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Chicago(|(November(12–16(
The(Age(of(Big(Data(
and(the(Modern(
Marketer(
Seth(Do?erer(
Conductor(
VP,(MarkeDng(and(Product(
(speaker(logo)(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Link: http://www.youtube.com/watch?v=QV3t-3QIf1E
Chicago(|(November(12–16,(2012(|(#SESCHI(
Data underpins our
economy and our society
- data about how much is
being spent and where,
data about how schools,
hospitals and police are
performing, data about
where things are and data
about the weather.
Tim Berners Lee,
Director of W3C.
Chicago(|(November(12–16,(2012(|(#SESCHI(
What(is((
Big(Data?(
Chicago(|(November(12–16,(2012(|(#SESCHI(
A collection of data sets so large and
complex that it becomes difficult to
process using on-hand database
management tools.
Chicago(|(November(12–16,(2012(|(#SESCHI(
DefiniDon(of(Big(Data(
•  a(
@twitterhandle
Volume - Variety - Velocity
(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Volume(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
700(million(
Facebook(
users(
250(million(
TwiHer(users(
156(million(
public(blogs(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Volume(
@dotterer
An(average(of(294(
billion(eMmails(are(
sent(every(day.(
(
2.4(Billion(
People(
Online((
(
Chicago(|(November(12–16,(2012(|(#SESCHI(
The(machines(also(talk((
to(each(other(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Variety(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Structured(Data(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Variety(
•  Structured(Data:(
•  Unstructured(Data(
•  Even(Structured(Data(has(issues((
@twitterhandle
Unstructured(
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(mostly)!
Chicago(|(November(12–16,(2012(|(#SESCHI(
Velocity(
Chicago(|(November(12–16,(2012(|(#SESCHI(
WalLMart(generates(more(than(1M(
transacDons(an(hour(into(databases(
esDmated(at(more(than(2.5(petabytes(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Hadoop(has(its(roots(as(a(search(engine(
•  (Apache(Hadoop,(the(leading(open(source(soluXon,(was(created(from(Google’s(MapReduce(
and(the(Google(File(System(but(adopted(by(a(Yahoo(employee.(
@dotterer
•  Now,(Hadoop(and(the(resulXng(and(related(technologies(and(toolsets(are(used(to(create(
distributed(processing(across(clusters(of(computers.(((These(scale(up(to(thousands(of(machines(
that(each(can(store(and(process(informaXon,(and(can(pick(up(failures(from(any(of(their(
neighbor(machines.(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Big data is the fuel. It is like oil. If you leave
it in the ground, it doesn’t have value. But
when we find ways to ingest, curate, and
analyze the data in new and different ways,
such as in Watson, Big Data becomes very
interesting.”
Stephen Gold, VP of
Marketing for IBM’s Watson
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
How(Search(Marketers(can(use(big(data(
•  Think(Flow,(not(Batch.(
•  PPC(Vendors(like(Marin,(Kenshoo,(Adobe(all(have(big(data(opXmizaXon((
•  Leverage(crossMdiscipline((SEO(posiXon(data(to(drive(Bids,(and(viceMversa)(
•  Mining(and(teasing(out(the(tacXcs(and(strategies(that(are(working,(vs.(what(is(noise(
•  Opportunity(forecasXng(
@dotterer
Chicago(|(November(12–16,(2012(|(#SESCHI(
How(Cross(Channel(MarkeDng(is(Changing(how(independent(silos(
are(working(Big(Data(Means….(
No(More(Silos(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
How(Cross(Channel(MarkeDng(is(changing(how(we(keep(score(
(
@dotterer
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
•  MCAMADS(M(MulXMChannel(AHribuXon,(Across(Digital(Channels:(
•  MCAMO2S(M(MulXMChannel(AHribuXon,(Online(to(Store(
•  MCAMAMS(M(MulXMChannel(AHribuXon,(Across(MulXple(Screens(
Are(you(referring(to(MCALO2S,(MCALAMS(or(MCALADC?(
http://www.kaushik.net/avinash/multi-channel-attribution-definitions-models/
Chicago(|(November(12–16,(2012(|(#SESCHI(
“The(ability(to(
take(data(L(to(be(
able(to(
understand(it,(to(
process(it,(to(
extract(value(
from(it,(to(
visualize(it,(to(
communicate(
(
(it's(going(to(be(a(
hugely(important(
skill(in(the(next(
decades”(
Hal(Varian((
L(Google’s(Chief(Economist(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Don’t(forget(the((
((((((((((clean(up(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Takeaways(
•  Big(Data(is(what(YOU(say(it(is,(so(that(you(can(solve(your(problems.(((
Don't(lose(sight(of(that,(or(blindly('trust(the(data'.(
•  The(more(frequently((or(faster)(you(analyze(your(data,(the(more(likely(it(is(to(be(valuable.((
Think(less(about(‘batch’(and(more(about(flow.(
•  Find(new(sources(of(data(–(don’t(just(dumping(more(of(the(same(at(it.(((
•  Keep(data(as(long(as(you(can(
•  Become((or(hire)(a(staXsXcian(M(Big(Data(and(advanced(analyXcs(have(emerged,(with(programs(
sprouXng(up(at(USC,(N.C.(State,(NYU(and(elsewhere.(
•  Figuring(out(what(the(data(tells(you(is(hard.((
•  How(to(convince(others(what(the(data(tells(you(is(harder.((VisualizaXon(helps.(
@dotterer
Chicago(|(November(12–16,(2012(|(#SESCHI(
Thanks!(
(
(
sdo?erer@conductor.com(
@do?erer(
Chicago(|(November(12–16,(2012(|(#SESCHI(
Photo(Credits(
•  hHp://www.flickr.com/photos/29691859@N03/2873017925/sizes/l/(
•  hHp://www.flickr.com/photos/baqueroguapo/5567237076/sizes/l/in/photostream/(
•  hHp://www.flickr.com/photos/markusperl/7689989242/sizes/l/in/photostream/(
•  hHp://www.flickr.com/photos/ragfield/6728728687/lightbox/(
•  hHp://www.flickr.com/photos/ppix/2305078608/sizes/l/in/photostream/(
•  hHp://www.flickr.com/photos/matneym/4145370899/sizes/l/in/photostream/(
@dotterer

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