Rajesh Angadi Brochure

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INFORMATION TECHNOLOGY
Rajesh angadi
rajesh_angadi@hotmail.com
Strategic Data Management
The growing need for companies to manage surging volumes of structured and unstructured data is continuing to propel
enterprise use of open-source Apache Hadoop software. But instead of replacing existing technologies, Hadoop appears to
be working alongside conventional relational database management systems (RDBMS).
Hadoop is designed to help
companies manage and
process petabytes of data. The
technology’s appeal lies in its
ability to break up very large
data sets into smaller data
blocks that are then distributed
across a cluster of commodity
hardware for faster processing.
Apache Hadoop is an open-
source software framework
for storage and large scale
processing of data-sets
on clusters of commodity
hardware. Hadoop is an
Apache top-level project being
built and used by a global
community of contributors and
users. It is licensed under the
Apache License 2.0.
Apache Hadoop framework has
following modules:
• Hadoop Common - contains
libraries and utilities needed by
other Hadoop modules
• Hadoop Distributed File
System (HDFS) - a distributed
file-system that stores data
on commodity machines,
providing very high aggregate
bandwidth across the cluster.
• Hadoop YARN - a resource-
management platform
responsible for managing
compute resources in clusters
and using them for scheduling
of users’ applications.
• Hadoop MapReduce - a
programming model for large
scale data processing.
Hadoop’s integral part of
Hadoop File System and
MapReduce, which has been
well designed to handle huge
volumes of data across a
large number of nodes. At a
high level, Hadoop leverages
parallel processing across many
commodity servers to respond
to client applications. The key
difference is, rather than only
looking at parallel computing,
it looks at parallelizing the data
access.
(Above picture shows Hadoop
Ecosystem and cluster details)
This all sounds great, but in
reality Hadoop is designed for
large files, not large quantities
of small files, so if you have
millions of 50 Kb documents,
that is not Hadoop’s sweet
spot.
What Big Data really is ?
While the volumes of data are
growing by leaps and bounds
from many sources, such as
social media, location data,
loyalty information, operations
and supply chain, the type of
information is also an issue.
It may be structured, semi-
structured or unstructured.
Making sense of and gaining
knowledge from this data
to achieve a competitive
advantage should be the
driving goal. So if you have Big
Data and need to search and
sort through the bulk of that
data, then Hadoop may serve
your purpose. If the majority of
the data is structured or even
unstructured but you are able
to add structured meta-data
describing the unstructured
portion and you want to
run standard reports on the
structured portion or retrieve
individual unstructured elements
then standard databases may
suit your needs. If you have
structured, semi-structured, or
unstructured with structured
meta-data, and want to run
complex analyses on the data,
to predict or ask questions
outside of the standard reports,
questions which cannot be
prepared in advance (i.e. the
types of queries most valuable to
real Business Intelligence), then
you probably need a column-
based data store.”In two-thirds
of the cases, companies are using
Hadoop for advanced analytics
and for types of analysis that
they were not doing before”.
The technology is much less
likely to be used for analyzing
conventional structured data
such as transaction data,
customer information and
call records, where traditional
RDBMS tools still appear
to have an edge. Despite
Hadoop’s early promise, the
study said, enterprises that use
it still face challenges related
to issues such as security,
clustering and a shortage of
people with Hadoop skills.
During rearrange of acronym
from EDW to DWE, standing
for “data warehouse
environment,” meaning multi-
platform DW.
From the single-platform EDW
to the multi-platform DWE. A
consequence of the workload-
centric approach is a trend
away from the single-platform
monolith of the enterprise
data warehouse (EDW) toward
a physically distributed data
warehouse environment
(DWE). A modern DWE consists
of multiple platform types,
ranging from the traditional
warehouse (which includes
data marts and ODSs) to new
platforms like DW appliances,
columnar DBMSs, noSQL
databases, MapReduce tools,
Hadoop Ecosystems
and HDFS.
In other words, users’
portfolios of tools for BI/
DW and related disciplines
are diversifying aggressively.
The multi-platform approach
adds more complexity to the
DW environment while BI/DW
environments have always
managed complex technology
stacks successfully. The
upside is that users love the
high performance and solid
information outcomes which
they get from workload-tuned
platforms.
As user organizations
dive deeper into big data
analytics, many users
dependsheavily on SQL-
based ad hoc queries as their
primary method for data
exploration and discovery
analytics (sometimes called
investigative analytics). At
the same time, the same
organizations are adopting or
considering Hadoop as their
primary storage platform for
big data. SQL-based analytics
and Hadoop are good choices
in isolation, but bringing them
together has a catch where
Hadoop’s support for queries
is minimal at the moment.
An emerging best practice,
among Data Warehouse
professionals with Hadoop
experience is to manage
diverse big data in HDFS, but
process it and moves the
results (via ETL or other data
integration media) to RDBMSs
(elsewhere in the Data
Warehouse architecture) that
are more conducive to SQL-
based analytics. HDFS serves as
a massive data staging area. A
similar best practice is to use an
RDBMS as a front-end to HDFS
data; this way, data is moved
via queries (whether ad hoc or
standardized), not via ETL jobs.
HDFS serves as a large, diverse
operational data store.
A straightforward solution is
to use a specialized analytic
database management
system (ADBMS) to query
big data in Hadoop and
elsewhere. This way, you get
the rich features and query
optimization capabilities of a
mature ADBMS, along with the
massive data store of Hadoop.
Also compared to Hadoop, an
ADBMS is far more conducive
to the iterative approach to
query development that most
business analysts and data
scientists demand for true
investigative analytics.
The author Rajesh Angadi
completed his BE, MBA, PMP
and is Hadoop Certified.
With 22 years of Information
Technology experience he
worked on projects for Unisys,
Intel, Satyam, Microsoft,
Ford, Hartford, Compaq,
and Princeton. He is always
fascinated by the latest
technology coming up in the IT
sector and striving to keep pace
with it. Interests in Information
Technologiesresearch areas like
Hadoop Ecosystem, Predictive
Analysis, Telematics, Clinical
research with Analysis.
