Martyn Jones presented on Big Data, analytics and 4th generation data warehousing. He discussed the importance of a comprehensive data supply framework to obtain, integrate and analyze data from various sources to provide strategic, tactical and operational decision making support. He described moving beyond traditional data warehousing to a 4th generation approach that leverages big data and analytics to gain insights and measure outcomes.

2. Big Data, Analytics and
4th Generation Data Warehousing
Martyn Jones
Big Data Spain 2015

3. agenda
∙ Imperatives.
∙ Data value chains.
∙ Resources.
∙ 4th Generation Data Warehousing.
∙ Analytics Data Store / Big Data.
∙ Information Supply Framework.
Friday 16th from 12:30 pm
to 13:15 pm
Room 25 - Technical
0 5 10 15 20 25 30 35 40 45

4. business background

5. the ages of data
B . C . L i f e o f B r i a n A . D .

6. C h a n g eI n s i g h t
P o t e n t i a l l y
u s e f u l
Simplicity
A b u n d a n t
V o l u m e V e l o c i t y V a r i e t y

7. framework
O b t a i n I n t e g r a t e A n a l y s e P r e s e n t
D A T A
D A T A
D A T A

8. the road to Big Data success…
S t r a t e g i c
T a c t i c a l
O p e r a t i o n a l A n a l y t i c s
A r c h i t e c t e d
M a n a g e d
I n t e g r a t i o n
D a t a

9. scope
BIZ DATA DW
BIG
DATA
STATS PRES

10. Business Imperatives
A good place to start

11. what’s important to business?
BE
NOTICED
CASH
FLOW
BE
NOTICED
CASH
FLOW
BE
NOTICED
CASH
FLOW

12. what else is important to business?
Market share
Differentiation
Ability to execute
Liquidity
Profitability
Time and place utility
React to
competitive threats
Enhance service
scope
Improving customer
service
Respond to price
pressure
Segmentation of n
Addressing short-term
attention spans
Ability to respond to
irrationality
Be noticed
Cash flow
Risk
Legislation
No pressBad press
Customer
centricity
Front office
empowerment
Excellence
Channel
excellence
Operational
excellence
Product
excellence
Cultures
IT business
value
Base protection
Expansion
Diversification
Consolidation

13. Augmented Competitive Forces
Competition from
within the industry
Suppliers Buyers
Replacements
Potential entrants
Threat of replacement
product or service
Threat of new
entrants
Bargaining
power
Bargaining
power
Sources: Michael Porter;Martyn R Jones
and others
Rivalry with
existing
competitors
Pressure groups
Media
Government
Power to
change the game
Exposure

14. McKinsey 7S Framework
Culture

15. differentiated capabilities

16. operating models
Customer segments
Channels
Products
Services
Organsational design
Processes
Data & information
Physical assets
Development
Deployment
Organsational design
Performance management
Information technology
Business
model
Operating
model
People
model
Customers
Systems People
Processes Organisation

17. objectives
1. Information awareness corresponding
to areas of operation and spheres of
control
2. Comprehensive data and information
supply framework
3. Continually seek to maintain and then
improve data’s contribution to
business

18. Business data everywhere
Where, when, what, who, why... how?

19. Data
I n t e r n a l P a s t
E x t e r n a l P r e s e n t
S h a r e d F u t u r e

20. Data
O p e r a t i o n a l O n l i n e
B i g D a t a A r c h i v e d
D a r k D a t a U n m a n a g e d

21. Data
A r c h i v e s S o c i a l M e d i a
D o c u m e n t s M a c h i n e L o g
M e d i a S e n s o r
B u s i n e s s
A p p l i c a t i o n s
D a t a
S t o r a g e
P u b l i c W e b

22. Activities, Abstractions and Relations

23. Velocity
Volume
Variety
Adequacy
Ambiguity
Small
Availability
Accuracy
Relevance
Persistence
Reliability
Value
Obtuseness
Listo
Complexity
Utility
Descriptiveness
Big
Velocidad
Volumen
Variedad
Adecuación
Ambigüedad
Precisión
Disponibilidad
Exactitud
Relevancia
Persistencia
Confiabilidad
Valor
Obtuso
Smart
Complejidad
Utilidad
Descriptivo
Grande
D a t a
Facets of Big DataFacets of Data

25. B I G D A T A
I n t e r n e t o f
T h i n g s
C L O U D
S t a t i s t i c s
D a t a
W a r e h o u s i n g
P r e s e n t a t i o n
D a t a S u p p l y F r a m e w o r k

26. Building Bill’s Data
Warehouse
25 years of... sometimes getting it right

27. Enterprise Data Warehousing – AS IS
S u b j e c t
o r i e n t e d
S t r a t e g i c
d e c i s i o n m a k i n g
I n t e g r a t e d
T i m e
v a r I a n t
N o n – v o l a t i l e

