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Minimum viable replacement presentation sIM presentation

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Minimum viable replacement presentation sIM presentation

  1. 1. ©2021 Kevin J Mireles 1
  2. 2. Agenda What marriages and projects have in common Why Large Enterprise Technology Projects are so Painful The Problem with MVPs When Replacing Legacy Technologies An Introduction to the Minimum Viable Replacement Framework Kev’s Five Steps to MVR Nirvana Feedback ©2021 Kevin J Mireles 2
  3. 3. What percentage of large IT projects fail? ©2021 Kevin J Mireles 3
  4. 4. What percentages of marriages fail? ©2021 Kevin J Mireles 4
  5. 5. Why? ©2021 Kevin J Mireles 5 Expectations Reality
  6. 6. The challenge is too many people think we have crystal balls filled with perfect insight 6 ©Kevin J Mireles @kevinjmireles
  7. 7. When in reality we can see only the tip of the iceberg, with 80% waiting to be discovered 7 ? ©Kevin J Mireles @kevinjmireles
  8. 8. My goal tonight is to help you align ©2021 Kevin J Mireles 8 Expectations Reality & to Maximize Your Probability of Success!
  9. 9. So what are my qualifications? ©Kevin J Mireles @kevinjmireles 9
  10. 10. ©2021 Kevin J Mireles 10 And what could be done differently? Why was it so painful?
  11. 11. 1. Applied the MVP model to replacing legacy systems 2. Thought could accurately predict the future 3. Missed key risks by focusing on averages 4. Failed to measure impact at a customer level ©2021 Kevin J Mireles 11 Why was it so painful? 1. Use the MVR framework to replace legacy systems 2. Accept uncertainty & adopt probabilistic approach to project management 3. Identify & solve for extremes 4. Quantify & integrate impact into project plans & status And what could be done differently? And these are my answers, which I’ll explain in detail to you!
  12. 12. MVPs are great when introducing entirely new capabilities to new customers ©2021 Kevin J Mireles 12
  13. 13. But what if your customers already have a car? ©2021 Kevin J Mireles 13
  14. 14. And that’s customized just for them? ©2021 Kevin J Mireles 14
  15. 15. Plus, they are being driven by your largest customers! ©2021 Kevin J Mireles 15
  16. 16. ©2021 Kevin J Mireles 16 + And unless you get them to swap their old car for the new car, you’ll still need to maintain all of your old legacy systems
  17. 17. Will the MVP concept work then? ©2021 Kevin J Mireles 17
  18. 18. Instead, embrace the Minimum Viable Replacement! ©2021 Kevin J Mireles 18 MVR
  19. 19. 1 9 Minimum Viable Product • New Technology • New customers Minimum Viable Replacement • New Technology • Existing customers ©2021 KEVIN J MIRELES MVRs focus on replacing existing technology for existing customers versus
  20. 20. Minimum Viable Replacement • What’s the bare minimum I have to do do to migrate all of my existing customers to the new version and retire the old system? 20 MVPs & MVRs have different end goals Minimum Viable Product • What’s the bare minimum I need to do to get an initial slice of customers? ©2021 KEVIN J MIRELES versus
  21. 21. Minimum Viable Replacement • Will my existing customers switch from their old version to the new version? 21 MVPs have to prove their value, while MVRs have to prove they’re good enough to get customers to upgrade Minimum Viable Product • Is there a market for my product? ©2021 KEVIN J MIRELES
  22. 22. Minimum Viable Replacement • Primarily compete with your existing products 22 The biggest competitors for a minimum viable replacement are the previous versions of the product Minimum Viable Product • Compete with other companies & products ©2021 KEVIN J MIRELES
  23. 23. 23 MVP ©2021 KEVIN J MIRELES
  24. 24. ©2021 Kevin J Mireles 24
  25. 25. ©2021 Kevin J Mireles 25 While as a percentage of customers, the number of laggards may be small, the largest customers will also usually be your biggest laggards Minimum Viable Replacement
  26. 26. For example, which companies are still using mainframes? ©2021 KEVIN J MIRELES 26 Mainframes are used by 71% of the Fortune 500 Joe’s hotdog stand or
  27. 27. MVP G MVP F MVP E MVP D MVP C MVP B 27 Minimum Viable Replacement = Sum total of all the MVPs required to meet all customer needs required to transition existing customers and retire existing system Customer segment A Customer segment B Customer segment C Customer segment D Customer segment E CustomersegmentF Customer segment G Segment Revenue Opportunity Simplest to most complex Smallest to largest MVP A ©2021 KEVIN J MIRELES