Hadoop Cluster
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LOGISTICS
If there was an area of the economy primed for the use of big data, it’s the travel industry. Big data … offers the poten-
tial for a vast shift for all travel companies, empowering them to enhance both the business and experience of travel.While
big data is a big, complex challenge for many organizations, it is one of the key factors driving the evolution of the travel
industry today and for the foreseeable future. Airlines, hotels, cruise companies, travel management, railways and travel
agencies have an opportunity to improve their business and the customer experience by effectively handling the big data at
their fingertips. It is not easy to collect, identify and analyze all of the bits of disparate data types that comprise big data.
But ignoring big data—is not an option for any travel and transportation industry which wants to remain competitive.
”At the Big Data Crossroads: turning towards a smarter, efficient and effective travel experience.”
Rapid changes and technology
options are making customers
smart shoppers with a
variety of choices via various
channels – phone, web, kiosk,
counter, 3rd-party agency.
They are also becoming more
demanding in the quality and
variety of services available as
a result of rewards systems or
loyalty programs. For travel &
transportation organizations to
capitalize on these and other
challenges in the industry, they
need ways to collect, manage
and analyze a tremendous
volume, variety and velocity
of data. Organizations who
can tackle the big data
challenges will differentiate
from competitors, gain market
share and increase revenue and
profits with innovative new
services.
Travel and transportation
companies are facing many
of the same challenges and
opportunities as other business
segments in terms of managing
risk, enhancing the customer
experience, and ensuring
operational excellence. The
need to balance cost, product/
service quality/safety, and
customer service is endemic
to all businesses.But for travel
and transportation companies,
it is particularly important
because these businesses are
undergoing a fundamental
shift. For these “service”
businesses, the importance
of quality and customer
satisfaction has never been in
question, but they are seeing a
change from “product-related
services” to “information-
related services.”
Smarter transportation
always results in operational
efficiency, improved end-to-end
customer experiences, reduced
fuel consumption and increased
flexibility. Logistics companies
are already working hard to use
sensor data in trucks to optimize
the routing and decrease fuel
consumption. American Travel
Company US Xpress has installed
1.000 sensors in each truck to
monitor where the trucks drive,
how fast it drives, how often it
breaks, when maintenance is
required and even the capabilities
of the driver. But there are
many more opportunities for
the transportation industry
instead of just saving on fuel.
Sensors in railways,trucks,
buses, taxies, cabs, school buses,
tourist buses, city cabs, ships or
airplanes can also give real-time
information about how these
are performing, how fast it is
going and how long it is standing.
With all this data, combined
with sensors that monitor
the health of the engine and
equipment, errors can be
predicted and maintenance
can be prepared without
losing to much time. It is even
possible to automatically
book maintenance at the
service location that requires
the least downtime for the
transportation companies.
While the engineer instantly
knows what the problem is
and how it can be solved. If
transportation usage has not
been optimized, a company
can lose a lot of money. With
sensor data it will get to know
where all trucks are at any
moment off- time, what their
inventory is as well as their
destination. This information
can help the transportation
company to optimize their fleet
Examples of Data Collection system for Transportation and Travel companies.
Data collection from different sources to separate databases is a challenge.
and increase efficiency.
Data Analytics for
Transportation system.
It is important to know the
exact inventory at all times,
especially if last minute
changes is needed to be
made. When all products
contain sensors which can be
tracked in real time.
Analytic insight can be
beneficial to multiple
functional areas such as
call center, operations,
marketing and sales. Most
important is rapid insight,
enables these parts of the
business to address customer
problems or respond to the
opportunities far more quickly
than previously possible.
The design, development,
and deployment of Big Data
analytical capabilities should
be seen from the outset as an
enterprise wide undertaking
even as nascent initiatives
incubate across different
functions within the business.
The design objectives of an
enterprise wide approach
to Big Data analytics should
include:
o Cross-functional program
governance.
o Alignment of insights
from all sources of Big Data
— consumers, customers,
partners, and suppliers.
o Integrate data to the point
of decision making — leverage
data from wherever it resides.
o Delivering insights within the
decision management context
of the roles they inform —
that is, task aligned and just in
time —and the style in which
and the speed at which the
decisions are made
While the volumes of data are
growing by leaps and bounds
from many sources, such as
social media, location data,
loyalty information, operations
and supply chain, the type of
information is also an issue.
It may be structured, semi-
structured or unstructured.
Making sense of and gaining
knowledge from this data
to achieve a competitive
advantage should be the
driving goal. So if you have Big
Data and need to search and
sort through the bulk of that
data, then Hadoop may serve
your purpose. If the majority of
the data is structured or even
unstructured but you are able
to add structured meta-data
describing the unstructured
portion and you want to
run standard reports on the
structured portion or retrieve
individual unstructured elements
then standard databases may
suit your needs.
The growing need for companies
is to manage surging volumes
of structured and unstructured
data is continuing to propel
enterprise use of open-source
Apache Hadoop software. But
instead of replacing existing
technologies, Hadoop appears
to be working alongside
conventional relational
database management systems
(RDBMS). Hadoop is designed
to help companies manage and
process petabytes of data. The
technology’s appeal lies in its
ability to break up very large
data sets into smaller data
blocks that are then distributed
across a cluster of commodity
hardware for faster processing.
Travel and Transportation
companies must do transition
towards a forward-looking style
of data analysis that generates
new insight and better
answers. This shift in mindset
also implies a new quality of
experimentation, cooperation,
and transparency across the
company. Along with this
Data Analytics for Transportation system.
Big Data Analytics
Transforming Travel & Transportation Industry
Rajesh angadi
rajesh_angadi@hotmail.com

transition, another prerequisite
to becoming an information-
driven business is to establish
specific set of data science
skills which includes mastering
both a wide spectrum of
analytical proceduresand
having a comprehensive
understanding of the business.
Companies must take new
technologicalapproaches
to explore information..