28. Operational Systems Data Warehouse
Purchasing
HR
Credit
Order
Processing
Marketing
SalesLogistics
Billing
Arrangements
ProductsParty
TimeGeography
Transactions
Subject oriented

29. Operational Systems Data Warehouse
Euro Account Customer:
Customer: Village Bank GmbH
Country code: D
Mutual Fund Customer:
Customer: Village Bankers
Region: Westphalia
NTIP Customer:
Customer: Village Bank International
Country: Germany
Account:
Number Customer Type
230956 441353 Euro
010555 441353 MF
291284 441353 NTIP
Party:
Number: 100441353
Name: Village Bank GmbH
Country: Germany
Integrated

30. Operational Systems Data Warehouse
0
10
20
30
40
50
60
70
80
90
100
Trading Activity Snapshots:
Date Security Amount
2006.09.01 MartyBank 79.000.000
2006.09.02 MartyBank 92.000.000
2006.09.03 MartyBank 44.000.000
2006.09.04 MartyBank 39.000.000
2006.09.05 MartyBank 80.000.000
Trading Activity: MartyBank
Time variant

31. Operational Systems Data Warehouse
Order
Processing
Create
Replace
Update Delete
Orders
Read Read
Read ReadWrite
Read
Non-volatile

32. Data Warehousing 2.0
Data Sources
StructuredData
ETL
Extract
Transform
Load
Internal
ODS
ODS
EDW
ETL
Extract
Transform
Load
Data Marts
StructuredData
Unstructured
Data
Mart
Data
Mart
Report Repository
Reports &
Extracts
Stats
Dataselectionandrepresentation
Dataanalytics
Reportsetandextractcreation
Service
Push/PullTechnology
Visualisation
Annotation
Users
Internal
Clients
Otherstakeholders
Metadata, Workflow/Process Control and CIW Management
Metadata Process
ÊDW
Management
Staging
Staged
Data
EDW
Unstructured
EDW
Data
Mart
StructuredData
Unstructured

33. Enterprise Data Warehousing – AS A BODGE
G e t d a t a
W o n d e r w h y i t ‘ s n o t
m e e t i n g e x p e c t a t I o n s
D u m p d a t a
Q u e r y d a t a V i s u a l i s e d a t a

34. Enterprise Data Warehousing – AS A BODGE
DW BODGER TEAM HADOOP TEAM
We built a data dog house using Oracle and
IBM technology and we called it a data
warehouse
We can do data warehousing too and it will be
cheaper, faster and smarter

35. Data Supply Framework
A data architecture for data sourcing,
transformation, integration, storage, search,
analysis and presentation

36. Data Supply Framework
Operational
Data Store
Data
Warehouse
Business
Intelligence
Data
logistics
Operational
applications
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
All
information
and data
consumers
All
information
consumers
All digital
data
All data processing, enrichment
and information creation

37. Internal
digital data
Data Supply Framework
External
digital data
Data logistics
Operational
Data Store
Data
Warehouse
Analytics
Data Store
Data Marts
Statistical
Analysis
Business
Intelligence
Scenarios
Data logistics
Primary data flow
Secondary data flow
Operational
applications
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es

38. EDW
ADS
DM
DM
DM
Statistical
analysis
ETL
T/ETL
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Message
Adapter
Message
Queue
OLTP
Staging
ODS
ETLT/ETL
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
TL
Data Supply Framework
Data Sources 4th Generation Data Warehousing
Data Sources Core Statistics
Cambriano Energy 2015

39. Core Data Sourcing
Comprehensive data acquisition and
transformation

40. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DW 3.0 Information Supply Framework
Cambriano Energy 2015
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter

41. 4th Generation
Data Warehousing
Providing a solid foundation for strategic,
tactical and operational decision making

42. Enterprise Data Warehousing – 4 GEN
S u b j e c t
o r i e n t e d
S t r a t e g i c ,
t a c t i c a l & o p e r a t i o n a l
s u p p o r t
I n t e g r a t e d
T i m e v a r i a n c e &
t i m e p e r s p e c t i v e s
C o n s t r a i n e d
v o l a t i l i t y
C l a s s i f i c a t i o n
s c h e m a
R u l e b a s e d
t r a n s f o r m a t i o n

43. 4th Generation EDW
Interpretation
Prediction
Diagnosis
Design
Planning
Monitoring
Debugging
Repairing
Instruction
Control
S t r a t e g y
T a c t i c s
O p e r a t i o n s

44. Using, applying and measuring
Big Data
Big Data
Big Data
Predictive Analytics
Predictive Analytics
Outcomes
EDW 4.0
EDW 4.0E(A)TL

