Digital infographics can use graphics to more easily convey information through visualization. There are different types of infographics including spatial, chronological, and quantitative infographics that use diagrams, charts, maps, and other visual elements to communicate information. Big data refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations. Big data is used across many industries for applications like customer analytics, predictive maintenance, risk analysis, and more. Hadoop is an open-source software framework that allows distributed processing of large data sets across clusters of computers using MapReduce.

1. Digital Infographics and
BIG DATA
A Crash Course

2. Info – Graphics
• Using graphic representations in order to convey information.
• Easier to remember
• Easier to understand
• Looks pretty

3. Spatial Inforgraphics
Information that describes relative
positions and the spatial
relationships in a physical or
conceptual location.

4. Chronographical Infographics
Information that describes sequential positions and the causal
relationships in a physical or conceptual timeline

5. Quantitative Infographics
Information that describes scale,
proportion, change and
organization of quantities in
space, time or both.

6. Diagrams

7. Icons

8. Sequence

9. Process

10. Timeline

11. Maps
Locator: Shows location in relation to something
else
Data: Shows quantitative information in relation
to its geographic location
Schematic: shows abstracted representations of
geography, process, or sequence

12. Charts
Flow Charts
Organization
Bar Chart
Pie Charts

13. LATCH / Pyramid / Familiarity
Location
Alphabet
Time
Category
Hierarchy
} Group by content!
Most important things go on top,
or early in the story!
Familiarity Helps!

14. Tricks to remember

15. Communication Methods
• Static – Information presented immediately and there is no motion
• Motion - Information presented progressively in a linear sequence
• Interactive – Information Presented Selectively based on user
choice

16. BIG data
Data that exceeds the processing power of traditional databases
Large amounts of
information
Rate of data flow Diverse data sources,
Layouts and formats

17. What is it used for?

18. What is it used for?
• Consumer product companies and retail organizations are monitoring social media like
Facebook and Twitter to get an unprecedented view into customer behavior, preferences, and
product perception.
• Manufacturers are monitoring minute vibration data from their equipment, which changes
slightly as it wears down, to predict the optimal time to replace or maintain. Replacing it too
soon wastes money; replacing it too late triggers an expensive work stoppage
• Manufacturers are also monitoring social networks, but with a different goal than marketers:
They are using it to detect aftermarket support issues before a warranty failure becomes
publicly detrimental.
• The government is making data public at both the national, state, and city level for users to
develop new applications that can generate public good. Learn how government agencies
significantly reduce the barrier to implementing open data with NuCivic Data
• Financial Services organizations are using data mined from customer interactions to slice and
dice their users into finely tuned segments. This enables these financial institutions to create
increasingly relevant and sophisticated offers.

19. What is it used for?
• Advertising and marketing agencies are tracking social media to understand responsiveness to
campaigns, promotions, and other advertising mediums.
• Insurance companies are using Big Data analysis to see which home insurance applications can
be immediately processed, and which ones need a validating in-person visit from an agent.
• By embracing social media, retail organizations are engaging brand advocates, changing the
perception of brand antagonists, and even enabling enthusiastic customers to sell their
products.
• Hospitals are analyzing medical data and patient records to predict those patients that are
likely to seek readmission within a few months of discharge. The hospital can then intervene
in hopes of preventing another costly hospital stay.
• Web-based businesses are developing information products that combine data gathered from
customers to offer more appealing recommendations and more successful coupon programs.
• Sports teams are using data for tracking ticket sales and even for tracking team strategies.

20. Real life examples – Real time data analysis
When a customer jokingly tweeted
the Chicago-based steakhouse
chain and requested that dinner be
sent to the Newark airport, where
he would be getting in late after a
long day of work, Morton's became
a player in a social media stunt
heard 'round the Interwebs. The
steakhouse saw the tweet,
discovered he was a frequent
customer (and frequent tweeter),
pulled data on what he typically
ordered, figured out which flight he
was on, and then sent a tuxedo-clad
delivery person to serve him his
dinner.

