The data literacy revolution

Dr Eugene Dubossarsky0414 573 322 | eugene@presciient.com | @cargomoose Find out more! https://presciient.com/media/

Talk outline

1. The data Landscape: technological change, do-ers and decision makers

2. Data analytics today:● Data analytics is in a

transitional phase● Data literacy is immature

2. The future of data analytics:● What is driving change● How change drives data

literacy

3. How you can participate in the data literacy revolution

The data literacyrevolution

‘Black and White Landscape with Horse’ (cropped) by Nick Page available at https://www.flickr.com/photos/nicksie2008/12953056854 under a CC by 2.0.

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?

https://pxhere.com/en/photo/1376465

“The only way to win is to learn faster than anyone else.”

Landscape

“The only way to win is to learn faster than anyone else.”

With technological change, comes social changeSocial 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?

https://pxhere.com/en/photo/1458907

“The only way to win is to learn faster than anyone else.”

Landscape

“The only way to win is to learn faster than anyone else.”

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?

Landscape

NEXT

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.”

LandscapeWhy is technological change so rapid?

We’re getting better at running projectsWe’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.”

Landscape

Projects accessible to large companies / nation states are more accessible to you and me

Landscape

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. Alpha zeta analytics consultancy)

Require manual labour to interact with customers (stores / branches, calls etc.)

Automate some interactions (i.e. via mobile apps)

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.

Landscape

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

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

Warning: “data” ≠ good decisionsSocially 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.

Using data can enable better decision makingA 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

Data literacy is required for good decision supportEveryone 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

Examples of data literacy Future

Traditional white collar literacies New white collar literacies

Literacy and simple numeracyFinancial literacyComputer literacyProcess and project literacyBasic visualisations (bar chart, pie chart etc.)Logic Narratives and storytelling

See left +

Probabilistic reasoningExperimentation / causalityScientific methodBasic statisticsCommon visualisations (histograms etc.)Basic understanding of data scienceAbstraction

Future

Future

Today

The dangers of ignoring data literacy Analogy: 10, 20, 30 years ago it was okay if an executive was computer illiterate and could not use:

● A web browser● Email● A computer

Is this this case today?

Asking why you need to be data literate (if you’re not a data professional) is the same as asking why a non-IT professional should be able to use their own email!

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.

‘Boston Police staff working at computer’ (cropped) by Boston City Archives available at https://www.flickr.com/photos/cityofbostonarchives/10086196513/ under a CC BY 2.0.

The dangers of ignoring data literacy (cont.) 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

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.

‘Boston Police staff working at computer’ (cropped) by Boston City Archives available at https://www.flickr.com/photos/cityofbostonarchives/10086196513/ under a CC BY 2.0.

The dangers of ignoring data literacy (cont.) 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 competitionThere 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

What is the data literacy of your environment?Are you:

● Comfortable with uncertainty

● Ready and willing to conduct experiments

● Comfortable with basic mathematics and statistics

● Versed in some common data visualisation techniques

Participate

‘Social Network Analysis Visualization’ (cropped & BW re-coloured) by Martin Grandjean https://commons.wikimedia.org/wiki/File:Social_Network_Analysis_Visualization.png under a CC BY-SA 3.0.

What does uncertainty look like in a business?The data analytics revolution requires a degree of comfort with the uncertain. It requires taking an experimental approach to business:

● A large part of the analytics function serves discovery based exploration

● A “nothing ventured nothing gained attitude”

● New type of deciding and managing.

You cannot plan for uncertainty:

● Managing uncertainty is not a waterfall project

● You can’t map the unknown on a Gantt chart

● Findings / insights are the deliverable (focus), not (only) building models

What does experimentation look like in a business?Experimentation helps us learn something new about the world and sometimes why something happens. It requires the collaboration of data professionals (i.e. data scientists) and data literate professionals (everyone else).

A data literate professional / manager:

● Works with data professionals to generate a hypothesis / idea for an experiment

● Decides if experimental results make sense

● Asks relevant questions about the data, experimental process and outcomes (sample size, data sources etc.)

This image was released by the National Cancer Institute, an agency part of the National Institutes of Health, with the ID 2310 (see:

https://visualsonline.cancer.gov/retrieve.cfm?imageid=2310&dpi=300&fileformat=jpg)

Next stepsImprove 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. See my talk “What data scientists should ask a prospective employer”

[details next!]

Participate

‘Man suit thinking’ (cropped and BW re-colour) by asierromero available at https://www.freepik.com/free-photo/man-suit-thinking_927831.htm

Next steps: improve your data literacyStatistics:

● 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)

● Presciient courses

Relevant tools, e.g. SQL, Python and/or R courses

● Massive Open Online Courses (MOOCs)● Study groups (see meetup.com)● Presciient courses

Participate

Next steps: network &There are meetups in multiple capital cities for:

● Data science● Tools (R, Python)● Statistics

Next steps: find a mentorLook 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

‘Man suit thinking’ (cropped and BW re-colour) by asierromero available at https://www.freepik.com/free-photo/man-suit-thinking_927831.htm

Next steps [optional]: work in data driven decision makingYou can work in data driven decision making as a data professional (skills permitting) or as a savvy data consumer (data literacy permitting). To get started:

1. aim to be hired in a decision support role and

2. see my talk “What data scientists should ask a prospective employer” @ presciient.com/media/

Participate

Next steps [optional]: The challenge of working in data driven decision making

● 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.

Thankyou& questions

presciient.com/media/presciient.com/current-courses/