  28. 28. Minimum Viable Replacement MVP G MVP F MVP E MVP D MVP C MVP B 28 Plus any additional enhancements/ inducements required to get stakeholders and customers to change and adopt new systems and processes MVP A Customer segment A Customer segment B Customer segment C Customer segment D Customer segment E CustomersegmentF Customer segment G Segment Revenue Opportunity Simplest to most complex Smallest to largest ©2021 KEVIN J MIRELES
  29. 29. Minimum Viable Replacement MVP G 1,2,3,4, 5,6,7, 8, etc. MVP F MVP E MVP D MVP C MVP B 29 And if individual customers are large enough to have customer- specific requirements, then an MVP/MVR has to be developed for each customer MVP A Customer segment A Customer segment B Customer segment C Customer segment D Customer segment E CustomersegmentF Customer segment G Segment Revenue Opportunity Simplest to most complex Smallest to largest ©2021 KEVIN J MIRELES
  30. 30. 30 While avoiding customer breakage is key, you have to balance the desire to meet your existing customers’ needs with the opportunity to serve new market segments with different functionality A Bird in the Hand? Two in the Bush Someone is always going to be unhappy, the question is who, what are the consequences and which tradeoffs are you willing to make? or ©2021 KEVIN J MIRELES
  31. 31. The Five Steps to MVR Nirvana ©2021 KEVIN J MIRELES 31 1 • Assess whether you’re dealing with an MVR or not 2 • Break your project into its constituent components • Define the segment MVPs/MVRS 3 • Identify & quantify sources of risk and opportunities • Set realistic expectations regarding risk, timelines, scope, etc. 4 • Develop & execute strategies to maximize benefits and minimize risk 5 • Iterate, iterate, and iterate some more
  32. 32. 1. Assess: Is this an MVR? Retiring legacy technology? Requires migrating existing users to new technology? Breakage is a significant risk? Common examples:  Merging and retiring existing systems post acquisition  Sunsetting a legacy product  Transitioning to a new underlying technology ©2021 Kevin J Mireles 32
  33. 33. Step 2: Decompose your project from both an execution & adoption perspective ©2021 KEVIN J MIRELES 33  • Assess whether you’re dealing with an MVR or not 2 • Break your project into its constituent components • Define the segment MVPs/MVRS 3 • Identify & quantify sources of risk and opportunities • Set realistic expectations regarding risk, timelines, scope, etc. 4 • Develop & execute strategies to maximize benefits and minimize risk 5 • Iterate, iterate, and iterate some more
  34. 34. MVP G MVP F MVP E MVP D MVP C MVP B 34 Identify your customer segments and then the capabilities/MVPs required for each customer segment Customer segment A Customer segment B Customer segment C Customer segment D Customer segment E CustomersegmentF Customer segment G Segment Revenue Opportunity Simplest to most complex Smallest to largest MVP A ©2021 KEVIN J MIRELES
  35. 35. Minimum Viable Replacement MVP G MVP F MVP E MVP D MVPC MVP B MVP A 35 Since you are dealing with enterprise icebergs, where 80% of the work required is invisible, impossible to know exact requirements Customer segment A Customer segment B Customer segment C Customer segment D Customer segment E CustomersegmentF Segment Revenue Opportunity Simplest to most complex Smallest to largest Customer segment G ©2021 KEVIN J MIRELES
  36. 36. Minimum Viable Replacement MVP G MVP F MVP E MVP D MVPC MVP B MVP A 36 The more customizations and changes required by internal and external stakeholders, the more difficult the migration will be Customer segment A Customer segment B Customer segment C Customer segment D Customer segment E CustomersegmentF Segment Revenue Opportunity Simplest to most complex Smallest to largest Customer segment G ©2021 KEVIN J MIRELES
  37. 37. Step 3: Identify & quantify sources of risk & opportunities ©2021 KEVIN J MIRELES 37  • Assess whether you’re dealing with an MVR or not  • Break your project into its constituent components, both from an execution and adoption perspective 3 • Identify & quantify sources of risk and opportunities • Set realistic expectations regarding risk, timelines, scope, etc. 4 • Develop & execute strategies to maximize benefits and minimize risk 5 • Iterate, iterate, and iterate some more
  38. 38. Use the KUCI Model to identify and quantify key sources of risk and opportunity ©2021 Kevin J Mireles 38 Risk Uncertainty Complexity Impact Kevin’s
  39. 39. It provides the math and the logic to explain why most estimates, project statuses and Gannt charts are just pretty lies ©2021 Kevin J Mireles 39 From Pretty Lies • We know what needs to be done • We know how to do it • We know when it will be done • We know how much it will cost • It’s all right here on our Project Plan! To Ugly Truths We have a hypothesis!