Disruptive paradigms of data
processingsuch as in-memory
databases and eventually
consistent computing models
promise to solve large-scale
dataanalytics problems at an
economically feasible cost.
A transportation company
already owns a lot of
information. Most of their
data must be refined; only
then can it be transformed
into business value. With Big
Dataanalytics, companies can
achieve the attitude, skillset
and technology required to
become a data refinery and
create additional value from
their information assets.
When companies adopt Big
Data as part of their business
strategy, the first question to
surface is usually whattype
of value Big Data will drive?
Will it contribute to the top or
bottom line, or will there be a
non-financial driver?
In this case, data is used
to make better decisions,
to optimize resource
consumption, and to
improveprocess quality
and performance. It’s what
automated data processing
has always provided, but
with an enhanced set of
capabilities. The second
dimensionis customer
experience; typical aims are
to increase customer loyalty,
perform precise customer
segmentation, and optimize
customer service. Including
the vast data resources of
the public Internet, Big Data
propels CRMtechniques to
the next evolutionary stage.
It also enables new business
models to complement
revenue streams from existing
products, and to create
additional revenue from
entirely new (data) products.
Successfully harnessing big
data can help achieve following
critical objectives for travel &
transportation transformation:
Increasing demands of
customers to have their freight
delivered as fast and cheap
as possible, transportation
companies face a challenge
which can be luckily tackled
with big data.
Maximizing availability of
assets, inventory in hand with
infrastructure.
Enhancing services to
increase revenue and manage
capacity.
Dramatically improve
the end-to-end customer
experience.
Reduction on environmental
impact and increase safety.
Operational efficiency with
Real-time route optimization.
Predictive network and
capacity planning
Strategic network planning
Operational capacity planning
Many cities around the world
are experimenting with smart
and intelligent transportation
systems which will eventually
reduce pollution and increase
road safety.Big Data analytics
are essential, helping to
produce an integrated view
of customer interactions and
operational performance to
ensure sender and recipient
satisfaction.
Conclusion is that in
immediate future we all will
be seeing amajor revolution
in this area due to Big Data
Analytics.
Technology experience
he worked on projects
for Unisys, Intel, Satyam,
Microsoft, Ford, Hartford,
Compaq, and Princeton. He
is always fascinated by the
latest technology coming
up in the IT sector and
striving to keep pace with
it. Interests in Information
Technologiesresearch areas
like Hadoop Ecosystem,
Predictive Analysis,
Telematics, Clinical research
with Analysis.Big Data Architechture
ITs Evolution in the
Manufacturing Industry
The manufacturing industry is an integral part of every national economy. Jobs, technology, and
regional trading indexes follow the high and lows of this important industry. Many IT and manufacturing
organizations are paying close attention to this sector, specifically, how manufacturers remain optimistic
about their financial prospects, how they find ways to capitalize on opportunities, or adjust expenses to
compensate for missing projected outcomes. Successes can be found everywhere across
industries, sizes, and regions.
For instance, some companies
lead to product innovation
curve while others are prime
examples of productivity,
sustainability, and quality.
Common to their efforts
has been the use of data
and information systems.
From managing resources
to operational process, and
other business activities,
manufacturers are immersed
in continuous improvement
closed loops that help
companies adapt business
capabilities to top priorities,
sharpen strategy, and stay
competitive.
Our research indicates that
leveraging information
technology to improve the way
they work is closely related
to top performance. In fact,
when we examine what makes
manufacturers breakaway
from their competitors, From
Deming to Lean, Six Sigma,
and Operational Excellence,
and from Business Intelligence
to Big Data, the Best-in-Class
are more likely to use these
technologies to update
business processes and feed
data back into operations as
new best practices emerge.
Manufacturing costs are
controlled and minimized
when there is no latency
between material flow and
information flow. Especially,
in the practice of demand-
based manufacturing, wherein
the vendors that supply
accessories to OEMs are based
on JIT (just-in-time) or in-line
sequencing methodologies, it
is absolutely imperative that a
good information technology
infrastructure is put in place.
The main goal of this IT
infrastructure is to enable
zero latency within and
between business processes
that have stakeholders that
are distributed across the
organization. A good IT
infrastructure in the process
industry is absolutely necessary
for not violating regulatory
or safety norms, to improve
performance and quality via
real-time process monitoring,
and finally, improve reliability
via appropriate maintenance,
driven by up-to-date
information on equipment
status. A good IT information
infrastructure in manufacturing
has five levels.
The lowest level is the ‘control
systems layer’ which directly
controls the equipment used
for producing the product.
At this level, the information
gathered is control data and
the output signal achieved from
the said device/equipment for a
certain input and controller. The
second level is the ‘supervisory
control layer,’ wherein,
‘Supervisory Controllers and
Data Acquisition systems’
(SCADAs), VSAT(Very Small
Aperture Terminal), VCON(
Rajesh Angadi
Vrushal Phadnis
Director, Losma India Pvt. Ltd
www.losma.in
“As a very large amount of
information is generated
during the manufacturing
process of a product;
involving design,
prototyping, its testing
and final production,
modern tools involving
the use of Information
Technology have become
highly important. Making
information available in
real time, and receiving
feedback from and of
a system are vital to
manufacturers, especially
to those involved in product
innovation. This is precisely
where IT contributes
greatly to the sector.
Additionally, the use of IT in
manufacturing has resulted
in reduction of wastage,
positively affecting
company’s bottom lines
and environmental impact.
The development and
implementation of new
information technology
based systems to meet the
goals of a manufacturing
organization will be
essential for the growth
and competitiveness
of manufacturing
organizations in the 21st
century.”
IT IN MANUFACTURING

are installed for exercising
supervisory control and
acquisition of process data and
information.
The third layer is that of the
manufacturing execution
systems – a ‘production
management functional
layer’ encompassing product
life cycle management
(including computer-aided
design), management of all
production operations such
as scheduling of production,
dispatch of production orders,
data collection on production
orders, production reporting
and analysis, tracking materials
and genealogy, etc.
