- Marek Danis is an experienced data scientist and trainer who has worked for Schlumberger Oilfield Services and the Digital Transformation Team. He has a MSc from Texas A&M University Mays Business School and specializes in QHSE (Quality, Health, Safety and Environment) analytics. - Marek runs his own consulting company in Austria focusing on QHSE analytics and using data science to decrease risk and increase business outcomes. He has developed a strong understanding of data analytics applications in corporate environments.

1. On data literacy
Marek Danis, MSc

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. 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. 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.”

6. 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

7. 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?

8. 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?

9. 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?

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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.

16. 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

17. 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

18. 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

19. 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

20. 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

21. Next steps: network &
There are meetups in multiple capital cities for:
● Data science
● Tools (R, Python)
● Statistics

22. 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.

23. 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.

24. The Independent Analytics Experts