Fit For Purpose: Preventing a Big Data Letdown

Grab some
coffee and
enjoy the
pre-show
banter
before the
top of the
hour!

The Briefing Room
Down to Business: How to Ensure Success with Big Data

Twitter Tag: #briefr The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh

  Reveal the essential characteristics of enterprise
software, good and bad
  Provide a forum for detailed analysis of today s innovative
technologies
  Give vendors a chance to explain their product to savvy
analysts
  Allow audience members to pose serious questions... and
get answers!
Mission

Topics
October: DATA MANAGEMENT
November: ANALYTICS
December: INNOVATORS

Parallel Universe
Ø  Crossing the
chasm
Ø  Reinvent data
movement
Ø  Recast data
transformation
Ø  Refresh your
team and vision!

Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor

RedPoint Global
RedPoint Global is a data management and integrated
marketing technology company
RedPoint Data Management offers solutions designed for
master data management (MDM), collaboration and
architecture integration
RedPoint’s Hadoop-powered, YARN-compliant application
provides a scalable and cloud-friendly solution for big data

Guest: George Corugedo
George Corugedo is Chief Technology Officer & Co-
Founder at RedPoint Global Inc. A mathematician
and seasoned technology executive, George has
over 20 years of business and technical expertise.
As co-founder and CTO of RedPoint Global, George
is responsible for leading the development of the
RedPoint Convergent Marketing Platform™. A
former math professor, George left academia to
co-found Accenture’s Customer Insight Practice,
which specialized in strategic data utilization,
analytics and customer strategy. Previous positions
include director of client delivery at ClarityBlue,
Inc., a provider of hosted customer intelligence
solutions to enterprise commercial entities, and
COO/CIO of Riscuity, a receivables management
company specializing in the utilization of analytics
to drive collections.

Fit for Purpose: Preventing a Big Data Letdown
October
2015

2 © RedPoint Global Inc. 2015 Confidential
The Gartner Hype Cycle

Overview of the Big Data Journey

Overview of the Big Data Journey
Skills
are
s3ll
a
scarce
resource
because

ecosystem
s3ll
immature

Technologies
being
held
cap3ve
by
the

“coder”
lifestyle

Technology
is
certainly
less
expensive

than
other
EDW
technologies
but
the

total
TOC
is
not
as
compelling
as
a

simple
hardware
to
hardware

comparison

Descent
into
trough
not
a
measure
of

the
technology
but
a
reac3on
to
the

hype

No
amount
of
marke3ng
hype
can

violate
the
laws
of
physics

Looking Deeper into the Reports

Big Data Arriving Faster Than Predicted Because Facilitators Are

Why the Synergies
The
Cloud
makes
data
capture
easy

Simple
to
subscribe
to
PaaS
services

that
manage
Big
Data

Real
3me
event
hubs
make
real
3me

capture
and
u3liza3on
a
subscrip3on

Elas3c
Compu3ng
allows
the

infrastructure
to
expand
and

contract
as
needed

Data
is
available
in
both
batch
and
real

3me
for
analysis

Results
can
be
stored
for
ac3on
or

propagated
across
a
service
bus
for

downstream
consump3on

Plethora
of
unaLended
algorithms
in
the

cloud
to
use
for
discovery
analy3cs

More
data
=
more
machine
learning

More
precise
ac3ons
are
taken

that
generate
addi3onal,

reinforcing
data

Automated
tes3ng
allows
the

machine
learning
to
further

reﬁne
classiﬁca3ons

No
need
for
absolute
truth,
can

learn
from
a
mere
data
stream.

Analy3cs
are
prescrip3ve

rather
than
just
predic3ve
if

deployed
correctly

How the Cloud and PaaS Facilitates Big Data Utilization

What’s the Difference Between Supervised Learning and Machine Learning?
Machine
Learning
with
Op0miza0on

Machine Learning - Deep Learning Neural Net
No
absolute
truth

NN
breakdown
data
at
its
lowest
form
then

learn
to
recombine
it

These
algorithms
are
very
fast
and
distributable

When
used
with
op3miza3on
thousands
or

millions
of
itera3ons
can
be
tested

Used
for
op3mizing
a
metric

This
is
why
strategy
is
so
important;
pick
your

metric
carefully

What Does This All Mean?
Synergy
between
Cloud
Compu3ng
(PaaS),

Machine
Learning
will
accelerate
the
adop3on

and
beneﬁts
of
Big
Data

Time
horizon
for
Big
Data
is
not
5-‐10
years
but

1-‐3
years

Category
leaders
are
already
implemen3ng

business
solu3ons

Mainstream
enterprises
will
start
implemen3ng

next
year

Depending
on
where
your
organiza3on
aspires

to
play,
serious
considera3on
needs
to
be
given

to
these
types
of
combined
solu3ons

immediately.

Strategy
becomes
more
important
than

logis3cs
and
execu3on

So Where are You Going to Land?
Are
you
awash
with
data
and
cant
seem
to
get
it

under
control?

You
know
there
is
value
there
but
you
just
wish

your
business
processes
could
take
advantage
of

the
insights?

Are
you
limited
by
the
avenues
of
execu3on

that
can
be
empowered
by
Big
Data
insights?