21. Real life examples
Macy's Inc. The retailer adjusts pricing in near-real time
for 73 million + items, based on demand and inventory.
PredPol Inc. The software can predict where crimes are
likely to occur down to 500 square feet. In LA, there's
been a 33% reduction in burglaries and 21% reduction in
violent crimes in areas where the software is being used.
Tesco. The supermarket chain collected 70 million
refrigerator-related data points coming off its units and fed
them into a dedicated data warehouse. Those data points
were analyzed to keep better tabs on performance, gauge
when the machines might need to be serviced and do
more proactive maintenance to cut down on energy costs
Companies like Time Warner,
Comcast, and Cablevision are
using big data to track media
consumption and engagement,
advertising, and customer
retention as well as operations
and infrastructure. The video
game industry is using big data
for tracking during gameplay and
after, predicting performance,
and analyzing over 500GB of
structured data and 4 TB of
operational logs each day.

22. Technologies
Traditional Setup
BIG Data with Hadoop

23. Traditional Database Layout
Structured Data Sources
This is the data creation component.
Typically, these are applications that
capture transactional data that gets
stored in a relational database. Example
sources include: ERP, CRM, financial
data, POS data, trouble tickets, e-
commerce and legacy apps.
Enterprise data warehouse (EDW)
This is the data storage component. The EDW is a
repository of integrated data from multiple structured
data sources used for reporting and data analysis.
Data integration tools, such as ETL, are typically used
to extract, transform and load structured data into a
relational or column-oriented DBMS. Example storage
components include:operational warehouse, analytical
warehouse (or sandbox), data mart, operational data
store (ODS) and data warehouse appliance.
Business Intelligence /
Analytics
This is the data action component.
These are the applications, tools
and utilities designed for users to
access, interact, analyze and make
decisions using data in relational
databases and warehouses.

24. BIG data and Hadoop
Unstructured data sources
This is the data creation component.
Typically, this is data that’s not or cannot be
stored in a structured, relational database.
Includes both semi-structured and
unstructured data sources. Example sources
include: email, social data, XML data,
videos, audio files, photos, GPS, satellite
images, sensor data, spreadsheets, web log
data, mobile data, RFID tags and PDF docs.
Hadoop (HDFS)
The Hadoop Distributed File System is
the data storage component of the
open source Apache Hadoop project.
It can store any type of data –
structured, semi-structured and
unstructured. It is designed to run on
low-cost commodity hardware and is
able to scale out quickly and cheaply
across thousands of machines.
Big data apps
This is the data action component.
These are the applications, tools and
utilities that have been natively built
for users to access, interact, analyze
and make decisions using data in
Hadoop and other nonrelational
storage systems. NOTE: It does not
include traditional BI/analytics applications
or tools that have been extended to
support Hadoop.

25. You may hear the term ‘MapReduce’
Don’t panic… it’s nothing complicated (in theory).
MapReduce is the resource management and processing component of Hadoop. MapReduce allows Hadoop
developers to write optimized programs that can process large volumes of data, structured and unstructured,
in parallel across clusters of machines in a reliable and fault-tolerant way.
Another benefit of MapReduce is that it processes the data where it resides (in HDFS)instead of moving it
around, as is sometimes the case in a traditional EDW system. It also comes with a built-in recovery system –
so if one machine goes down, MapReduce knows where to go to get another copy of the data.
Although MapReduce processing is lightning fast when compared to more traditional methods, its jobs must
be run in batch mode. This has proven to be a limitation for organizations that need to process data more
frequently and/or closer to real time. The good news is that with the release of Hadoop 2.0, the resource
management functionality has been packaged separately (it’s called YARN) so that MapReduce doesn’t get
bottlenecked and can stay focused on what it does best: processing data.

26. Remember… Hybrid structure is also possible
and extensively used