15. Which is enshrined in the all-knowing Gantt chart Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 90% 3 90% 4 90% 5 90% 6 90% 7 90% 8 90% 9 90% 10 90% 11 90% 12 90% 13 90% 14 90% 15 90% 16 90% 17 90% 18 90% 19 90% 20 90% 15 Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ©Kevin J Mireles @kevinjmireles
  16. 16. But it assumes 100% confidence rates that all the tasks will be completed on time and with quality Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 90% 3 90% 4 90% 5 90% 6 90% 7 90% 8 90% 9 90% 10 90% 11 90% 12 90% 13 90% 14 90% 15 90% 16 90% 17 90% 18 90% 19 90% 20 90% 16 Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 100% 2 100% 3 100% 4 100% 5 100% 6 100% 7 100% 8 100% 9 100% 10 100% 11 100% 12 100% 13 100% 14 100% 15 100% 16 100% 17 100% 18 100% 19 100% 20 100% ©Kevin J Mireles @kevinjmireles
  17. 17. In reality, you’d be pretty excited if you had a 90% confidence level that each task would be completed in the allotted time frame Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 90% 3 90% 4 90% 5 90% 6 90% 7 90% 8 90% 9 90% 10 90% 11 90% 12 90% 13 90% 14 90% 15 90% 16 90% 17 90% 18 90% 19 90% 20 90% 17©Kevin J Mireles @kevinjmireles
  18. 18. Unfortunately, even with a 90% confidence rate of completing each task on time and with quality, the actual overall probability drops to 12% after 20 tasks. Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 90% 3 90% 4 90% 5 90% 6 90% 7 90% 8 90% 9 90% 10 90% 11 90% 12 90% 13 90% 14 90% 15 90% 16 90% 17 90% 18 90% 19 90% 20 90% 18 Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 81% 3 73% 4 66% 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 81% 3 73% 4 66% 5 59% 6 53% 7 48% 8 9 10 11 12 13 14 15 16 17 18 19 20 Weeks 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Tasks 1 90% 2 81% 3 73% 4 66% 5 59% 6 53% 7 48% 8 43% 9 39% 10 35% 11 31% 12 28% 13 25% 14 23% 15 21% 16 19% 17 17% 18 15% 19 14% 20 12% ©Kevin J Mireles @kevinjmireles
  19. 19. 19 The ability to forecast the future decays exponentially making it impossible to accurately predict outcomes for projects that combine both uncertainty and complexity ©Kevin J Mireles @kevinjmireles (% Probability of Success for Each Task )^(# of Variables) = Overall Probability of Success as Initially Planned (100%)^20 100% (95%)^20 36% (90%)^20 12% (80%)^20 1% (70%)^20 0%
  20. 20. At the end of PI Planning do not average fist of five confidence rates, multiply them to get your true probability of success! 20 The same rules of probability impact your forecasting ability when you have multiple teams working on multiple components required for a feature/EPIC. ©Kevin J Mireles @kevinjmireles
  21. 21. Understand that predictability and innovation are inversely related. If innovation goes up, predictability goes down. 21 Innovation Predictability High Innovation Low Predictability Low Innovation High Predictability ©Kevin J Mireles @kevinjmireles
  22. 22. The probability of successfully executing business-software project within estimated time & scope can be roughly quantified by multiplying the probability of technical success by the probability of customer adoption by the number of discrete components. 22 Probability of Technical Success X Probability of Successful Customer Adoption( )^N Perceived Work Required = Amount of work, e.g. additional steps, learning, change, etc. required to adopt Power, Culture & Personality Factor = The various dynamics that determine someone’s willingness to try new things, change existing habits/processes Perceived Value* - Perceived Work Required*( +/- 100 ) Power, Culture & Personality Factor* Probability of Successful Customer Adoption = Perceived Value = Benefit customer or users expect to receive, includes expected, initial & long-term value. * Expressed as number between -100 & 100 N = Number of discrete project components, e.g. tasks, user stories, features, epics, etc. The larger than number of variables, lower probability of success % Confidence in Team X % Confidence in Technology )(Probability of Technical Success = % Confidence in Team = % Confidence that the team has the technical skills, resources, cohesion and subject matter expertise, etc. to successfully deliver a technically sound solution with the available technology % Confidence in Technology = % Confidence that the technology chosen has the usability, stability, flexibility, scalability, etc. to meet the project’s requirements ©Kevin J Mireles @kevinjmireles
  23. 23. 23©Kevin J Mireles @kevinjmireles What you’ll see is that any complexity and uncertainty drives your ability to accurately forecast project scope and timelines to almost zero quickly. Probability of Technical Success X Probability of Successful Customer Adoption( )^N = Overall probability of project success 100% X 100% ( )^1 = 100% 100% X 80% ( )^1 = 80% 80% X 80% ( )^1 = 60% 80% X 80% ( )^2 = 41% 80% X 80% ( )^3 = 26% 80% X 80% ( )^4 = 17%
