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On data literacy by Marek Danis

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This presentation will describe a new literacy: “data literacy”, the analogy with computer literacy, and reasons why this skillet will soon be as essential to all professionals as computer literacy is today. It will address the advent of decision making as the key managerial activity and the resulting democratization of AI and analytics. The presentation will address issues of mindset, as well as skillset, and the ways to acquire a basic level of data literacy to derive value from AI and ML assisted processes in one’s daily tasks.

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On data literacy by Marek Danis

  1. 1. On data literacy Marek Danis, MSc
  2. 2. Marek Danis, MSc Marek is an experienced data scientist and trainer with strong business outcome focus. Previously, Marek worked for more than 10 years for Schlumberger Oilfield Services around the world in several technical, engineering and training positions (including 3 years as a trainer of technical as well as corporate culture related content in Russia and UAE), and later with the Digital Transformation Team. Marek began his a Data Analytics education at the Texas A&M University Mays Business School in Houston. Since that time, he has developed a strong acumen for QHSE (Quality, Health, Safety and Environment) related analytics and data science. Marek also runs his own consulting company in Austria, specialising in QHSE analytics and how to decrease risk and increase business outcomes.
  3. 3. The obvious: we are in a period of rapid technological change ● We see new technologies every month ● “Would you like fries an app with for that?” But why is technological change so rapid and what are the consequences? “The only way to win is to learn faster than anyone else.” “The only way to win is to learn faster than anyone else.”
  4. 4. With technological change, comes social change Social change follows technological change (though at a more non-uniform pace) as we adapt, e.g: ● We adapted to literacy and numeracy as reading became the norm ● We became computer literate as computers became the norm How will we adapt to the widespread use of data & analytics? “The only way to win is to learn faster than anyone else.” “The only way to win is to learn faster than anyone else.”
  5. 5. Two key questions addressed in this talk: Why is technological change so rapid and what are the consequences? How will we adapt to the widespread use of data & analytics? NEXT
  6. 6. We’re getting better at running projects ‘Eric Ries - The Lean Startup, London Edition’ (cropped, B&W re-colour) by Betsy Webber available at https://www.flickr.com/photos/betsyweber/6730126217 under a CC by 2.0. “The only way to win is to learn faster than anyone else.” Why is technological change so rapid?
  7. 7. We’re getting better at running projects We’ve seen improvements in: ● Project methodology ● How we manage large groups of people ● What technology can enable: ○ New applications ○ Leverage pre-existing infrastructure ○ Reduction in project costs ○ Reduction in project risks ‘Eric Ries - The Lean Startup, London Edition’ (cropped, B&W re-colour) by Betsy Webber available at https://www.flickr.com/photos/betsyweber/6730126217 under a CC by 2.0. “The only way to win is to learn faster than anyone else.” Why is technological change so rapid?
  8. 8. Projects accessible to large companies / nation states are more accessible to you and me Traditional projects / start up Today you can also... Set up infrastructure: ● Hire technical expertise ● Buy fixed hardware ● Build / buy software ● Maintain software / hardware Rent infrastructure / use the cloud: ● Variable pricing ● Scalable Hire permanent staff, hire contractors through agencies etc. Access short-term experienced labour onsite or remote (e.g. AlphaZetta analytics consultancy) Require manual labour to interact with customers (stores / branches, calls etc.) Automate some interactions (i.e. via mobile apps) Why is technological change so rapid?
  9. 9. But even if we can build anything... “The big question of our time is not Can it be built? but Should it be built?” ― Eric Ries, The Lean Startup The key challenge is in deciding what to do in order to achieve key outcomes under uncertain conditions, competition and change. ‘Sipping Bird’ (cropped, B&W re-colour) by RobinLeicester available at https://commons.wikimedia.org/wiki/File:Sipping_Bird.jpg under a CC BY-SA 3.0. Consequence of technological change
  10. 10. The nature of work is changing: less about doing, more about deciding ● Doing things is becoming easier and faster ● Data literacy is playing a central role in business ● Many tasks are being automated across many sectors, meaning: With automation people will have to do what machines can’t do: DECIDE! Today ‘Tesla Autobots’ (cropped & BW re-colour) by Steve Jurvetson available at https://www.flickr.com/photos/44124348109@N01/6219463656 under a CC by 2.0. Consequence of technological change
  11. 11. Good decisions are more important than ever before ‘wocintech (microsoft) - 229’ (cropped & BW re-colour) by WOCinTech Chat available at https://www.flickr.com/photos/wocintechchat/25392653883 under a CC by 2.0. In particular good decisions are important when: ● You're in a in a highly uncertain & complex environment ● You have competitors ● You have to make a large volume of decisions ● Not making a decision is not an option, and making a bad decision has has an immediate and very bad outcome. Today