45. Using, applying and measuring
Big Data
Predictive
analytics
Select
predictions
Define
trackable
actions
Apply
outcomes and
actions to EDW
4
Accumulate
campaign Big
Data
Descriptive
analytics
Select findings
Combine with
trackable
actions
Apply
outcomes and
actions to EDW
4
Run campaign
Analyse campaign and performance of Big Data analytics

46. Forecasts and results – from all perspectives
-400
-300
-200
-100
0
100
200
300
400
500
01/15 02/15 03/15 04/15 05/15 06/15 07/15 08/15 09/15 10/15 11/15 12/15 01/16 02/16 03/16 04/16 05/16 06/16
Cambriano Big Data Campaign 2015-2016
Forecast Actual Strategy BD Costs Benefit
Values Relativity Dimensions HierarchiesStructures
Past Future

47. Using, applying and measuring
•Combining Big Data analytics with Data
Warehousing 4.0
•Planning and managing initiatives
•Measuring, analysing and reporting the
effectiveness of business initiatives
•Measuring, analysing and reporting the
tangible contribution of the Big Data
analytics process to the creation of business
value

48. Big Data and Core Statistics
A multi-faceted data theatre for ad-hoc,
speculative and immediate operational
analytics

49. Internal
digital data
Data Supply Framework
External
digital data
Data
logistics
Operational
Data Store
Data
Warehouse
Analytics
Data Store
Data Marts
Statistical
Analysis
Business
Intelligence
Scenarios
Data
logistics
Primary data flow
Secondary data flow
Operational
applications
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es

50. DSF 4.0 Data Value Chains
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
DATA INFORMATION KNOWLEDGE
Requires context Requires interpretation Requires wisdom
Relevant Correct Usable
Irrelevant Incorrect Useless
Meaningless Misleading Wrong
Value? Value? Value?
  
  
  

51. DSF 4.0 Data Assets in MOSCOW
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
RISK
ASSET
SECURE
BAU
Assurance
Highest High Medium/Low
Very
low/None
MUST SHOULD COULD WON’T
Yes Yes Maybe Maybe/No
Yes Yes Yes Maybe/No
Yes Yes Yes Maybe/No

52. DSF 4.0 Data Assets in MOSCOW
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
RISK
ASSET
SECURE
BAU
Assurance
Highest High Medium/Low
Very
low/None
MUST SHOULD COULD WON’T
Yes Yes Maybe Maybe/No
Yes Yes Yes Maybe/No
Yes Yes Yes Maybe/No

53. DSF 4.0 Data Supply Framework
External
digital data
Data
logistics
Operational
Data Store
Data
Warehouse
Analytics
Data Store
Data Marts
Statistical
Analysis
Business
Intelligence
Scenarios
Data
logistics
Primary data flow
Secondary data flow
Operational
applications
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
OLTP
Applications
‘What if’
analysis
MIS /
Reporting
Visualisation
Publication
º
All digital
data

54. Internal
digital data
DSF 4.0 Data Supply Framework
External
digital data
Data
logistics
Operational
Data Store
Data
Warehouse
Analytics
Data Store
Data Marts
Statistical
Analysis
Business
Intelligence
Scenarios
Data
logistics
Primary data flow
Secondary data flow
Operational
applications
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
All
information
consumers
º
All digital
data

55. Internal
digital data
External
digital data
Primary data flow
Secondary data flow
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
º
Statistics
Data
Science
Big Data
Small Data
Smart Data
This Data
That Data
That
department
Messing
with data
Map Fatten
Retrospect
Reports
Alerts
Visualisation
Analytics
This
department
The other
department
Map Reduce
DSF 4.0 Data Supply Framework

56. DSF 4.0 Data Supply Framework
Operational
Data Store
Data
Warehouse
Business
Intelligence
Data
logistics
Operational
applications
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es
All
information
and data
consumers
All
information
consumers
All digital
data
All data processing, enrichment
and information creation

57. EDW
ADS
DM
DM
DM
Statistical
analysis
ETL
T/ETL
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Message
Adapter
Message
Queue
OLTP
Staging
ODS
ETLT/ETL
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
TL
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Message
Adapter
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es

58. EDW
ADS
DM
DM
DM
Statistical
analysis
ETL
T/ETL
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Message
Adapter
Message
Queue
OLTP
Staging
ODS
ETLT/ETL
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
TL
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Message
Adapter
Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es

59. EDW
ADS
DM
DM
DM
Statistical
analysis
ETL
T/ETL
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Message
Adapter
Message
Queue
OLTP
Staging
ODS
ETLT/ETL
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
TL
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Message
Adapter
Data Sources – This element covers all the current sources, varieties and
volumes of data available which may be used to support processes of
'challenge identification', 'option definition', decision making, including
statistical analysis and scenario generation.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

