tm forum latin america 2013
TRANSCRIPT
Gustavo Gattass AyubTechnology StrategistMicrosoft Brazil
Future of Analytics and Business Intelligence
Beyond the Hype
70% of U.S.
smartphone owners
regularly shop online
via their devices.
44% of users
(350M people)
access Facebook via
mobile devices.
50% of
millennials use
mobile devices to
research products.
60% of U.S.
mobile data will be
audio and video
streaming by 2014.
Mobility
2/3 of the world's
mobile data traffic will
be video by 2016.
33% of BI will
be consumed via
handheld devices
by 2013.
Gaming consoles are
now used an average of
1.5 hrs/wkto connect to the
Internet.
80% growth of
unstructured data is
predicted over the
next five years.
1.8 zettabytes of
digital data were in
use worldwide in 2011,
up 30% from 2010.
1 in 4Facebook users
add their location
to posts
(2B/month).
500M Tweets
are hosted on
Twitter each day.
38% of people
recommend a brand
they “like” or follow
on a social network.
100MFacebook “likes”
per day.
Brands get
Big DataSocial
Mobility Cloud
Tackling growth in the volume, velocity and variety of data
Major Technology Trends driving Big Data
40M people
and over
2B hrs/mo
232M
unique
searchers
450M unique
users per
month
180M
unique users
per month*
294,000 unique
consumers
per month
Over
1 billion users
worldwide
900M ads
per month
70M
consoles sold in
38 countries
Over 20M
sensors sold
520M unique
visitors per
month
Windows Live
Messenger
280M connected
users per month
Just A Few Microsoft Consumer Services
Some Big Data Application Areas in Microsoft
Industry Stats XBOX
TARGETINGCYCLE TIME REDUCTION
Collect, Store and Correlate data
from different sources
Improve Xbox Live end-user
experience
Understand Usage Patterns
PRODUCT PLANNING
SEGMENTATION/SENTIMENT ANALYSIS
Analyze actual customers usage
patterns
Segment customers based on
usage patterns
Sentiment analysis /Social Media
analysis / Text mining
MS.COM & Adworks
TARGETINGSURFACE TABLET, WINDOWS PHONE 8
Adworks – optimizing
performance for advertising
customers
Analyze 30 TB per day
Web site analytics on
MS.com
SECURITY
SECURITYINTRUSION DETECTION
Compliment & enhance existing
Defense in Depth and incident
response systems
Analyze data in flight across
networks
Anticipate attack vectors
5
Building Blocks To Success
• Standard models for known business problems,
• Some analytics with unstructured data
• Value of enterprise level analytics recognized
• Self service BI launched
• Model scores fed into decision engines in batch mode
• External customer treatments driven by value segments
• Robust analytics on both unstructured and structured data
• COE for Advanced analytics & client showcases
Big Data-Big Math capabilities
Enterprise wide service
• Differentiated products and services by segment
• Insight-driven real-time interactions with customers and partners
• Analytic capabilities as an external service
• Industry recognition as best in classSustainable Competitive
Advantage
At The Starting Gate
Winning Customer
Maturity Model FrameworkDATA |
INFORMATION
| ACTION
What does it take to achieve world-class maturity in Enterprise Analytics?
…Evaluate where we fall in the analytics maturity model, & determine where we need to be
• Analysis localized, no best practices sharing
• Data is structured, and in small cubes
• Excessive manual analyses, no real-time decision making
6
TerabytesMegabytes
BI Professionals
Self Service
Analysis with
Power Pivot &
Power ViewBusiness Analysts
Interactivity &
exploration
with data in
Excel
Exabytes
DATA |
INFORMATION
| ACTIONMicrosoft Data Science Team
Data Scientists
Advanced
Analytics from
Microsoft and
3rd parties,
Hadoop
BIG DATA DATA SCIENTIST Refers to datasets whose size is beyond the
ability of typical database software tools to capture, store, manage & analyze
It is game changing - it mashes up external data (beyond firewall) with internal for insight
Often unstructured and disparate data sources Big Data does not have to be transactional
Creates value for the enterprise by transforming data into analysis solutions for real-time decision making and implementing these solutions in a production environment for access by business users
Data Scientist deals in Big Math. Has deep quantitative skills in mathematics, statistics, machine learning, and programming
…Volume, Variety, Velocity => Harder to derive insight but can lead to big business value
Think Lifecycle !!!
How to succeed on Big Data ?