The fourth layer is the ‘plant
to enterprise connection layer’
wherein the business processes
of the plant are connected to
the business processes of the
enterprise. The connection is
established by a business rules
engine that establishes the
link between plant systems
and enterprise systems for
information sharing, analysis
and reporting. The final layer
is the ‘enterprise application
layer’ which has all of the
enterprise level applications
such as ERP, enterprise asset
management, supply chain
management, customer
relations management, etc.
A good infrastructure will
seamlessly tie in one layer with
the other so that all of the
layers are interconnected in
real time.
The technologies that are
used for various applications
include, but are not limited
to, state-of-the-art in Web
2.0, database programming,
service-oriented architecture,
systems and network analysis,
wireless communication,
enterprise mobility, design,
and application of new
technologies in the domain
areas of artificial intelligence,
operations research, global
optimization, theory of
constraints, simulated
annealing, stochastic predictive
modelling and so on. Real-time
and non-real-time operating
systems are used, based on
the mission-criticality of the
operations.
For instance, maintenance is a
function that not only concerns
the maintenance organization
within the company but has
an impact on production (via
the need for availability),
inventory (for spares),
purchasing (for procurement
of spares, tools), finance (for
budgeting, asset replenishment,
replacement, etc.), logistics (for
scheduling, dispatching and
executing maintenance work
orders), corporate executive
management (business
strategy for ensuring lower
manufacturing cost, and/or
increasing reliability in order to
get better return on production
assets) and so on.
This concept of achieving a
unified synthesis of existing
business processes, knowledge,
data, etc., within the company,
which are buried in disparate
applications, divisions and
departments using a zero
latency infrastructure is now
called ‘enabling a real-time
enterprise.’ By using the real-
time enterprise infrastructure,
a manufacturing organization
can assist its management and
labor to maximize performance
efficiency, manufacturing
effectiveness, quality, reliability,
and thereby increase the
competitive advantage leading
to better market share and
better return to shareholders.
Recent news about job gains
or lack and manufacturing
sector growth remind us that
there are numerous ways to
achieve success. Some people
focus on the potential of micro
regions around the world.
Others believe in continuous
improvement cycles that
encourage companies to thrive
in tough conditions and with
fewer resources. Technology is
buzzing with activity describing
how mobility, cloud computing,
and Big Data are shaping
efficiency. Manufacturing
organizations like to connect
success with customer needs.
They tend to focus on innovation
to bring better products and
services to the market. Others
pay attention to strengthening
Supply Chain Management
– which is becoming part of
manufacturing inefficiencies
and risks.
Labor, capital, materials,
and assets represent key
areas for keeping companies
in business in the long run,
so manufacturers continue
positioning resources
management at the top of
their priorities. Opportunities
to succeed are everywhere
and the common denominator
is quality. Most companies
rightfully describe quality as
their competitive advantage.
Manufacturer executives
tend to use quality to manage
intangible assets such as
brand and reputation. These
arguments imply that quality
is not fixed in time or assigned
to a product, a supplier, or a
single company initiative. It
represents how the dynamic
force of quality influences all
operational tactics, enables
learning new things; accelerate
opportunities, or how
companies bounce back from
failure.
Quality principles explain that
growth doesn’t depend on
a best practice; it is how you
adapt and respond to the
specific situation. Good quality
as a common denominator
makes company leaders aware
of change including new risky
situations, new customer
needs, new competitors,
and new priorities as well
as the chances for success
measured in terms of customer
satisfaction.
Review finds that margin
growth (60%) and organic
revenue growth (43%)
are the top two goals for
manufacturers in 2014.
But with growth comes
growing pains. No longer can
manufacturers only be content
with maintaining standards
organization-wide, while being
Shaker Tekwani
CEO, Spectrum Cable-Tech
www.spectrumcables.com
IT in manufacturing is
the most vital tool in
21st century. It enable
manufacturers to virtually
proto type plant layouts
, optimize raw materials
usage and asses relevant
factors prior to investing
in plant redesigns or new
factories.
IT help manufacturers
to enable the transfer,
storing and processing
of data, knowledge and
information while at the
same time controlling the
hardware machinery and
also facilitates mailing
services, telephone and
FAX Networks.
The Internet traffic is
increasing day by day and
manufacturing companies
have got the opportunity of
Globalization.
IT has connected the
world in its web of
opportunities and has
made manufacturers’ life
more exciting and easier
like never before.
more efficient in managing
front and back-office processes.
Today’s manufacturers are also
required to be increasingly
innovative and more agile in
decision-making to stay ahead
of competitors, and provide
greater value to customers. In
many cases, it is no longer safe
to provide the same products
to the same customers
without change. Therefore,
manufacturers must enable
collaboration and access to
greater amounts of data. It has
been found that Enterprise
Resource Planning should
enable manufacturers to
accomplish these goals.
There is no argument against
the power of data as fuel for
organizational transformation.
Manufacturing companies
want operational visibility
to anticipate risks and close
performance gaps. They also
want a greater understanding
of users to direct innovation
efforts, operational incentives
to increase competitive
capabilities, and opportunities
for partnerships to grow.
Data is of paramount
importance to support those
and other decisions that help
manufacturers gain quality,
improvements, and efficiencies.
What a difference a decade
makes. Fresh from their MBA
school degrees and “measure
to improve” as motto,
manufacturing executives
from the early 1990s used
spreadsheets to manage by
numbers.
Later on, Business Intelligence
tools were used to analyze
metrics, define controls,
and create performance
dashboards. The internet
amplified the data explosion.
Social media expanded the
data structures by adding
pictures, text messages, likes,
preferences, and behaviors
to define personas for users
of internet technology. Cloud
computing has also expanded
data storage, processing, and
sharing options. More recently,
advances in mobility allow for
quick access to information.
The difference is that
manufacturing organizations
understand that everyone
can benefit from using data
to support decisions that help
improve the way they work.
