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Bi g Dat a
Too Bi g To I gnor e
Speaker
Bi g Dat a Consul t ant and Manager
Cur r ent l y wor ki ng on 3r d
Bi g Dat a
pr oj ect
I BM & Cl ouder a Cer t i f i ed
Dat aCr uncher s, I BM Bi g Dat a Par t ner
Bi g Dat a 2
Agenda
Bi g Dat a
The Dat a Dr i ven Or gani zat i on
The Lambda Ar chi t ect ur e
Bi g Dat a 3
Bi g Dat a Techni cal Dr i ver s
( i t ’ s al l about maki ng i nt el l i gent choi ces)
Bi g Dat a 4
Our Vi si on
Bi g Dat a
Vol ume
Bi g Dat a
Vel oci t y
Our Vi si on
Volume
Var i et y
Our Vi si on
Bi g Dat a Busi ness Dr i ver s
Do More
wi t h Less
Bi g Dat a 9
ANALYTICS
COSTS
Gartner
McKinsey
Forrester
Research
Bi g Dat a 10
Big Data Creating Transparency
Enabling
Experimentation to
discover needs,
expose variability
and improve
performance
Segmenting populations to
customize actions
Replacing/Supporting human
decision making with
automated algorithms
Innovating new business
models, products and
services with big data
Bi g Dat a Tr ansf or ms
Bi g Dat a 11
Tr ansf or mat i on of Onl i ne
Mar ket i ng
BLOGS. FORBES. COM/ DAVEFEI NLEI B
Bi g Dat a 12
Tr ansf or mat i on of Oper at i ons
BLOGS. FORBES. COM/ DAVEFEI NLEI B
Bi g Dat a 13
Tr ansf or mat i on of Cust omer
Ser vi ce
BLOGS. FORBES. COM/ DAVEFEI NLEI B
Bi g Dat a 14
Tr ansf or mat i on of Ener gy
Bi g Dat a 15
Bi g Dat a Def i ni t i on
Bi g Dat a Technol ogi es al l ow you t o
i mpl ement Use Cases whi ch Legacy
Technol ogi es can’ t .
Bi g Dat a 16
Bi g Dat a 17
How Dat a Dr i ven Ar e You?
The Dat a Dr i ven
Or gani zat i on
Bi g Dat a 18
Googl e
“ ALL our busi ness deci si ons ar e based on
DATA”
Bi g Dat a 19
Pr oct or & Gambl e
Bob McDonal d, CEO P&G
“ Da t a i s t he ne w r a w ma t e r i a l f o r a ny
bus i ne s s i n p a r wi t h c a p i t a l , p e o p l e ,
l a bo r ”
P&G anal yzes 200 Tb
- 4 Cl i cks on an i pad t o get i nf or mat i on needed.
Bi g Dat a 20
Bi g Conf usi on
Bi g Dat a 21
Bi g Dat a 22
I mpl ement i ng Bi g Dat a
Our Vi si on on Dat a
Cur r ent Si t uat i on
Bi g Dat a 24
Our Vi si on #1
Focus on Dat a not on Der i ved
Dat a
Bi g Dat a 25
Our Vi si on #2
Dat a i s i mmut abl e
Bi g Dat a 26
Our Vi si on #3
Quer y = f unct i on ( al l dat a)
Bi g Dat a 27
Our Vi si on: Concept
Bi g Dat a 28
Our Vi si on: Ar chi t ect ur e
Bi g Dat a 29
Dat aCr uncher s
We enabl e compani es i n envi si oni ng,
def i ni ng and i mpl ement i ng a dat a
st r at egy.
A one- st op- shop f or al l your Bi g Dat a
needs.
The f i r st Bi g Dat a Consul t ancy agencyBi g Dat a 30
Dat aCr uncher s - Par t ner shi ps
Bi g Dat a 31