  40. 40. First, understand the relationship between uncertainty & complexity ©2021 Kevin J Mireles 40 Risk Uncertainty Complexity
  41. 41. 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% Probability Pop Quiz! ©2021 Kevin J Mireles 41 If you are 90% confident that each task can be completed on time & with quality, what’s the probability that the predicted outcome will be achieved after 20 tasks?
  42. 42. 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% Answer = 12% ©2021 Kevin J Mireles 42
  43. 43. ©2021 Kevin J Mireles 43 (% 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% The ability to forecast the future decays exponentially making it impossible to accurately predict outcomes for projects that combine both uncertainty and complexity! Key Takeaway!
  44. 44. Which is why projects should start out red, and only change to yellow and green after key milestones are hit ©2021 KEVIN J MIRELES 44 Cone of uncertainty Source: wikimedia.org Cone of uncertainty
  45. 45. No different than a marriage or any other journey into the unknown – where the beginning is much different than reality ©2021 KEVIN J MIRELES 45 Source: wikimedia.org Just because there’s high likelihood of failure and you know the road ahead won’t go as planned, doesn’t mean you don’t get married!
  46. 46. Second, let’s define & understand the concept of impact ©2021 Kevin J Mireles 46 Risk Uncertainty Complexity Impact Kevin’s
  47. 47. Are all customers, scenarios & ultimately impacts the same? ©2021 Kevin J Mireles 47
  48. 48. No, some are much, much bigger than others! ©2021 Kevin J Mireles 48 $523,964 $260,174 $1,996 $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 0 200 400 600 800 1000 Rank Revenue in millions
  49. 49. Walmart is 263X larger than smallest Fortune 1,000 ©2021 Kevin J Mireles 49 $523,964 $260,174 $1,996 $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 0 200 400 600 800 1000 Rank Revenue in millions
  50. 50. And 11 million times bigger than the average sole proprietorship or 13K times bigger than the largest small business ©2021 Kevin J Mireles 50 Business Size Average Revenue in millions How many times bigger Walmart is Zero Employees $ 0.046 11,390,522 1 to 4 Employees $ 0.387 1,353,912 5 to 9 Employees $ 1.000 523,964 10 to 19 Employees $ 2.164 242,128 20 to 99 Employees $ 7.124 73,549 100 to 499 Employees $ 40.775 12,850 # 1000 on Fortune 1000 $ 1,990.000 263 Walmart $ 523,964.000
  51. 51. Not only does impact tend to be hyper-concentrated, it positively correlates with complexity & uncertainty ©2021 KEVIN J MIRELES 51 48 # of Brands 24 # of Countries 1,152 Potential Permutations X = 48 # of Brands 24 # of Countries 12,000,000 Potential Permutations X = 10,500 Locations X 24 # of Countries 24 Potential Permutations =
  52. 52. Which is why replacement projects tend to blow up just when you think they are almost done ©2021 Kevin J Mireles 52
  53. 53. The KUCI Risk Identification & Quantification model can help you avoid surprises throughout the project ©2021 Kevin J Mireles 53 Risk Identification& Quantification Uncertainty Complexity Impact Kevin’s
  54. 54. Key KUCI components ©2021 Kevin J Mireles 54 Risk Uncertainty Execution How confident are you that you can deliver the solution as defined? Adoption How confident are you that the solution delivered will be adopted by your end users/customers? Complexity Variables How many variables, e.g. tasks, teams, technologies, user types, geographies etc. are involved in the project? Impact Upsides When/where in the project lifecycle/ user base are the benefits concentrated? Downsides What can go wrong? What are the potential downsides if not successful, and when/where will the impacts be concentrated?
  55. 55. Uncertainty in software projects is primarily driven by two things ©2021 KEVIN J MIRELES 55 Probability of Technical Success X Probability of Successful Customer Adoption ( ) Can you execute the work as defined? Will the dogs eat the dog food?