  12. 12. Using data can enable better decision making A key purpose of data and data analytics is to facilitate better decision-making in particular it enables us to: ● Process and use all information available to us ● Apply reason to all possible options and outcomes ● Decide in a timely manner Today
  13. 13. But… we’re not using data for good decision making Currently we are in an anomalous time: data-related technology and skills is changing but our professional environment is not necessarily changing with it: ● Social change is slow (e.g. similar to the computer revolution where some executives couldn’t use their web browser/email 10+ years ago) ● We are in comfortable economic times with little competition There are exceptions: where outcomes are ambiguous and they matter cultures tend to be data literate (e.g. hedge funds, sports betting, political parties, military intelligence etc.) ● Management class (and corporate cultures) are not necessarily data literate even if they hire data scientists. If data scientists are expensive tropical fish, then water is a data literate environment ‘Tesla Autobots’ (cropped & BW re-colour) by Steve Jurvetson available at https://www.flickr.com/photos/44124348109@N01/6219463656 under a CC by 2.0. Today
  14. 14. and… we erroneously think “data” = good decisions Socially we haven’t all caught up with what it means to use our data in professional environments: ● Buzzwords still abound: “data” + “analytics” with no mention of their role in decision making ● Many data scientists are miserable in lucrative careers ● Many data scientists are hired for roles other than that of a data scientist ● Many data scientists work in data illiterate environments Today ‘Confused’ (cropped & BW re-colour) by CollegeDegrees360 available at https://www.flickr.com/photos/83633410@N07/7658298768 under a CC by 2.0.
  15. 15. Data literacy is required for good decision support Everyone will be a data professional, doing what computers can’t do: searching for insights to support good decision making. This requires data literacy. A data literate professional is: ● comfortable with uncertainty ● ready and willing to conduct experiments ● Comfortable with basic mathematics and statistics ● Versed in some common data visualisation techniques Future ‘Tesla Autobots’ (cropped & BW re-colour) by Steve Jurvetson available at https://www.flickr.com/photos/44124348109@N01/6219463656 under a CC by 2.0. ‘Three people sitting beanbag chairs working’ (cropped & BW re-coloured) by katemangostar https://www.freepik.com/free-photo/three-people-sitting-beanbag-chairs-working_993093.htm How we adapt
  16. 16. Examples of data literacy Future Traditional white collar literacies New white collar literacies Literacy and simple numeracy Financial literacy Computer literacy Process and project literacy Basic visualisations (bar chart, pie chart etc.) Logic Narratives and storytelling See left + Probabilistic reasoning Experimentation / causality Scientific method Basic statistics Common visualisations (histograms etc.) Basic understanding of data science Abstraction Future Future Today How we adapt
  17. 17. The dangers of ignoring data literacy Ignoring the development of data literacy in your organisation means running the risk of: ● Being unable to process all the information available to you / your organisation ● Being unable to understand and question the output of data professionals ● Contributing to a floundering data analytics environment ● Missing opportunities, inability to pivot, no competitive edge FutureHow we adapt
  18. 18. Next steps Improve your data literacy: ● Statistical training ● data science courses & MOOCs Network: see data science meetups in your city Find a mentor Aim to be hired in a decision support role. Participate ‘Man suit thinking’ (cropped and BW re-colour) by asierromero available at https://www.freepik.com/free-photo/man-suit-thinking_927831.htm
  19. 19. Next steps: improve your data literacy Statistics: ● Bedtime reading: OpenIntro Statistics (www.openintro.org) ● If interested consider a masters in statistics / econometrics / biostatistics Data Literacy courses: ● Courses on data visualizations (see MOOCs) ● AlphaZetta Academy Relevant tools, e.g. SQL, Python and/or R courses ● Massive Open Online Courses (MOOCs) ● Study groups (see meetup.com) ● AlphaZetta courses Participate
  20. 20. Next steps: network & There are meetups in multiple capital cities for: ● Data science ● Tools (R, Python) ● Statistics
  21. 21. Next steps: find a mentor Look for a good mentor (see networking), expect them to: ● Have relevant experience & willingness to share their knowledge ● Be your champion ● Help you understand realistically your strengths and weaknesses ● Guide you in developing your data literacy (or in building your data science skills) Participate ‘wocintech (microsoft) - 229’ (cropped) by WOCinTech Chat available at https://www.flickr.com/photos/wocintechchat/25677265212under a CC by 2.0.
  22. 22. Next steps: working in decision support role ● You can work in data driven decision making as a data professional (skills permitting) or as a savvy data consumer (data literacy permitting). ● Very competitive hiring environment. Job-seekers have varied backgrounds / qualifications ● No fixed qualification / no centralised qualifying body / unprotected environment ● Hiring managers are often unqualified to hire data professionals or data literate professionals ● Fast paced technological environment ● High churn in the industry Participate Untitled (cropped and BW re-colour) by pxhere available at https://pxhere.com/en/photo/410489 under a CC BY 2.0.
  23. 23. The Independent Analytics Experts

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