Is
your
strategy
ar3culated
suﬃciently
to
point

the
technology
in
the
right
direc3on?

Do
you
lack
the
corporate
will
to
undertake
this

program
of
change?

Can
you
aﬀord
to
wait?

What to do When you Get back to your desk - #1
“Start small—look for low-hanging fruit and trumpet any early success. This will
help recruit grassroots support and reinforce the changes in individual behavior and
the employee buy-in that ultimately determine whether an organization can apply
machine learning effectively. Finally, evaluate the results in the light of clearly
identified criteria for success.”
McKinsey Quarterly – June 2015 | by Dorian Pyle and Cristina San Jose
http://www.mckinsey.com/insights/high_tech_telecoms_internet/an_executives_guide_to_machine_learning?cid=other-eml-ttn-mip-mck-oth-1509

These
resources
make
the

diﬀerence
between
success
and

failure

Keep
project
going
and
don’t
allow

small
challenges
to
prevent

progress

Focus
on
strategy
and
a
well

deﬁned
outcome

Be
rigorous
both
in
process
and

analysis

Interpret
and
trumpet
early

success

Leverage Technologies that are designed to take advantage of Big Data

RedPoint Cloud Implementations - Azure
5
x
small
A-‐SQL
RPI
Databases
(S1,
Scalable)
IIS
Azure
cache
IIS
Azure
load

balancer
Access
and
Endpoint
Control
Scalable
SQL
DW
(Client
Marketing
DB)
DevOps
Support
AD
DC
Or
AD
FS
Campaign
Orchestration
Virtual
Network
Data
Management
Event
Hub
Campaign
Orchestration
Data

Management
Availability
set
Infinitely
Expanding
Data
Lake
Affinity
group
Single
Master,
Multiple
Tenant
Node
Scaled
through
telemetry
and
scripting
Machine

Learning
Session
Data,
Real
Time

Decision
Support

Forrester CCCM Wave Report - Leader
Debut
in
leader
wave

#1
in
Customer
Sa3sfac3on

#1
in
Cross
Channel
Integra3on

#1
in
product
strategy/roadmap

#1
User
Interface

RedPoint Ranked First for Data Quality and Data Integration

Summary for Success
Recognize
this
is
the
3me.

Category
leaders
are
already
there.
Mainstream

enterprises
are
moving
in
next.

The
enabling
technologies
have
come
together

to
make
this
accessible
to
anyone.

Step
back
from
the
logis3cs
and
focus
on

strategy.
Its
how
you
point
the
machine
to
align

to
your
objec3ves.

Start
small,
think
big,
scale
fast
and
trumpet

results.

Find
the
right
resources
that
can
work
across

disciplines.

Be
rigorous.

There
has
been
too
much
hype
in

this
space,
be
the
counter-‐hype.

Don’t
fear
failure;
just
do
it
quickly
and
move
on

Keep Calm and Hadoop On!
www.redpoint.net

contact@redpoint.net

Perceptions & Questions
Analyst:
Robin Bloor

Hadoop: The Agony
and The Ecstacy
Robin Bloor, PhD

Why the “Big Data Hype Cycle” is Misleading
u  Big Data is an ecosystem,
not a technology – which
distorts the picture
u  Some analytics applications
have experienced “absurd
acceleration”
u  Hadoop is, in many
instances, the laggard
u  Nevertheless, Hadoop is
growing like bamboo in
spring

The Necessity
Forbes: Recent academic research found
that companies that have incorporated
data and analytics into their operations
show productivity rates 5 to 6 percent
higher than those of their peers.

What Is a Data Scientist?
u Project manager
u Qualified statistician
u Domain business
expert
u Experienced data
architect
u Software engineer
(It’s a TEAM)

The Corporate Culture Issue
u Some companies have an
established analytics
culture, but most do not
u Establishing one is not a
simple task
u A corporate analytics
structure can disturb the
corporate hierarchy
u Even where one exists,
great technology
opportunities/pitfalls
exist
u Analytics is, or has
become, disruptive

The Technology Issue
u  Technology maturity varies
u  In particular, the Hadoop
stack varies and needs
careful consideration in
respect of components
and distros
u  By comparison the
analytics S/W is mature
u  Streaming architectures
are less mature (lambda
architectures are
relatively new)
u  It all needs to support an
end-to-end business
process

The Reality
The value is only ever delivered by
ACTIONING the analytical discoveries

u  How easy is your technology to implement?
Describe the process for a typical customer.
u  What do you regard as the normal priorities
for establishing an enterprise level analytics
capabilities?
u  Is there an ROI calculation of any kind that
applies?
u  There’s clearly a trend to low latency
analytics. How do you see this developing?

u  How does this relate to the usual BI applications,
or doesn’t it?
u  How much integration work is necessary?
u  Is there any Hadoop distribution that you prefer?
If so, why?

Upcoming Topics
www.insideanalysis.com
October: DATA MANAGEMENT
November: ANALYTICS
December: INNOVATORS

THANK YOU
for your
ATTENTION!
Some images provided courtesy of Wikimedia Commons and http://thespiritscience.net/2014/11/02/
scientists-propose-parallel-universes-really-exist/

Fit For Purpose: Preventing a Big Data Letdown

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