Today’s game changing insight
is data in the right form at
the right time and in the right
hands. This requires that
manufacturing organizations
must reach for opportunities
to bring manufacturing data to
the forefront. Users need help
to make sense of data in order
to prepare plans, execute, and
measure effectiveness. Data,
facts, and insight complement
intellect and expertise which
increases the decision-making
ability at all levels of the
organization. When managed
effectively, manufacturing
data can help companies grow
and support the vision for
a knowledgeable and more
effective organization.
When the strategy is to
improve, tactics turns into
data trails and information
management. Primarily,
we want to collect the
thoughts and insights of the
manufacturing community
about Big Data as a driver
for improvements, but also
to understand opportunities
that are materializing in this
sector. During the study we
will connect with users via
a questionnaire and 1-on-
1 interviews. Additionally,
we look at how users adapt
technology solutions to fit
their needs. Another reason
for participating is personal
development. People want
to know how to use data
effectively. Conversations
quickly evolve to their vision
on how Big Data can help them
improve.
For example, some people
expect Big Data to create a
bigger and better picture.
U Rajagopalan
CEO, Toshikcon ABS
www.toshikcon.com
“For any industry, there is
a need for IT solutions –
to have better planning/
monitoring tools; to
perform repeated jobs/
operation in easy way; for
simple & cost-effective
communication; etc. In
particular, IT solutions
improve the productivity
in industries, in various
levels. This provides
instantaneous operation
status update; warning
signals through SMS etc.
to operators. Current
process trends; comparison
with previous/ standard
one; pre-warning signals;
breakdown alerts; spares
management, supervisory
controls; data acquisition
& backup to supervisors
/ mid level management;
and MIS reports &
web updates on sales,
production, performance
etc. to top management.”
Others want access to more
and different knowledge.
There is increasing interest
in behaviors, events, and
actions. Culture is always
factor. Solutions – Business
intelligence tools, analytics
embedded in enterprise
systems, or homegrown
solutions can help transform
data into knowledge and value.
Users have plenty of options:
Enterprise solutions from SAP
and production software from
Apriso can be architected to
take control of operational
data across all facilities, and
all countries. Preventive,
defensive, and triage activities
can be supported by a
combination of Microsoft and
Honeywell solutions for both
plant and enterprise users.
Visualization and analysis from
Product based companies can
help manufacturers identify
what they don’t know about
warranty or asset reliability.
Applications to manage specific
quality tests from Viewpoint
Systems can connect R&D and
system engineers with the data
that they need to characterize
product degradation.
Manufacturers are in the
interesting position of being
in control of massive amounts
of data and being driven to do
their job better. Perhaps you’re
already familiar with how Big
Data is helping companies map
customers and opportunities,
customize products, or
increase user experience.
While this is important, we
want to also look into how
manufacturing intelligence
(MI) can be used to arm
ordinary people with facts and
operational knowledge, so they
can take extraordinary actions
that lead to productivity gains.
Rajesh Angadi completed his
BE, MBA, PMP and is Hadoop
Certified. With 22 years of
Information Technology
experience he worked on
projects for Unisys, Intel,
Satyam, Microsoft, Ford,
Hartford, Compaq, and
Princeton. He is always

must be made as towhether a
particular bit of data deserves
to be captured, and whether it
has relevancewhen combined
with other data. For example,
the face of a known criminal
amongthousands of images
might trigger a “stop”; a
pattern of credit fraud might set
offwarnings; and indications of
growing customer churn might
inspire a coupon offer.Anytime
there is an important anomaly in
the data, it needs to be pointed
out beforethe data is stored
so that real-time action can
be taken. By its very nature,
network traffic is Big Data. In
just one slice of the network—
the mobilenetwork—there are
6 billion mobile subscriptions
in the world, and every day, 10
billion textmessages are sent.
Making Big Data Business-
2) The sheer volume of
Big Data overwhelms the
normal data warehouse. For
example,Facebook reports
that its users register 2.7 billion
likes and comments per day.
For many, this magnitude of
data is intimidating: they can’t
keep up with it, much less sort
it,analyze it, and extract value
from it.
SMEs Empowerment through Big Data Analytics
Big Data comes with big challenges. Today, as data transcends into the ever-expanding
realm of Big Data, a growing community of expertsis beginning to agree. They see in
Big Data the same transformative, wealth-creating power. The problem today is that the
ever-increasingdeluge of information—terabytes to petabytes to Exabytes—threatens to
swamp us in a gusher of unfiltered, unstructured, unprocessed, and seemingly unmanageable
information. As things stand now, the data ecosystem is highly fragmented. Between those
who create data and those who could potentially extract value from it sits a labyrinth
fraught withcomplexity, disparity, and miscommunication.If analytics are to be the new
“refinery,” some of that fragmentation will need to be addressed with greater connectivity,
trust, and efficiency.
3) All of that data can be
challenging to manage when
flooding in at a velocity
that, formany players, far
outpaces their processing
ability. In order for Big Data
to be a game changer, it
needs to be analyzed at a rate
that matches the blistering
speed at whichinformation
enters data warehouses.
In microseconds, decisions
Rajesh Angadi
1Anniversary
True Nature of Big Data, in
seeking better-informed
decision making, many
organizations are running data
warehouses and employing
traditional data analytics—
to reduce churn, bolster
campaigneffectiveness, and
counter fraud, to name a
few applications. Big Data
represents arevolutionary step
forward from traditional data
analysis, characterized by its
three mainelements: variety,
volume, and velocity.
1) The variety of data comes
in two flavors: structured
and unstructured. Structured
dataenters a data warehouse
already tagged and is easily
sorted. The vast majority
of today’s data, however,
is unstructured, and fed by
sources such as Facebook,
Twitter,and video content. It’s
random, difficult to analyze,
and enormous.
Rajesh Angadi completed
his BE, MBA, PMP and is
Hadoop Certified. With
22 years of Information
Technology experience
he worked on projects
for Unisys, Intel, Satyam,
Microsoft, Ford, Hartford,
Compaq, and Princeton. He
is always fascinated by the
latest technology coming
up in the IT sector and
striving to keep pace with
it. Interests in Information
Technologiesresearch areas
like Hadoop Ecosystem,
Predictive Analysis,
Telematics, Clinical research
with Analysis.
