Data Literacy

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Mufaddal Haidermota

Data Literacy is all about understanding and arguing with data and what it takes to become a data literate.

Data & Analytics

Definition
Literacy can be defined as
the ability to derive
information from written
words.
Data Literacy can be defined
as the ability to derive
meaningful information from
data and argue with data.
Let's broaden the above
definition and really
understand what data
literacy is!

Are you as curious as George!
 Reading and Understanding (Ask Questions)
Is it relevant ? Is it correct?
Should we go with the face value?
 Writing and Discovery (Look at data &
produce solutions)
More brains looking at data more varied solutions
 Curiosity and CriticalThinking (Critical Eye
towards the answer provided for the
question asked)
 Inclination towards asking questions,
 What question to ask,
 Critical eye towards the answer provided and
its usefulness

Do you trust your data ?
Data Literacy is the ability to
question if my data is accurate.
Data Literacy is the ability to
question if my data is useful.

Do you trust what you see ?
Data Literacy is the ability to
understand how aggregations
were calculated.
Data Literacy is the ability to
understand basic statistics.

Do you trust what you create ?
Data Literacy is the ability to use
facts to communicate complex
ideas - without misleading the
audience.
Data Literacy is the ability to use
all the facts, including missing
data.

The Difference
Shift from
the ability to make it
happen -> to the ability
to decide what to make
it happen.

It’s a Top-Down Change
The data literacy
initiative needs to start
from the top of the
corporate chain, for
impact and boarder
coverage of its
importance and
understanding.
Educational programs
are needed for data
literacy to drive the
change.

Challenges
 Dismiss data because they are
unable to comprehend it
 Cultural challenge
 Complexity (human thinking)
 No common language

Data – A Common Language
 New language we all
need to be fluent in.
 Data is a language that
anyone can learn to read
and speak.
 Start seeing data as the
beginning of the
conversation, not the
end.
 Engaging thoughtful
dialogue.
 Seeing data and deciding
what’s next !

Few Thoughts...
Data literacy isn't just about
visualizing charts or
understanding charts
Its much broader than that
..how we think about data that
is necessary,
then its all about people and
not about technology or data
science, it is how we uncover
the curiosity and ability to ask
the required questions

What's hot

The ever increasing computational capacity has enabled us to acquire, process and analyze larger data-sets and information. However, the human memory and attention required to use this data is more limited and has remained relatively constant. Data visualization can enable us to compress data and encode it visually in ways that allows us to aid perceptual and cognitive understanding. However, data visualisation alone is not enough and often we need to try to tell stories through data. Storytelling with data can enable us to move from analysis to synthesis, from numbers to visuals, and from an argument to a story. Operating at this intersection of data, visual and story can help persuade not only through logos (logic) but also through pathos (empathy) and ethos (credibility). In trying to tell compelling data stories, we can empower our selves to engage, communicate and persuade a large and diverse audience. In this talk, I discuss ‘why’ stories work and what we can learn about the art of storytelling from other mediums like oral storytelling, written stories, pictures, comics and movies. I will summarise basic principles that can help us in our crafting journey, as we take the data through the layers of abstraction. The focus would be on unpacking the seven dimensions of creating an engaging data story - Abstraction (data patterns), Representation (visual encoding), Framing & Transition (perspective, focus), Messaging (verbal, text annotation), Flow (arrangement) and Interactivity. Further, creating data stories is a cross disciplinary activity that requires us to operate at the intersection of a visual designer, data scientist and storyteller. It is both a science and an art. So how does one realistically learn these multitude of skills needed to get good at it. I will also discuss ideas about the possible path that practitioners could adopt to learn this craft through sustained practice. ## About the Speaker Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. He uses storytelling and data visualization as tools for improving communication, persuasion and leadership. He conducts workshops and trainings for corporates, non-profits, colleges, and individuals at narrativeVIZ Consulting. He also teaches storytelling with data as invited guest faculty in academia, both in management context at IIM Bangalore and IIM Ahmedabad and in design context at NID, Bangalore. His background is in strategy consulting in using data-driven stories to drive change across organizations and businesses. He has 15 years of management consulting experience, first with AT Kearney in India, then with Booz & Company in Europe and more recently with startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps

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