2. US$883.55B
IoT related revenue is expected to grow from USD 130.33
Billion in 2015 to USD 883.55 Billion by 2022, at a CAGR
of 32.4% between 2016 and 2022
Source: http://www.postscapes.com/internet-of-things-market-size/
4. LIFECYCLE OF IT..
Problem
Statement
Problem
Statement
Design
Thinking
Design
Thinking
Business Model
Workshop
Business Model
Workshop
Eco-System
Development
Eco-System
Development
Scalability of
Innovation
Scalability of
Innovation
Define the problem in a single sentence
using everyday words for absolute general
audience
Problem
Statement
Put yourself in the shoes of your users.. All
users.
Design
Thinking
How would you sustain the Innovation? What
is the revenue model?
Business
Model
Workshop
We can not do any BIG thing alone! So, a
very collaborative Eco-System is a must!
Eco-System
Development
Once we Innovate, then we need to scale
the innovation with sustainable progress
Progressive
Innovation
5. It is a structured process of Innovation! Adding the touch of Practicality!
Viability
to be a
BUSINESS
Desirability
By
PEOPLE
Feasibility
with
TECHNOLOGY
Process
Innovation
Functional
Innovation
Emotional
Innovation
6. EDUCATION...
Institutional
Education
• Intellectual
Readiness
Institutional
Education
• Intellectual
Readiness
Professional
Education
• Technical
Readiness
Professional
Education
• Technical
Readiness
Forward
Education
• Trend
Readiness
Forward
Education
• Trend
Readiness
Practical
Education
• Leadership
Readiness
Practical
Education
• Leadership
Readiness
This is scoring for Institutional Knowledge (course
curriculum, instructors & student peers); score yourself by
comparing the rank of the institution & rank of the program
Intellectual
Readiness
Have you learned something beyond your curriculum?
Internship, competition? Local, Global? Did you take some
certification training from the vendors? Contributed?
Technical
Readiness
Forward education is fundamental for Innovation.The
question is, who’s done it already? How would you know?
How often you read a new book? A Journal? A
blog/slideshare/vlog? How many and how often you
blog/tweet/post/respond?
Trend
Readiness
Technical competencies are sometime barricade for
leadership, as it tends on micro-controls.What is your
influential power? Is this progressing? Managing on
eXperience of Customers, Employees, Industry, Leaders and
beyond?Your Style??
Leadership
Readiness
7. APTITUDE...
AttitudeAttitude
AdaptableAdaptable
Self
Driven
Self
Driven
DisciplineDiscipline
DependableDependable
CollaborativeCollaborative
What is your attitude – Always positive? Is everyone
extremely excited to work with you? Are you the person
everyone wants you in their team?
Attitude
How often you adapt yourself to the environment? Change
technology? Learn something new and apply?
Adaptable
Does someone has to tell you what you need to do? If
something looks odd, do you take action? (Pick up the
banana peel from the pathway?)
Self Driven
How disciplined are you? How do you manage your time?
How many things are you engaged in? Can you get things
done ontime? Do you always meet expectations?
Discipline
How dependable are you? Can I give you something and
forget all about it? Does is only apply for your superior’s
orders? Do you do this for everyone? By yourself?
Dependable
What is your communication style? Work Alone? Work with
team? Delegate? Influence?
Collaborative
8. Think out-of-the-box
Share the idea
// Move Fast & Break Things //
// Think Different //
// Don’t be Evil //
My Z Score
Score Sheet - Aptitute
Score Sheet - Education
10. Collect or Generate the Data – Machine to Machine, User Generated, System Generated
Ingest the Data – Stream, Batch,Variation with Velocity.. (Scribe, Kinesis, Kafka, Flume)
Store the Data – Structured (SQL/NoSQL, DW, Search/Cache) or Unstructured
(Hadoop/HDFS or QueryLess Storage)
Analyze the Data – Process, Classify, Predict, etc. Batch processing (Hadoop, Spark,
Redshift) or Stream processing (Spark Stream, Storm)
Act on the Data – Execute,Visualize, Simulate..
NB – Need to have a stable and updated Data Set (Specially for Bangladesh)
INGEST STORE ANALYZEGenerate ACT
12. OPEN SOURCE DISTRIBUTION
Ingest Logs Receive Events Train Models Score Models Sync Results Search Data
Big Data
Components
• Kafka
• Storm
• Flume to HBase,
ES
• Cassandra
• Kafka
• Storm
• Flume to ES
• HBase read
• Spark
• Kafka
• HBase write
• Spark
• Elasticsearch
write (index)
• Spark
• HBase read
• Elasticsearch
read (query)
13. Hypervisor/ CMP
BIG DATA SOLUTION COMPONENTS
Advanced Analytics /
ML
Reporting/Dashboard/
Visualization
Analytics
Data Integration
and Processing
SQL / DW
Cluster Manager / Admin
Storage Service
Compute/
Storage HW
No SQL /
Time Series
PivotalPivotal
ECS, Isilon,xTremIO
Open Stack
YARN/
HDFS
Spring XD
Gemfire
Pivotal HDB /
Greenplum
HAWQ
Graphlab
MadLib
HortonworksHortonworks
Super-Micro
DAS
Open stack
YARN/
HDFS
Spark, Flume,
Sqoop, Kafka
HBASE
Hive
Hive, Spark
Zeppelin
Spark ML, Spark R
SAP HANASAP HANA
Cisco/
VMAX/VNX
VMware
AppDirector /
HANA
SAP Data Services
HANA
BW
HANA
Business Objects,
Lumira
PAL
SparkSpark
Super-Micro
DAS
OpenStack/
xStream
YARN
Spark Streaming
HBASE
Spark SQL
Hive
Storm
Zeppelin
Spark ML
SAP HANA VoraSAP HANA Vora
Cisco/SM,
DAS/ VMAX
VMware /
OpenStack
YARN/
HDFS
Spark Streaming,
Sqoop, DS
HANA
Spark SQL/BW
Vora/Spark
Business Objects,
Lumira
PAL, Spark