Friendly
Big Data can also change how
we interact with businesses.
Moment to moment,
consumers’interactions with
the world around them create
an often-ignored by-product:
massiveamounts of personal
data. These include searches
on weather, price comparisons,
purchases, and thousands of
other daily choices and actions.
By analyzing the datagenerated
by all of this activity, Big Data
offers an opportunity to
revolutionize the wayconsumers
and sellers interact.
Some businesses are already
moving to take action. Here are
a few examples:
1) Wal-Mart’s inventory
management implemented
radio frequency identification
(RFID)technology to connect
real-time information between
suppliers and its Retail Link
datawarehouse. In the process,
it reduced out-of-stocks by an
estimated 16 percent.
2) FedEx achieved real-time
visibility with shipping and
consumer data across more
than46,000 distribution and
supply chain locations.
Integration of its clinical and
cost data led to the discovery
of Vioxx’s adverse effectsand
subsequent withdrawal of
the drug from the market.
Consequently, Big Data has
already become top of mind
among CIOs.
1. Revenue Assurance.
Integrating intra-company
data could hone identification
andprevent fraud before
it occurs. Fraud-intensive
industries such as healthcare
would particularly benefit.
2. Risk Mitigation. Every day,
networks carry petabytes
of critical information for
enterprises, governments, and
consumers, opening an ever-
increasing risk ofintrusions and
security attacks. Data federation
across wider geographic and
networkfootprints would enable
identification of suspicious
patterns while signaling the
needfor immediate action.
3. Customer Lifecycle. Businesses
can zero in on instances of
customer frustrationand offer
an immediate response, thereby
improving the consumer
experience and lessening churn.
Any service-based industry
that values its customer
relationshipswould benefit.
4. Market Execution. Big Data
enables better market services
through analytics, creating
improved opportunities for
cross-selling and up-selling.
Banking and Internet commerce
stand out as potential
beneficiaries.
5. Product Innovation. Consumer
input is critical in product
development, and today many
companies are already clamoring
to know more about the likes
and dislikes oftheir customers.
Integrating noncompany
sources of data such as social-
networkfeeds would provide
a more holistic view of how
consumers feel about a product,
potentially revealing the need
for a new product before it is
imagined or on thedrawing
board.
SMEs are beginning to see big
data as something more than
just an enterprise trend. Some
are starting to realize that they
can identify trends, patterns
and gain competitive advantage
by harnessing the power of
growing data volumes. But he
cautions that before rushing to
implement a big data solution,
small businesses need to take
a step back and remember that
bigger is not necessarily better.
SMEs need to keep a sharp eye
on cost and execution and take
stock of their needs before
Big
Data
Campaign
Analysis
Product
Performance
Customer
Segmentation
Financial
Risk Analysis
Sentiment
Analytics
Predictive
Analytics
Social Media
Analytics
Fraud
Detection
Customer
Relationship
Managment
COVER STORY | EMPOWERING SMEs IN INDIA

establishing a data strategy.
Smaller data sets from CRM
platforms, social media or email
marketing programmes can still
provide much-needed insight
to help businesses understand
customer behavior patterns and
showcase trends. The key is to
find the appropriate vehicle to
visualize and present this data
in a way that reveals overlooked
opportunities and actionable
insights. This is what could
make big data, assuming you
can still call it big data, viable
to deploy for SME businesses.
Instead of a bank-breaking big
data solution, small businesses
should focus their efforts and
dive deep into a few business-
critical sets of data – such as
sales in a specific sector, or
performance metrics during
peak versus low seasons. This
strategy will provide quicker and
better results than companies
that try to take on too much.
Little data can yield big results
for many departments of small
businesses, for everyone from
the sales department to the
executive director.
SMEs should be looking at big
data. Interest in big data has
reached new heights for many
SMEs as they attempt to capture
information and glean insights
from ongoing conversations on
social channels and the ‘digital
dust’ consumers leave when
browsing the web, shopping
online, listening to music in the
cloud and using smartphone
applications. Like many other
businesses, SMEs need to glean
a better understanding of often
volatile consumer behavior to
know what they want before
consumers do themselves.
The problem is that many SMEs
lack big data expertise, are the
technologies to achieve these
goals are available and more
affordable than most small
businesses expect, especially
when factoring in the losses
from a lack of investment in a
world where competitors are.
What’s required are the smarts
– knowing which questions to
ask of the data and how the
organization can best use what
it finds.
SMEs that have decided against
big data projects or are still
hesitant imagine the major
inhibitors to be not enough
staff with expertise and the
expected cost of big data
initiatives.Nevertheless, business
managers need to grasp the
nettle.
We generally don’t need
mountains of data to gain
insight from it: we simply need
to be asking the right questions,
and smaller companies are
just as capable of asking
intelligent questions as bigger
companies. Whether it be for
big data or little data. Many
SMEs are already running big
data technology within their
enterprise without even thinking
about it as such, with MongoDB
or another NoSQL database.
MongoDB is already running
in many businesses because
of its ease of use and dynamic
schema, so SMEs that want
to get started with a big data
project need not invest heavily
in learning new technologies.
Even Hadoop, which is complex,
is likely to have its complexity
hidden in the near future with
applications that SMEs will use,
pointing to companies such as
Datameer, MetaMarkets and
Infochimps.
Big data advisors
Channel partners can play a
significant role in the promotion
and implementation of big data
technologies.By integrating a
vendor’s solution with other
sector-specific tools, the channel
can cater to the individual
needs of an SME customer
and eliminate the need for
multiple expensive solutions.
The channel plays an important
role in being able to aggregate
and manage data from many
different sources, coming from
a combination of cloud and
on-premise applications. Their
knowledge of a vertical or
market segment will provide a
huge value-add for customers.
