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Mostly.ai Summit Vienna 2017- Seizing the Machine Learning Opportunity, Ulla Kruhse-Lehtonen, DAIN Studios

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Gartner predicts 2017 to be the year, when Machine Learning reaches its peak in terms of inflated expectations. This is the right time to not only talk about the sea of opportunity, but also provide a no-nonsense view of the practical obstacles which organizations will encounter on their path to seizing them.
In this talk, Ulla cover these topics from a managerial perspective:
- Machine Learning use cases from the industry
- Factors of success and non-success
- Ideation process for data products
- Efficient organizational setup
- Recruiting & retaining of data scientists

Published in: Data & Analytics
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Mostly.ai Summit Vienna 2017- Seizing the Machine Learning Opportunity, Ulla Kruhse-Lehtonen, DAIN Studios

  1. 1. © Dain Studios 2016© Dain Studios 2016 Seizing the Machine-Learning Opportunity Mostly AI Summit, Vienna September 4, 2017 Ulla Kruhse-Lehtonen Tel. +358 45 639 3125 ulla.kruhse-lehtonen@dainstudios.com
  2. 2. © Dain Studios 2016 2 CEO, Founding Partner, DAIN Studios Vice President, Consumer Analytics and Insights, Sanoma Director, Consumer Analytics, Nokia Management Consultant, Accenture, XLENT, Nexus Economist, Labor Institute for Economic Research PhD, Economics, Helsinki School of Economics Information Leader of Year 2013, Finland Ulla Kruhse-Lehtonen
  3. 3. © Dain Studios 2016© Dain Studios 2016 DAIN Studios Services and Products DAIN Studios is a Digital and AI consulting and product company that started operations in March 2016 in Finland and Germany. Consultancy Data Strategy Digital Transformation Data Science Artificial Intelligence Consumer Privacy Data Management Business Intelligence Available Data Products Recommendation Engine Intelligent Data Platform Smart Dashboards Our clients include companies in - Telecommunications - Banks - Insurance - Airlines - Railways - Consumer goods - Media - Cybersecurity - Travel
  4. 4. © Dain Studios 2016 4 Data Scientist. The Sexiest Job at Sanoma. Source: Sanoma Strategy 2013
  5. 5. © Dain Studios 2016© Dain Studios 2016 Machine-learning applications across industries
  6. 6. © Dain Studios 2016 Companies can create new business with data 6 Internal DataExternal Data Current Business New Business Data & Analytics as a Business Business Optimization Business Optimization Data Partnerships Collaborate with external partners. Exchange data to enable new offerings or business models which would not be possible alone Provide 3rd parties with access to your data assets, insights, and analytical capabilities to enable them to grow and improve their business. Utilize external data sources to enhance your own data asset to enable further optimization of business processes Combine internal data to further optimize existing business and processes to enable new offerings
  7. 7. © Dain Studios 2016 A productized consumer data asset creates value for our current business and enables the innovation of new businesses and products Value of consumer data asset Time New, unknown innovations Improvements to current business Disbelief, frustration Energy and excitement
  8. 8. © Dain Studios 2016 Data and Analytics play a significant role in the development of intelligent products and services Source: Eric Rice, 2011
  9. 9. © Dain Studios 2016© Dain Studios 2016 Embedding analytics into business processes Service Roll-Out Business Spearheads Analytics Modeling It is much easier to roll out purely technical data products (e.g. recommendation engines) than products that involve people having to change their way of working (e.g. marketing automation)
  10. 10. © Dain Studios 2016© Dain Studios 2016 The vision and use cases guide direction for analytics
  11. 11. © Dain Studios 2016 Define the ambition level for data Data used for current business, product development, and new business areas AmbitiousModerate Use data for the optimization of your current business Data seen as an enabler Data seen as a strategic asset Mainly internal data used Use internal and external data for differentiation Focus on core business Own market seen widely No/limited commercialization of internal data APIs enable data as a business and data partnerships Example
  12. 12. © Dain Studios 2016 We have identified six company personas (A highly unscientific presentation) Black Box Optimist Details, Details Pessimist No Rush Covering our Backs Smart