  56. 56. Which I’ve translated into an equation ©2021 KEVIN J MIRELES 56 Probability of Technical Success X Probability of Successful Customer Adoption ( ) % 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
  57. 57. Confidence in Team Questions ©2021 KEVIN J MIRELES 57 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
  58. 58. Technology Confidence Questions 58 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. ©2021 Kevin J Mireles
  59. 59. The second half of the equation is the probability of successful customer adoption ©2021 KEVIN J MIRELES 59 Probability of Technical Success X Probability of Successful Customer Adoption ( ) 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* ( +/- 10 ) 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 1 & 10
  60. 60. Perceived Value Questions 60 Perceived Value Ranges Low Perceived Value 0 to 5 Medium Value 6 to 8 High value 9 to 10 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. ©2021 Kevin J Mireles
  61. 61. Perceived Work-Required Questions 61 Perceived Work Required Low perceived work 0 to 5 Medium Perceived Work 6 to 8 High Perceived Work 9 to 10 Does the application eliminate the need for a person to operate the system? Completely automates the process, so no UI or operator required. No change No. Requires additional people. Does the application subtract or add work for the user? Eliminates time- consuming steps!  No change Adds work.  Does the application require training? No! Eliminates the user interface all together Some training would be good but fairly intuitive Yes! Lots & lots of repetition to get good at it but users will use it infrequently. How much work is required before get value? No work whatsoever or someone else does all the work. Some setup required but fairly straightforward. Requires not just training, but integration into other systems, massaging data, etc. before get value Does getting value require organizational & process changes? No changes whatsoever beyond eliminating steps people hated. Minor changes to process. Yes! Need to change fundamental processes, roles & Organizations in order to get benefit ©2021 Kevin J Mireles
  62. 62. Power, culture, personality & other factors: What additional factors will increase or decrease your probability of successful adoption 62 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. ©2021 Kevin J Mireles
  63. 63. Which you can then use to forecast your project’s overall probability of success as currently defined ©2021 KEVIN J MIRELES 63 Probability Technical Success Probability of Adoption Success Overall Probability of Success Team Confidence X Technology Confidence X Feature A 90% 90% 90% = 73% Feature B 80% 80% 80% 51% Feature C 80% 80% 80% 51% Overall Probability 58% 58% 58% 19%
  64. 64. Step 4: Develop & execute strategies to maximize benefits & minimize risk ©2021 KEVIN J MIRELES 64  • Assess whether you’re dealing with an MVR or not  • Break your project into its constituent components, both from an execution and adoption perspective  • Identify & quantify sources of risk and opportunities • Set realistic expectations regarding risk, timelines, scope, etc. 4 • Develop & execute strategies to maximize benefits and minimize risk 5 • Iterate, iterate, and iterate some more
  65. 65. Start by focusing on the extremes ©2021 Kevin J Mireles 65
  66. 66. 66 Traditional UX guidelines espouse focusing on the 20% of the scenarios that meet 80% of your customer needs For an MVP, it makes sense to focus on the 20% of the capabilities that meet the needs of the majority ©2021 KEVIN J MIRELES
  67. 67. 67 For an MVR focus on the one percent that drives the 40- 60% of your revenue or impact and 80% of your complexity ©2021 KEVIN J MIRELES
  68. 68. 68 The 1% have such vastly different requirements from the other 99% that trying to transform a system designed for everyone else is almost impossible ©2021 KEVIN J MIRELES
  69. 69. 69 Design & Architect for the 1% while delivering for the 80% first Identify the business and scalability requirements for the largest, most complex, highest- risk customers/ scenarios first. But deliver simpler solutions that meet the needs of your less complex customers first so you can deliver value to market sooner while building more complex solutions for later delivery Design 1st Build 1st Build 1st ©2021 KEVIN J MIRELES
  70. 70. 70 Or develop entirely different systems to meet the very different needs Standard designs & features for the 99% Custom solutions for the 1% Shared Infrastructure whenever possible ©2021 KEVIN J MIRELES
  71. 71. Step 5: Iterate, iterate and iterate some more ©2021 KEVIN J MIRELES 71  • Assess whether you’re dealing with an MVR or not  • Break your project into its constituent components, both from an execution and adoption perspective 3 • Identify & quantify sources of risk and opportunities • Set realistic expectations regarding risk, timelines, scope, etc. 4 • Develop & execute strategies to maximize benefits and minimize risk 5 • Iterate, iterate, and iterate some more
  72. 72. 72 Ultimately, successfully executing a Minimum Viable Replacement is a lot like trying to get to the other side of a mountain range without a detailed map, not impossible but much more difficult than your management expects, so good luck and god speed! – Kevin Mireles ©2021 KEVIN J MIRELES
  73. 73. 73 Visit www.DontMakeMeWork.com to read more about Minimum Viable Replacements or email Kevin@kevinmireles.com or connect with me on Linkedin! Thanks! Kevin Mireles ©2021 KEVIN J MIRELES

Editor's Notes

  • I’ve worked in a warehouse, as a gas station attendant, a food service worker 3X, reporter, editor, private investigator, hammock salesman in the Amazon, photographer, house painter, door-to-door cable TV salesman, furniture mover, tour guide, project manager, and even a product manager for a small shipping company
  • New technology designed to replace existing solution for your existing customers with the goal of moving everyone off of legacy systems and retiring your prior offerings
  • Why? Because the extremes usually drive the biggest impact
  • The challenge is that complexity, uncertainty and impact all tend to go hand-in-hand
  • 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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