  24. 24. Technical Ability Questions: Below are some questions to ask and probability ranges for determining confidence in team’s technical ability to execute per their estimate. This is far from complete list. 24 Probability of Accurately Forecasting Success Low confidence Probability = 0-40% Medium Confidence Probability = 41%-70% High Confidence Probability = 80%-100% What is the team cohesion & performance like? New team. Struggling team. Some experience. Together <1 year Stable high-performing team How much experience does the team have with the technology? Little to none. New area software, form factor, etc Some experience but still learning Experts. Done similar projects previously How much expertise does the team have with the overall subject matter? Little to none. Doesn’t understand the business. Some experience but not experts. Experts, understand not only common use cases but exceptions as well Are separate IT ecosystems being merged? Separate ecosystems with different data models, business rules, etc. Two or more similar systems being merged Enhancing existing functionality How much experience does the team have with the specific existing systems & processes? Little to none. Never worked on app & no documentation Worked on it but not a systems expert Experts. Helped develop system. Know where are all the quirks are. How much experience does the team have with the specific users? Little to none. Many different types of users Some familiarity Worked as a user How much will the changes impact other systems or other systems impact you? High. Requires changes to dozens/unknown # Small changes No changes If interacting with other systems, how much control does team have over the other systems. Low. Need to request assistance from group with different priorities/ governance Some. Can shape other teams’ priorities but still risks High control. Complete control over dependent systems© Kevin J Mireles
  25. 25. Technology Confidence Questions: Below are some questions to ask and probability ranges for determining confidence in technology to execute per their estimate. 25 Probability of Accurately Forecasting Success Low confidence Probability = 0-40% Medium Confidence Probability = 41%-70% High Confidence Probability = 80%-100% Is the technology proven? Never at our company or to solve similar problems with similar scale, etc. Yes, In similar companies or our organization, but not in exact same manner Yes! Standard part of technology stack Has the technology been proven to scale? Never at our company or to solve similar problems with similar scale, etc. Yes, In similar companies or our organization, but not in exact same manner Absolutely! No issues. How easy is it to develop for? Don’t know. Not an easy-to- develop for/in platform. Lots of quirks & unintuitive workarounds Works reasonably well & understand quirks. Easy & developers like it! Has the technology been deployed in a customer-facing manner? No. Or Don’t know. Yes at other companies. Yes, within our company Can it be easily customized to meet our unique use cases? No. Or Don’t know. Should be able to but haven’t fully validated. Yes. How stable/bug-free is the technology? Not stable. Buggy technology. Should be fairly reliable but haven’t deployed in our org. Stable & bug free. © Kevin J Mireles
  26. 26. Perceived Value Questions: Below are some questions to ask and probability ranges for determining perceived value. 26 Probability of Accurately Forecasting Success Low confidence Probability = 0-40% Medium Confidence Probability = 41%-70% High Confidence Probability = 80%-100% Does the product match and exceed current capabilities of existing system critical to core users? No. Matches some but not all functionality. It’s an MVP not MVR. Yes. Matches all existing functionality Matches & exceeds all functionality Are the benefits immediately visible to the end user? No. Requires work & or training to discover functionality For the most part, but requires some level of discovery Everything is completely visible Do you have a narrowly focused target market with fairly homogenous needs & traits or a broad range of users with different needs/use cases? Target = whole world. From large to small, etc Will serve a variety of different users with a variety of use cases Laser focused on specific subset of users. Do you know who your most important customers are? No More or less. Absolutely! Know each & everyone by name, role, etc. What level of usability & value testing have you done? Testing? What’s that? Tested interactive prototype but not functional. Thoroughly tested completely usable prototype with key users Does the product save time & money or increase revenue No. No change from existing system Absolutely! Saves both time & money! How does the application impact key customer metrics Negatively impacts or no alignment to existing KPIs Somewhat, but not directly. Aligned to key goals, e.g. closing new sales. © Kevin J Mireles