60. EDW
ADS
DM
DM
DM
Statistical
analysis
ETL
T/ETL
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Message
Adapter
Message
Queue
OLTP
Staging
ODS
ETLT/ETL
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
TL
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Message
Adapter
Core Data Warehousing – This is a suggested evolution path of the DW 2.0
model. It faithfully extends the Inmon paradigm to not only include
unstructured and complex data but also the information and outcomes
derived from statistical analysis performed outside of the Core Data
Warehousing landscape.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

61. EDW
ADS
DM
DM
DM
Statistical
analysis
ETL
T/ETL
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Message
Adapter
Message
Queue
OLTP
Staging
ODS
ETLT/ETL
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
TL
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Message
Adapter
Core Statistics – This element covers the core body of statistical competence,
especially but not only with regards to evolving data volumes, data velocity
and speed, data quality and data variety.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

62. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DW 3.0 Information Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu
INTO THE ZONE!

63. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Complex Data – This is unstructured or highly complexly
structured data contained in documents and other complex
data artefacts, such as multimedia documents.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

64. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Event Data – This is an aspect of Enterprise Process Data,
and typically at a fine-grained level of abstraction. Here are
the business process logs, the internet web activity logs and
other similar sources of event data. The volumes generated
by these sources will tend to be higher than other volumes
of data, and are those that are currently associated with the
Big Data term, covering as it does that masses of
information generated by tracking even the most minor
piece of 'behavioural data' from, for example, someone
casually surfing a web site.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

65. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Infrastructure Data – This aspect includes data which could
well be described as signal data. Continuous high velocity
streams of potentially highly volatile data that might be
processed through complex event correlation and analysis
components.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

66. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Event Applicance – This puts the dynamic data collation,
selection and reduction functionality as close to the point of
event data generation as physically possible.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

67. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Signal Applicance – This puts the dynamic data collation,
selection and reduction functionality as close to the point of
continuous streaming data generation as physically possible.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

68. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DW 3.0 Information Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Distributed Inter Process Communication – Different forms of
messaging allow high volumes of data to be transmitted in
near real time.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

69. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Staging and Reduction – Traditional data staging combined
with in-line data reduction.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

70. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
ET(A)L – Extending ETL to include data analytics components
tightly integrated into parallel ETL job streams.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

71. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
ADS – The Analytics Data Store. 1. Statistics oriented 2.
Integrated by focus area 3. Variable volatility 4. Time variant
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

72. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Statistical Analysis – Qualitative analysis. Diagnostic analysis,
predictive analysis, speculative analysis, data mining, data
exploration, modelling.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

73. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Scenarios and outcomes – 1. Snapshots of outcomes of
scenario analysis as the process of analyzing possible future
events by generating alternative possible outcomes. 2.
Captured outcomes of statistical analysis.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com Martyn Richard Jones 2015 – martynjones.eu

74. ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
DSF 4.0 Data Supply Framework
Martyn Richard Jones 2015 – martynjones.eu
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Write back – The ability to append data, update data and
enrich data within the Analytics Data Store, and to provide
scenario data to the Core Data Warehousing.
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com

75. DSF 4-0 – Core Statistics: Analytics Data Store
Martyn Richard Jones 2015 – martynjones.eu
ADS
Statistical
analysis
ET(A)L
Staging &
Reduction
Signal
Appliance
Message
Adapter
Message
Queue
Infrastructure
Data
Write back
Complex data
Event Data
Event
Appliance
Scenario 1
Scenario 2
Scenario 3
Core Data Warehousing
Core Statistics
Data
Sources
Message
Adapter
Cambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com

76. DSF 4.0 – Analytics Data Store
Martyn Richard Jones 2015 – martynjones.euCambriano Energy 2015 - http://www.cambriano.es Published by goodstrat.com
Distributed File System
Non-relational distributed file storage / NoSQL
DFS (Including ‘refractoring’ of Unix
primitives)
Unix File Store
POSIX compliant
Document
DBMS
Graph DBMS
Key-Value
DBMS
In-memory Column Oriented Relational
DBMS
Relational DBMS (MPP/SMP/Hybrid)
Object DBMS
POSIX compliant Unix / Linux primitives
Relational DBMS

77. DSF 4.0 – What’s important?
Cambriano Energy 2015 - http://www.cambriano.es
Data
Warehouse
Martyn Richard Jones 2015 – martynjones.euPublished by goodstrat.com
Business
Intelligence
Operational
Data Store
Analytics Data
Store
Statistical
Analysis
Dark Data
Big Data
Internet of
Things
Knowledge
Management
Structured
Intellectual
Capital
Cloud

78. Summary
A good place to end, for now

79. Summary
• Consider everything
• Question everything
• Never stop hypothesising
• Never stop testing
• For every initiative have a business
imperative
• Make continuous engagement and
involvement a goal

80. Muchas gracias
Many thanks
Big Data Spain 2015

81. Big Data, Analytics and
4th Generation Data Warehousing
Big Data Spain 2015