SMEs can make great use of
big data if it includes drilling
down to the machine data layer:
Monitoring and understanding
machine data can enable
companies to identify and
resolve IT and security issues
with an accuracy and immediacy
not previously possible.There’s
a really key role that the channel
can play in helping companies
of all sizes, SMEs included,
to understand that using
big data – particularly at the
machine data level – can have a
profound effect on operational
intelligence.Massively improved
IT systems management, rapid
response to security threats
and streamlined compliance
processes that channel partners
can leverage with SMEs as
easyto- understand, quick-win
scenarios.Channel partners can
play a huge role in delivering
big data to the SME market, as
they can offer bespoke vertical
solutions – such as retail, Telco
or utilities-specific know-
how or applications.Further,
channel partners interact with
customers on a daily basis and
understand their requirements
better than vendors. They can
also add training and services to
differentiate their offering. Most
partners are missing a trick with
the SME market at the moment.
For now, most channel partners
seem more tuned to enterprise
needs, which can assume more
control of big data technologies
such as Hadoop because of their
inherently larger staff. But this
overlooks a huge opportunity in
the SME market, where the bulk
of the world’s companies are,
and where most of the world’s
data is too. The channel partners
which can tap this market will
win big in big data.
By giving SMEs access to
financing that brings these
technologies within their reach
in an affordable way, big data/
analytics capability is making
its way to smaller companies
quickly. It’s a trend that’s
only going to grow for small
businesses. If those SMEs
collaborate with a channel
partner, they can take advantage
of some of the most effective
methods to gain necessary data
insight, while gaining a deep
level of industry expertise.
LEADER SPEAK
“In General, the trend for Small
& Medium Enterprise is very
encouraging in India, but being
competitive in the changing
scenario is the prime factor to get
to the top. They should always
try to reduce manufacturing costs
along with quality systems, which
can help them in terms of efficient
pricing & quality product.
Also, proper marketing planning
will give an edge to the small &
medium enterprises since they got
limited marketing budget, wants
maximum returns out of it and
also they may not have marketing
expertise. Marketing Plan should
always be linked with business goals
& objectives.
Expertise, Innovation & Networking
will fix the success of the SME in
this competitive market.”
Jyoti MIshra
Executive, Nord India
“Today supply chains are big, complex and global. Keeping them humming is an enormous challenge.
We think the world is entering the era of small, simple and local supply chains, powered by a new
generation of manufacturing technologies such as 3D printing, intelligent assembly robotics and
open-source hardware – also known as the Software Defined Supply Chain”.
Rapid Prototyping and Additive Manufacturing
with Big Data Analytics
Prototyping is used to
evaluate and test the
design, ergonomics, safety,
functionality and other aspects
of a device. Choosing the right
prototyping process can enable
companies to notice design
errors and other issues that
could later cause significant
problems, thereby saving both
money and time.
Conventional prototyping
methods such as machining,
injection moulding and soft
tooling can provide high-quality
and highly accurate prototypes.
However, these processes can
be time-consuming, expensive
and complicated.
Rapid Prototyping
Rapid Prototyping for Direct
Digital Manufacturing deals
with various aspects of joining
materials to form parts. Additive
Manufacturing (AM) is an
automated technique for direct
conversion of 3D CAD data into
physical objects using a variety of
approaches. Manufacturers have
been using these technologies
in order to reduce development
cycle times and get their
products to the market quicker,
more cost effectively, and
with added value due to the
incorporation of customizable
features. Realizing the
potential of Additive
Manufacturing applications,
a large number of processes
have been developed allowing
the use of various materials
ranging from plastics to metals
for product development.
Rapid prototyping methods
have dramatically changed
the landscape, allowing for
a prototype to be made in
as little as one day.In some
circumstances, the same
methods can be used to make
the final product, further
speeding up the production
process by means of additive
manufacturing.
Rapid prototyping methods
The most common types of
rapid prototyping methods
are additive technologies,
meaning that the model is built
by adding material layer by
layer. By contrast, subtractive
prototyping methods create a
and drilling.A rapid prototype
originates with a computer
model. Typically, this model
is fabricated using computer-
aided design (CAD). In some
cases, where the final product
will be custom-made. A
prototyping machine reads the
computer data and slices it into
different layers. The machine
then builds the prototype by
adding material layer by layer.
Additive Manufacturing
enables the fast, flexible and
cost-efficient production of
parts directly from 3D CAD
data – a technology that helps
you to perform your tasks in an
innovative way.
Additive Manufacturing refers
to a process by which digital 3D
design data is used to build up
a component in layers by
LOGISTICS
Rajesh Angadi
Differences between Traditional and Additive Manufacturing
Billet Machining Part Scrap
Traditional
Foil/Powder
AM Part Scrap
Motor Vehicles
Consumer Products
Business Machines
Medical
Academic
Government/Military
Others

components
•To Introduce systematic
solutions for process selection
and design for AM
The technology has especially
been applied in conjunction
with Rapid Prototyping - the
construction of illustrative
and functional prototypes.
Additive Manufacturing is
now being used increasingly
in Series Production. It gives
for most varied sectors of
industry the opportunity to
create a distinctive profile for
themselves based on new
customer benefits, cost-saving
potential and the ability to
meet sustainability goals.
Benefits
The strengths of Additive
Manufacturing lie in those
After the item that will
be printed is selected
it is scanned into 3d
cad software
The wench is
manipulated by
selecting individual
parts in software
Customize parts by
selecting colours.
Then press print
The printer injects
ink and binder into
powdery composite
meterial in thin layers
Retrive the wrench
from the composite trey
Remove excess the
composite meterial
from the wrench
Cure the wrench
if neccessary
Now compare the
original wrench with
the fabricated wrench
STEP 1 STEP 2 STEP 3 STEP 4 STEP 5 STEP 6 STEP 7 STEP 8
ADDITIVE MANUFACTURING
Bed-Based Meterial Process
A PROCESS OF JOINING
METRIALSTO MAKE OBJECTS FROM
3DMODEL DATA, USUALLY LAYER
UPON LAYER, AS OPPOSED TO
SUBTRACTIVE MANUFACTURING
METHODOLOGIES
ADDITIVE MANUFACTURING:
ADDITIVE MANUFACTURING INCLUDES
THE FOLLOWING PROCESSES
BED BASED
METERIAL
LAMINATION
FEDER
BASED
METERIAL
EXTRUSION
depositing material. The term
“3D printing” is increasingly
used as a synonym for Additive
Manufacturing. However, the
latter is more accurate in that
it describes a professional
production technique which
is clearly distinguished from
conventional methods of
material removal.