  13. 13. © Dain Studios 2016© Dain Studios 2016 The Black Box Optimist Company rationale: • We are behind and need to do something quickly • Data Science is so complicated • Let’s get a tool where we can dump our data in and get insights out – it will cost money, but then it’s done Challenges: • Overestimate the possibilities of technology • Underestimate the impact on organization, required competences, process changes
  14. 14. © Dain Studios 2016© Dain Studios 2016 Details, Details Company rationale: • We need to have the business strategy, digital strategy, technology strategy, and marketing strategy including business case calculations for the next 5 years in place before we can start with execution Challenges: • Drowning in complexity • Stagnate and lose time by answering questions you only will be able to address when you get going
  15. 15. © Dain Studios 2016© Dain Studios 2016 The Pessimist Company rationale: • We don’t have anything (no people, no data,...) • These kinds of projects always fail (“the project that shall not be named”) • Let’s start with something very small that doesn’t disturb our core business Challenges: • Hard to make a business difference as use cases siloed, manual, and small • Company-level transformation not happening
  16. 16. © Dain Studios 2016© Dain Studios 2016 No Rush Company rationale: • At some point, we will probably need to ramp up our data capabilities, but our business is going well so there is no rush • Anyway, how would someone from the outside be able to tell us what to do? We are the experts of our business Challenges: • Past success is no guarantee of future results; total unpreparedness is dangerous • Individuals fear that data and analytics will challenge their role / position
  17. 17. © Dain Studios 2016© Dain Studios 2016 Covering our Backs Company rationale: • Let’s hire McKinsey/BCG/IBM/Accenture/… because they know what to do • If it fails anyway, we don’t get the blame as we used the big, expensive consultants Challenges: • Trying to outsource the thinking (and execution) instead of getting own hands dirty and leverage their domain knowledge • Not learning as an organization
  18. 18. © Dain Studios 2016© Dain Studios 2016 Smart Company rationale: • We want to be leaders in digital transformation • Our products and services need to be intelligent and adoptive • Our sales and marketing processes need to be based on customer understanding • We are not afraid of change; we embrace the journey Challenges: • Take the whole organization onto the journey • Satisfy the stock market
  19. 19. © Dain Studios 2016© Dain Studios 2016 Takeaways 1. Don’t outsource thinking 2. Analytics is a journey 3. You don’t need to know exactly where to start but get a couple of good people to start specing things out
  20. 20. © Dain Studios 2016 20 Recruiting Data Scientists (and other data people)
  21. 21. © Dain Studios 2016© Dain Studios 2016 AI and Big Data mean new roles for a company Analytics Strategist Data Scientist Consumer Data Privacy and Protection Officer Data Steward/ Custodian Solution Architect Data Architect Big Data Engineer Database Developer Business / Data Science Legal Technology
  22. 22. © Dain Studios 2016© Dain Studios 2016 ECRUITING DATA SCIENTISTS Look for team versatility Be prepared to pay a competitive salary Consider recruiting from abroad, but don’t outsource analytics completely unless your organization is very mature Do not only hire Data Scientists, you need the rest of the talents as well RETAINING TALENT • Give targets but don’t micromanage • Offer opportunities for professional growth • Give people a sense of belonging, that their work makes a difference Retaining and Recruiting Data Scientists
  23. 23. © Dain Studios 2016© Dain Studios 2016 Getting a job as a Data Scientist • Study quantitative topics; PhD a plus • Use data and modeling techniques during your studies • Learn to code • Show business-mindedness: Do not join a company to “do research”; understand the big picture and business goals • Show drivenness: Don’t just expect “requirements”
  24. 24. © Dain Studios 2016 DO • Invest in people: recruit key persons and improve the analytics skills of business people • Build passion for analytics at every level of the organization • Find common ways of working together across organizational silos ensuring end-to- end delivery of results • Define common metrics that guide strategic and operational decisions DON’T • Expect immediate ROI. Analytics transformation is a long-term effort • Do not believe that technology resolves the business questions of your company • Outsource data and analytics to one company function • Allow individual departments to opt out of strategically important implementation targets Do’s and Don’ts in Data Execution - Summary
  25. 25. © Dain Studios 2016 © Dain Studios 2016

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