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Big Data, Too Big To Ignore

  • 1. Bi g Dat a Too Bi g To I gnor e
  • 2. Speaker Bi g Dat a Consul t ant and Manager Cur r ent l y wor ki ng on 3r d Bi g Dat a pr oj ect I BM & Cl ouder a Cer t i f i ed Dat aCr uncher s, I BM Bi g Dat a Par t ner Bi g Dat a 2
  • 3. Agenda Bi g Dat a The Dat a Dr i ven Or gani zat i on The Lambda Ar chi t ect ur e Bi g Dat a 3
  • 4. Bi g Dat a Techni cal Dr i ver s ( i t ’ s al l about maki ng i nt el l i gent choi ces) Bi g Dat a 4
  • 5. Our Vi si on Bi g Dat a Vol ume
  • 6. Bi g Dat a Vel oci t y
  • 7. Our Vi si on Volume Var i et y
  • 9. Bi g Dat a Busi ness Dr i ver s Do More wi t h Less Bi g Dat a 9 ANALYTICS COSTS
  • 11. Big Data Creating Transparency Enabling Experimentation to discover needs, expose variability and improve performance Segmenting populations to customize actions Replacing/Supporting human decision making with automated algorithms Innovating new business models, products and services with big data Bi g Dat a Tr ansf or ms Bi g Dat a 11
  • 12. Tr ansf or mat i on of Onl i ne Mar ket i ng BLOGS. FORBES. COM/ DAVEFEI NLEI B Bi g Dat a 12
  • 13. Tr ansf or mat i on of Oper at i ons BLOGS. FORBES. COM/ DAVEFEI NLEI B Bi g Dat a 13
  • 14. Tr ansf or mat i on of Cust omer Ser vi ce BLOGS. FORBES. COM/ DAVEFEI NLEI B Bi g Dat a 14
  • 15. Tr ansf or mat i on of Ener gy Bi g Dat a 15
  • 16. Bi g Dat a Def i ni t i on Bi g Dat a Technol ogi es al l ow you t o i mpl ement Use Cases whi ch Legacy Technol ogi es can’ t . Bi g Dat a 16
  • 17. Bi g Dat a 17 How Dat a Dr i ven Ar e You?
  • 18. The Dat a Dr i ven Or gani zat i on Bi g Dat a 18
  • 19. Googl e “ ALL our busi ness deci si ons ar e based on DATA” Bi g Dat a 19
  • 20. Pr oct or & Gambl e Bob McDonal d, CEO P&G “ Da t a i s t he ne w r a w ma t e r i a l f o r a ny bus i ne s s i n p a r wi t h c a p i t a l , p e o p l e , l a bo r ” P&G anal yzes 200 Tb - 4 Cl i cks on an i pad t o get i nf or mat i on needed. Bi g Dat a 20
  • 21. Bi g Conf usi on Bi g Dat a 21
  • 22. Bi g Dat a 22
  • 23. I mpl ement i ng Bi g Dat a Our Vi si on on Dat a
  • 24. Cur r ent Si t uat i on Bi g Dat a 24
  • 25. Our Vi si on #1 Focus on Dat a not on Der i ved Dat a Bi g Dat a 25
  • 26. Our Vi si on #2 Dat a i s i mmut abl e Bi g Dat a 26
  • 27. Our Vi si on #3 Quer y = f unct i on ( al l dat a) Bi g Dat a 27
  • 28. Our Vi si on: Concept Bi g Dat a 28
  • 29. Our Vi si on: Ar chi t ect ur e Bi g Dat a 29
  • 30. Dat aCr uncher s We enabl e compani es i n envi si oni ng, def i ni ng and i mpl ement i ng a dat a st r at egy. A one- st op- shop f or al l your Bi g Dat a needs. The f i r st Bi g Dat a Consul t ancy agencyBi g Dat a 30
  • 31. Dat aCr uncher s - Par t ner shi ps Bi g Dat a 31

Hinweis der Redaktion

  1. Geen goede slogan!
  2. businesses are looking at bigdata solutions What are the key business drivers? Looking at volume, velocity, variability and agility: Help delivering a more agile framework of software to your organization
  3. 44 times as much data in the next decade, 15 Zb in 2015 Data silos (erp, crm, …) Customers Trimble (3Tb in hun database systeem) Truvo (wijzigen van een index duurt 24u) Traditionele systemen kunnen dit volume niet aan. How many data do you have? Turn 12 terabytes of Tweets created each day into improved product sentiment analysis Convert 350 billion annual meter readings to better predict power consumption
  4. Real time Time sensitive decision taking Fraud detection Energy allocation Marketing campaigns Market transactions Solution: Real-time solutions in combination with batch (hadoop) Nosql systems
  5. Structured Unstructured 80% is unstructured data, A key drawback of using traditional relational database systems is that they're not good at handling variable data. A flexible data model Word, email, foto, text, video, …? What are your needs regarding variety? The end result: bringing structure into unstructured data Monitor 100’s of live video feeds from surveillance cameras to target points of interest Exploit the 80% data growth in images, video and documents to improve customer satisfaction
  6. Structured Unstructured 80% is unstructured data, A key drawback of using traditional relational database systems is that they're not good at handling variable data. A flexible data model Word, email, foto, text, video, …? What are your needs regarding variety? The end result: bringing structure into unstructured data Monitor 100’s of live video feeds from surveillance cameras to target points of interest Exploit the 80% data growth in images, video and documents to improve customer satisfaction
  7. VVVA -> traditional tech doesn ’ t cut it anymore
  8. Big Data allows you to have a competitive edge
  9. Discovery Decision-Making Persuasion http://www.zdnet.com/how-big-data-is-being-used-today-three-ways-7000005714/
  10. - Vroeger leads
  11. Monitoring social media Twitter, … Interactions vd customer met interne systemen Email Web app Klantendienst Een meer tevreden klant
  12. Vroeger 1 meetpunt per jaar Een beperkt productieplaatsen Nu Veel producenten/consumenten Provisioning -> real time decision making Billing Complexer maar correcter
  13. VVVA -> traditional tech doesn ’ t cut it anymore
  14. Do we keep on investing into a product HR google video (strata) Do people in their offices in less big cities get less promoted?
  15. CEO From exoskeleton to nervous system
  16. CEO From exoskeleton to nervous system
  17. Silos not interconnected. A lot of data is not analyzed. Komt dit overeen bij jullie? A lot of Data is ignored Data is aggregated (derived) No central data repository with all raw data because of volume and complexity Word, excel, mail, logs, … External data is not used (internet, social media, business partners, …) Historic data is archived but no longer used