  27. 27. Perceived Work-Required Questions: Below are some questions to ask and probability ranges for determining perceived work-required. 27 Probability of Accurately Forecasting Success Low confidence Probability = 0-40% Medium Confidence Probability = 41%-70% High Confidence Probability = 80%-100% Does the application eliminate the need for a person to operate the system? No. Requires additional people. No change Completely automates the process, so no UI or operator required. Does the application subtract or add work for the user? Adds work.  No change Eliminates time-consuming steps!  Does the application require training? Yes! Lots & lots of repetition to get good at it but users will use it infrequently. Some training would be good but fairly intuitive No! Eliminates the user interface all together How much work is required for before get value? Requires not just training, but integration into other systems, massaging data, etc. before get value Some setup required but fairly straightforward. No work whatsoever or someone else does all the work. Does getting value require organizational & process changes? Yes! Need to change fundamental processes, roles & Organizations in order to get benefit Minor changes to process. No changes whatsoever beyond eliminating steps people hated. © Kevin J Mireles
  28. 28. Power, culture, personality & other factors: What additional factors will increase or decrease your probability of successful adoption 28 Probability of Accurately Forecasting Success Decrease confidence Subtract up to 30% Neither good nor bad No change Increase Confidence Add up to 30% Power dynamics: Who has the power in the relationship? They do! They are your largest customer & can easily switch as there are lots of competitors They need you as much as you need them • You are one of their largest customers and they can’t get paid unless they use the new software. • They’re lower-level employees with little power Culture/Personality: Does the organization culture/ users’ personalities embrace or reject change? Org has been working same way for decades & embraces tradition as core value Willing to try new things & changes when makes sense Org is always looking for new toys and embraces change as competitive advantage Regulatory or other environmental constraints Regulations or other things about the environment make adopting new systems high risk Regulations require adopting new system to comply Money: How much will it cost to make changes? Customer has to pay lots of money to adopt Customer is paid to make changes Etc. © Kevin J Mireles
  29. 29. 29 Some Confidence Less Confidence Even Less Confidence The inability to predict the future is why adopting agile & agile planning are so critical and why project plans and timelines lead to unrealistic expectations Current Program Increment Next Program Increment Next +1 Program Increment Future Work No Confidence Agile doesn’t mean no planning, it embraces the fact that omniscience is an illusion and change is mandatory for success! Instead agile requires not less planning, but more planning, just done iteratively and at different levels of precision based on the size/complexity of the work and the time frame. ©Kevin J Mireles @kevinjmireles

Editor's Notes

  • Waterfall: Can and should know everything therefore can and should be able to forecast the future
  • The probability of successfully predicting the outcome of a specific task or an entire project is driven by the complexity, i.e. number of variables involved, and the probability of successfully achieving the goals for the task or project
  • Waterfall: Can and should know everything therefore can and should be able to forecast the future
  • The probability of successfully predicting the outcome of a specific task or an entire project is driven by the complexity, i.e. number of variables involved, and the probability of successfully achieving the goals for the task or project
  • The probability of successfully predicting the outcome of a specific task or an entire project is driven by the complexity, i.e. number of variables involved, and the probability of successfully achieving the goals for the task or project
  • The probability of successfully predicting the outcome of a specific task or an entire project is driven by the complexity, i.e. number of variables involved, and the probability of successfully achieving the goals for the task or project
  • The probability of successfully predicting the outcome of a specific task or an entire project is driven by the complexity, i.e. number of variables involved, and the probability of successfully achieving the goals for the task or project
  • The probability of successfully predicting the outcome of a specific task or an entire project is driven by the complexity, i.e. number of variables involved, and the probability of successfully achieving the goals for the task or project

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