How do you cut the Big Data clutter and tell interesting, insightful and impacting stories? This session talks about the need for Data Visualization & how Visual stories can come to the aid of the Big Data problem associated with meaningful consumption. The point is illustrated by leveraging several industry case studies.
1. MAKING SENSE OF BIG DATA:
VISUAL STORY TELLING
B GANES KESARI,
VP, GRAMENER
2. A data visualisation and analytics company
We handle terabyte-size data via non-traditional analytics and visualise it in real-time.
Gramener visualises
your data
Gramener transforms your data into concise dashboards
that make your business problem & solution visually obvious.
We help you find insights quickly, based on cognitive research,
and our visualisations guide you towards actionable decisions.
3. Generation Analysis Consumption
Big data…
Transaction data
Increasing volumes of data
being churned out by systems
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Social network data
Consumers embracing Web
2.0 & social media lifestyle
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
M2M data
Devices generating & logging
data from every activity
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
creates opportunities
Each industry is poised to take advantage of big
data to varying degrees. Some factors that increase
the relevance of big data to an industry are:
Volume of data
The larger the volume of data, the
more likely it is that a firm will
benefit from increasing use of data.
Variability
Greater fluctuations in performance
offer more potential for a data-driven
organisation to improve results.
Customer intensity
More customers (or stakeholders of
any kind) offer greater potential for
segmentation and tailored action.
Transaction intensity
This permits greater automation of
decision making, allowing processing
power to replace human judgement.
Turbulence
Frequency at which leaders and
laggards change place in a sector
indicates potential for disruption.
McKinsey Global Institute, Big Data, June 2011
… but at a high cost
Investments of various kinds are required to make
the data actionable. It is not enough that the data
just exists, or is collected. Some challenges are:
Technology
Collection, storage, analysis and
visualisation of data – all require
investments in modern technology.
Talent
The deeper the analysis & data
expertise a firm has, the better it can
leverage data. But such talent is rare.
Organisational change
A shift in mind-set from experience-driven
decision making to data driven
decision making is required.
Data access
Collecting relevant data, storing it,
and making it available to analysts in
an easy manner requires investment.
Supplier ecosystem
A mature vendor ecosystem
providing end-to-end or piece-wise
solutions to these is not yet a reality.
4. A DATA VISUALISATION
CHALLENGE…
You will see 3 questions.
You have 30 seconds.
Try it!
Your timer
starts now
16. VISUALIZING THE
GENERAL ELECTIONS
Can we understand the brief history of
elections in India?
How have the political fortunes
changed over time?
How did the biggest election of them
all unfold in 2014?
EXPLORATORY | INTERACTIVE
17. India’s General Elections landscape…
~300 Parties fielding 8000 candidates
~1 Mn booths served by 20 Mn people
~800 Mn Registered Voters
Varied data on several parameters
~21,000 Votes/sec of live results
A Big Data problem… in every sense
19. LIVE ELECTION ANALYSIS
Our CNN-IBN
Microsoft Election
Analytics Canter, which
you can see at
www.bing.com/electio
ns or election-results.
ibnlive.in.com,
served over 10 million
requests on 16th May
2014 — the day of India
election results.
This is one of the
largest real-time
visualisations that we
(and perhaps many
others) have attempted
http://ibn.gramener.com/live
21. INDIA’S MOST
PERSISTENT PARTY
Does any party hold a consistent 100%
failure rate?
Which party holds record for being most
persistent in adversity?
Which party’s candidates have lost deposits
for nearly a decade?
EXPLANATORY| STATIC
23. VISUALIZING
WEATHER
How did weather change in India over
the past century?
What were the hottest and coldest
places?
Are there places that exhibit some
interesting patterns?
EXPLANATORY| VIDEO
Image credit:
https://www.flickr.com/photos/vesiaphotography/11627471004
24. 100 YEARS OF INDIA’S WEATHER
1901
1911
1921
1931
1941
1951
1961
1971
1981
1991
2001
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
http://www.youtube.com/watch?v=WT0Aq41BaOQ
25. STORIES FROM TEXT
Can business impacting stories be
mined from large bodies of text?
Can investors better read companies by
studying Investor earning calls?
Can companies understand what
analysts want & be better prepared?
EXPLANATORY| INTERACTIVE
Image credit: ttps://www.flickr.com/photos/a_mason/3009985823
26. HOW IS THE TEXT PROCESSED?
Web Scraping Tokenization Part-of-
Speech
tagging
Entity Transform
detection
Text Analytics Engine
Analytics Engine
Compute
Visualization Engine
Ticker Qtr #Qns
AAPL 53% 3
AAPL 51% 7
GS 52% 6
MSFT 53% 4
... ... ...
MS 54% 9
JP 53% 6
... ... ...
Data Extraction
Ticker Qtr %Gr
AAPL 53% 23%
AAPL 51% -35%
GS 52% 95%
MSFT 53% 101%
... ... ...
MS 54% 14%
JP 53% 20%
... ... ...
28. VISUALIZING
MOVIES
What are the popular, critically
acclaimed ones?
Where do my preferences figure?
Which one should I watch next?
EXPLORATORY| INTERACTIVE
29. The Shawshank
Redepmption
The Godfather
The Dark Knight
Titanic
The Phantom
Menace
Twilight
New Moon
Wild Wild West
Transformers
The Good, The
Bad, The Ugly
12 Angry
Men
7 Samurai
Rang De
Basanti
Taare Zameen
Par
Yojinbo
MORE VOTES
BETTER RATED
Many unwatched movies
Few unwatched movies
Mix of watched & unwatched
Few watched movies
Many watched movies
Movies on the IMDb
3 Idiots
https://gramener.com/imdb/
31. BEST PLACES TO
LIVE
FINDING ‘BEST
PLACES’ TO LIVE IN
Can we plug into public data to
better understand cities?
Can we identify the best places to
live?
Can this be customized to an
individual level?
EXPLORATORY| INTERACTIVE
Image credit: https://www.flickr.com/photos/dynamosquito/2431025077
33. WHAT DOES THE
WORLD SEARCH FOR?
What are some questions that interest
people ?
How does this vary across countries?
Can we do ongoing ‘search-listening’?
EXPLORATORY| INTERACTIVE
Image credit:
https://www.flickr.com/photos/uberculture/2561190022
37. Session Slides available on Slideshare at:
http://www.slideshare.net/gramener/hydspin-dec14-visual-story-telling
Ganes Kesari
Twitter: @kesaritweets
Email: ganes.kesari@gramener.com
gramener.com
blog.gramener.com
http://slideshare.net/gramener
Editor's Notes
Gramener is a data analytics and visualisation company.
We have the ability to process data at a small and a large scale.
We analyse the data to find non-intuitive insights that lie hidden behind it and present it as a visual story that makes those insights obvious in real time.
Let’s take a small test. We’ll show a table of numbers on the screen, and ask 3 questions about those numbers. You have 30 seconds to answer these. You can just write down the answers or remember them – there’s no need to say the answer out aloud.
Your timer starts now.
What answers did you get?
How many numbers were above 100?
How many were below 10?
Which quadrant had the highest total?
[Typically, there will be a lot of variance in these answers]
So there’s considerable variation in the answers you get.
Now, let’s do the same exercise again, but with some extremely simple highlighting. It’s the same questions. You have 30 seconds. This time, you can say the answer out aloud if you like.
Your time starts now.