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More from Swiss Big Data User Group (20)
Unleash the power of Big Data in your existing Data Warehouse
- 1. WHT/082311
Unleash
the
power
of
Big
Data
in
your
legacy
Data
Warehouse
Harro
M.
Wiersma
M.Sc.
Big
Data
Guy
- 2. WHT/082311
§ Harro M. Wiersma
§ born 1976 in Groningen, the Netherlands
§ Master of Science – University of Phoenix (AZ)
Computer Information Systems
§ past: contractor (DBA / Project Management / Team Management)
§ Manager Database IKEA / Technical Lead Infrastructure Engineering Sunrise /
§ Department Head Service Engineering Opitz Consulting CH
§ Head of IT Data Warehouse at PostFinance
§ current: Big Data Guy – looking for nice challenges
§ main
focus
area‘s:
Telecom,
Finance
and
Retail.
§ hobby‘s:
golf,
whisky,
freelance
sound
engineer
and
tv
producer.
§ contact: h@rro.wiersma.info
WHO
AM
I
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 3. WHT/082311
MAIN
PROBLEM
–
A
CLEAR
VIEW
how can we prevent to get different results
from different systems
about
the same KPI’s?
how
can
we
use
our
own
data
to
support
our
opera+onal
processes?
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 4. WHT/082311
KEEP
A
STRAIGHT
FOCUS
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 5. WHT/082311
BIG
DATA
OR
RIGHT
DATA
I‘m
not
interested
in
technology.
I‘m
not
interested
in
data.
I
am
interested
in
translaRng
data
into
informaRon
for
decision
making.
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 6. WHT/082311
MORE
DATA,
WAY
MORE
DATA
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 8. WHT/082311
TRACK
EMOTIONS
...
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
www.realeyesit.com
- 9. WHT/082311
TRACK
MOVEMENTS
...
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
www.retailnext.net
- 10. WHT/082311
CURRENT
DWH
CHALLENGES
§ load-to-report, very unflexibile
§ longer nightly loads – is the night still long enough?
§ does the project-requester still now why (s)he needed the data when
finally delivered, or has an alternative solution been created in the
meanwhilea?
§ several different „sources-of-truth“ ...
§ how can we process these vast amounts of data?
§ how to implement new sources of untraditional data?
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 11. WHT/082311
BIG
DATA
CHALLENGES
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 12. WHT/082311
bDWH
–
BRINGING
BUSINESS
AND
IT
STRATEGIES
TOGETHER
Leveraging
untradiRonal
sources,
social
media
and
transacRonal
data
to
gain
the
elusive
360
degree
view
of
the
customer
and
your
business.
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 16. WHT/082311
…
AND
BRING
BIG
DATA
INTO
THE
WAREHOUSE
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 17. WHT/082311
THE
POWER
OF
BIG
DATA
–
THE
bDWH
CONCEPT
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 18. WHT/082311
§ IT
does
knows
data
and
infrastructure
(only)
§ Business
knows
the
intelligence
to
be
applied
to
the
data
to
derive
value
§ Business
knows
how
to
discover
data
pa;erns
(manual
and
automated)
–
Data
ScienRsts
§ Business
understands
their
seman+cs
beVer
§ Business
can
perform
data
interroga+on
in
an
experiment
and
associate
rules
of
engagement
early
on
for
data
usefulness
§ IT
can
create
reusable
reports
of
these
experimental
results.
