ML4ALL Conference promoting ML
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How to promote machine learning in your organization
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ML4ALL Conference promoting ML
- 1. Breaking the barriers to ML in your organization Rich Jolly VP, Data Science Hawes Group May 29, 2018 @rich_jolly
- 2. Key Messages • Proactively address management’s concerns • Use value stream mapping to find key leverage points • Focus – use 80/20 rule • Create a virtuous cycle – build on your wins Luddites @rich_jolly
- 3. But we don’t have the software or compute power to do that! @rich_jolly
- 4. Recent advances transform Data Science Compute availability Rich, open source software Powerful, open source machine learning libraries The capabilities that a few years ago only the big players could achieve, are now within the reach of most companies! @rich_jolly
- 5. BI, AI, ML, Analytics, Big Data – I don’t even know how to think about it! @rich_jolly
- 6. Framing Analytics Projects Business Intelligence Data Science Information – guiding decisions Automation – improving operations Data Set Size @rich_jolly
- 7. But what should we work on? @rich_jolly
- 8. Value stream mapping • From lean management • Series of events - taking a product or service from its raw materials through to the customer • Material and information flow • Focus on areas that add value • To identify and remove waste (muda) • Focus on points of high leverage @rich_jolly Cf. https://en.wikipedia.org/wiki/Value_stream_mapping
- 9. Example: VSM & ML in an Agile Process • Collaboration – NLP checking team code for synergy (duplication, expertise, etc.) • Documentation –Automatically creating artifacts or real time tracking of progress • Automatic generation of simple code tasks Source: https://www.linkedin.com/pulse/making-machine-learning-part-your-future-agile-team-ashish-gupta/ http://www.ryantomlinson.com/6-engineering-organisation-anti-patterns/ https://www.slideshare.net/RichardJollyPhD/rich-jolly-executive-vp-corporate-ebitda-from-data-science @rich_jolly
- 10. Pareto Rules! • A great deal of machine learning benefit can be realized with reasonable effort! – Don’t forget regression • Rank opportunities – Pick low hanging fruit • Monitor progress – And results @rich_jolly
- 11. What should we watch out for? @rich_jolly
- 12. Common pitfalls in data analysis • Looking for silver bullets • Searching for keys under the lamp post • Conflation of effects and variables • Bring me a rock • Lack of domain expertise For further reading: - ‘Minding the Analytics Gap,’ Ransobtham, Kiron and Kirk Prentics, MIT Sloan Mgmt Review, Spring 2015 @rich_jolly
- 13. Even if we have some success, how can we sustain it? @rich_jolly
- 14. Build a virtuous cycle Successful Analytics Projects More organizational confidence More resources Building analytic capabilities is an evolutionary process @rich_jolly
- 15. OK, you’ve convinced me. Let’s give it a shot! @rich_jolly
Editor's Notes
- What are some of the areas to watch out for in data analysis projects? First, don’t expect the team to deliver silver bullets. Usually, analytics projects deliver slow, steady, but measurable benefits. Second, avoid the ‘Searching for keys under the lamp post’ effect. If you’ve never heard the joke, let me tell it. ‘Tell joke’ Sometimes the data team needs to move outside the comfort of the most well traveled databases to find what is sought. Next, be careful not to conflate effects with variables. For example, your goal with a project may be to improve patient experience as measured by their response in a post appointment survey. You make it a goal to reduce their wait time to achieve this goal. However, you may find that once you have reduced the wait time, the survey response on patient experience has decreased. The ‘effect’ here, the patient experience, is a complex function of many variables, one of which is wait time. By reducing wait time, you may have impacted one of the other variables. Remember to monitor your key dependent variable. Have you heard the parable of ‘bring me a rock?’ ‘Tell it’ Make the most efficient use of your data science team by being as explicit as you can as you outline your questions. Another common pitfall is lack of domain experience. If you are hiring a consultant be sure to ask about their expertise in your area.