Instead of milling a workpiece
from solid block, for example,
Additive Manufacturing builds
up components layer by
layer using materials which
are available in fine powder
form. A range of different
metals, plastics and composite
materials may be used.
includes
•Provides a comprehensive
overview of AM technologies
plus descriptions of support
technologies like software
systems and post-processing
approaches
•To discuss the wide variety of
new and emerging applications
like micro-scale AM, medical
applications, direct write
electronics and Direct Digital
Manufacturing of end-use
On Demand Services
(Service Bureaus)
LOWEST BARRIER TO PARTS
- Distributed globally
- 3-10 day turn around avg
- Alomost all technologies
3D Printers
MOST AFFORDABLE
SOLUTION
- $10 -$50K USD
-Simple and easy to use
- Optimized for form, fit
some function
3D Production
Systems
HIGHEST PERFORMANCE
SYSTEMS
- $50 - $500K & up USD
- Optimized for performance
- Broad application
Solution Classification
Addictive Manufacturing
Highest cost
per unit
Lowest cost
per unit
Fewer units
Cost per
Costperunitmanufactured
areas where conventional
manufacturing reaches its
limitations. The technology
is of interest where a new
approach to design and
manufacturing is required so
as to come up with solutions.
It enables a design-driven
manufacturing process - where
design determines production
and not the other way around.
Additive Manufacturing allows
for highly complex structures
which can still be extremely
light and stable. It provides
a high degree of design
freedom, the optimization
and integration of functional
features, the manufacture of
small batch sizes at reasonable
unit costs and a high degree of
product customization even in
serial production.
- Binder jetting: a liquid bonding agent
is selectively deposited to join powder
materials.
- Powder Bed Fusion: Thermal energy
selectively fumes regions of a power
bed.
- Vat Photo polymerization: Liquid
photo polymer in a VAT is selectively
cured by light activated polymerization.
Short lamination sheets of material
are bonded to form an object.
– Directed Energy Depositions:
Focused thermal energy is used
to fuse materials by melting as
the materials are being
deposited.
– Material setting droplets of
build materials are selectively
deposited.
– Material Extrusion:
Material is selectively
dispensed through a nozzle
or orifice.
to get better availability and
better throughput, or you can
go further to the optimization
side. Big data can also be
used to compare one plant to
another on the equipment-to-
equipment level.
If you have 50 to 200 plants,
you want to see the best
practices across all of the
plants. You can see what
aspects of the equipment
breaks down the most. The big
data gives you temperature,
pressure, and maintenance
records. You can see shop
floor notes that offer insight
into why some equipment
is breaking. When you’re
producing a lot of data, you
can use that data to get your
efficiency up and your defects
down.
Big data, along with complex
analytics that can help to
better monitor and analyze
parts during the laser sintering
process. Perhaps most
importantly, the technology
will be able to monitor any
temperature abnormalities
that could structurally impact,
compromise the nozzle during
Additive Manufacturing.
Companies estimated that the
big-data-enabled “in-process”
inspection could increase
production speeds by 25
percent, while cutting down on
inspection after the building
process is complete by that
same 25 percent.
Technology experience he
worked on projects for Unisys,
Intel, Satyam, Microsoft,
Ford, Hartford, Compaq,
and Princeton. He is always
Functional Principle
The system starts by applying
a thin layer of the powder
material to the building
platform. A powerful laser
beam then fuses the powder
at exactly the points defined
by the computer-generated
component design data. The
platform is then lowered
and another layer of powder
is applied. Once again the
material is fused so as to
bond with the layer below
at the predefined points.
Depending on the material
used, components can
be manufactured using
stereolithography, laser
sintering or 3D printing.
Big Data Analytics
Analytics and prognostics in
plants used to be the terrain of
large, leading-edge companies.
The ability to crunch plant data
to predict failures, optimize
throughput, and determine
best-practices that a company
that can save a fortune by
making a plant half a percent
more efficient.
The number of benefits that
come from using big data
to analyze and optimize
plant operations. Analytics
can coordinate operations
with reliable, timely, and
contextualized information.
You can reduce operating costs
with real-time monitoring and
data analysis. You can improve
enterprise connectivity
with anywhere, anytime
information, and you can
enhance decision-making for
improved performance with
integrated history, alarming,
and trending. Those using
analytics to improve plant
efficiency are going deeper into
the use of data. Where they
used to use analytics to tweak
equipment for improvements,
they’re not using data to truly
optimize their systems.
You can monitor the equipment

www.martonline.in16 March 2014
* Expert in providing offshore IT services management (outsourcing) such as
databases, server infrastructure management and change initiative planning.
* Expertise in data warehousing set-up and solutioning.
* Provide technical expertise and leadership to facilitate the implementation of
enterprise-wide shared infrastructure services and business process management
(BPM).
* Direct or indirect involvement in development of policies, standards and
guidelines that direct the use of Infrastructure Services within the enterprise.
* Certified in Hadoop Ecosystem (HDFS, HBase, Hive, Pig, Sqoop and MySQL)
Administration
* Experience in implementations involving Hadoop clusters, Hive, Sqoop, SENTRY
and MySQL with Amazon EC2 environments as well as RHEL 6.4 environments
using Cloudera CDH3/4.
Contact :
RAJESH ANGADI
B-405, Aisshwarya Opulence, Outer Ring Road Bangalore
rajesh_angadi@hotmail.com/+91-9591800293/93434518802
For Reading Mart visit :
www.martonline.in

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