§ Business
can
siX
the
data
to
curate
the
context
§ Big
Data
needs
to
be
curated
to
be
useful
The
bDWH
concept
brings
Business
and
IT
together
to
create
added
value
IN
WHAT
DOES
THE
bDWH
CONCEPT
DIFFER
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 19. WHT/082311
THE
bDWH
PARADIGM
CHANGE
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 20. WHT/082311
THE
COMPLETE
bDWH
VALUE
CHAIN
20
Collec+on
–
Structured,
unstructured
and
semi-‐structured
data
from
mulRple
sources
Inges+on
–
loading
vast
amounts
of
data
onto
a
single
data
hub
Discovery
&
Cleansing
–
understanding
format
and
content;
clean
up
and
forma[ng
Integra+on
–
linking,
enRty
extracRon,
enRty
resoluRon,
indexing
and
data
fusion
Analysis
–
Intelligence,
staRsRcs,
predicRve
and
text
analyRcs,
machine
learning
Delivery
–
querying,
visualizaRon,
real
Rme
delivery
on
enterprise-‐class
availability
Collec+on
Inges+on
Discovery
&
Cleansing
Integra+on
Analysis
Delivery
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 21. WHT/082311
KEY
SUCCES
FACTORS
§ Business
needs
to
drive
and
execute
the
bDWH
program
§ Data
colloca+on
and
discovery
is
the
most
cri+cal
step
§ Metadata
is
needed
to
process
the
data
prior
and
post
bDWH
integraRon
§ Data
quality
can
be
processed
by
integraRng
taxonomies
§ Data
visualiza+on
is
needed
to
discover
data
§ Metrics
and
metadata
will
be
the
bridge
to
integrate
to
the
bDWH
§ Centralized
infrastructure
is
needed
to
create
a
data-‐hub
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 22. WHT/082311
§ Bring
together
exis+ng
internal
knowhow,
combine
it
with
external
knowhow.
don‘t
silo
your
teams.
§ It‘s
not
about
hardware,
it‘s
about
the
concept
and
way
of
thinking.
§ Reusable
data,
but
which
data
is
the
‚sole
truth‘?
§ Who
owns
your
data?
do
they
really
want
to
have
transparency?
§ Are
we
allowed
to
use
our
data
as
we
would
like
to?
§ Think
of
new
and
future
business-‐concepts
to
be
supported.
FIRST
STEPS
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 23. WHT/082311
The
challenge
facing
the
business
today
is
the
ability
to
influence
the
buyer
decisions
in
a
window
of
opportunity
that
does
not
last
long.
The
analyRcs
available
at
a
personalizaRon
level
drives
the
buyer
whether
it
is
choosing
a
Doctor
or
buying
a
new
laptop.
To
compete
in
this
new
era,
businesses
need
to
be
driven
by
data
and
analyRcs,
which
are
largely
different
from
tradiRonal
transacRons
and
campaigns!
Both
the
“GeneraRon
Z”
and
“Millennial
GeneraRon”
of
buyers
will
not
be
swayed
by
tradiRonal
engagement
models
of
selling
products
and
services!
FROM
TRANSACTIONAL
TO
BEHAVIOURAL
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 24. WHT/082311
PREDICTIVE
BUSINESS
INTELLIGENCE
–
DATA
ANALYSIS
§ you
know
what
you
know
–
perfect,
use
it!
§ you
know
what
you
don‘t
know
–
learn
§ you
don‘t
know
what
you
know
–
invesRgate
§ you
don‘t
know
what
you
don‘t
know
–
find
someone
who
does!
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 25. WHT/082311
§ Do
not
try
to
implement
without
integraRon
in
your
current
landscape
§ Find
a
easy
target,
for
example
your
data-‐archive
§ Collabora+on
is
key!
Learn
from
other
industries
§ Create
cross-‐func+onal
teams:
IT
–
Analysts
–
Business
§ Champion
business
value:
a
ROI
is
there!
§ OrganizaRons
that
don’t
leverage
the
big
data
that
they
have,
risk
losing
ground
to
their
compeRtors
§ Get
on
it,
now!
TAKE
AWAYS
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 26. WHT/082311
This is the moment…
Are you ready?
Big
Data
is
a
Game
Changer
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 27. WHT/082311
QUESTIONS
&
ANSWERS
Harro M. Wiersma M.Sc.
h@rro.wiersma.info
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich
- 28. WHT/082311
REFERENCE
CASE
I
-‐
FINANCE
§ no
fixed
card-‐limit
§ acRve
transacRon
monitoring
based
on:
§ customer
profile
§ credit
raRng
firms
(4!
in
the
USA)
§ acRve
balance
§ payment
history
§ result:
lower
security:
payment
in
profile:
only
signature,
otherwise:
pincode
or
direct
contact
by
phone
with
AmEx
§ result:
less
reversed
transacRons
(<3%)
-‐>
lower
costs!
§ result:
beVer
insight
in
customers
spending
-‐>
predicRve
analyRcs!
©
2013
Harro
M.
Wiersma
–
23
September
2013
–
Swiss
Big
Data
Usergroup
Zürich