How can code life cycle automation satisfy the growing demands in modern enterprise business intelligence? Whilst an agile approach to BI development is useful for delivering value in general, the use of advanced automation techniques can also save significant resources, prevent production errors, and shorten time to market. Gentlemen from Data To Value, Manta Tools, Volkswagen and M&G investments presented and discussed different approaches to agile BI development. Take a look!

1. Tomas Kratky
CEO
Manta Tools
Nigel Higgs
Co-founder
Data To Value

2. Agenda
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3. Introduction to services

4. We help organisations get more value from their
Data
Architecture
5 Core practice areas covering both
business and architecture aspects of
data managment
Service delivery through:
Onsite consulting
Onsite / Offsite Managed Services
Expert users of technology
accelerators for bridging technology &
business Data gap

5. Lean Data Management
Focus on reducing waste &
minimising TCO
Unification of unstructured
information / knowledge
management & structured data
management
Key mantra is minimising time
spent on building solutions
customers do not want
Lean
Information
Managemen
t
Shorter
iterations
Prototyping
& Minimum
Viable
Products
Build-
Measure-
Learn
cycle
Early
adopters
Cross
functional
teams
Actionable
metrics

6. Unique Value Proposition
Experience & expertise
Founders have over 40 years experience working with data
Skilled in defining data strategy and implementing data architecture,
governance and analytics solutions
Focussed on delivering business value
Align data strategy with client’s strategic goals
Work packages based on business case and ROI
Lean, agile & iterative approach
Hybrid consultancy model to scale to meet demand
Partner with innovative vendors of data tools software

7. We have a number of industry
partnerships that allow us to hit the
ground running
We also use industry leading
platforms such as AWS for hosting
and Tableau for Data Visualisation
Our focus is on providing customers
with the most appropriate tooling to
continue to make progress after initial
projects have completed
Tooling & Partners
Data to Value industry partners
& platforms:

8. Lean Approach – Iterative Process
Maturity benchmarking
Data Profiling & Data
Discovery
Harvest key metadata
(apps, lineage, processes
etc.)
Test rules &
capture metrics
Generate risk &
cost metrics
Capture quality,
governance &
modelling notes
Review issues
using
visualisations &
dashboards
Prototype data
solutions
Implement
practical

9. Integrated Approach
data quality &
governance
metrics
data models,
glossaries &
dictionaries
disparate data
& metadata
data profiling &
metadata
discovery
ontologies
controlled vocabularies

10. Typical Outputs
DQ Issue lists & KPIs to
guide decision making
Powerful, interactive
visualisations
Models, Knowledge
Graphs & Glossaries
to understand what
data assets you
have
Dashboards articulating the
data quality issues that are
holding you back
Clean, actionable &
well structured data
in a variety of
formats
Ready to use
Prototypes & POCs

11. Passionate, Innovative, Lean
Lean Information Management specialists
Data to Value Ltd.
2nd Floor Elizabeth House,
Waterloo,
London SE1 7NQ
United Kingdom
T +44 (0) 208 278 7351
www.datatovalue.co.uk
info@datatovalue.co.uk

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30. Enterprise vs Business Driven BI
Raju Sonawane

31. About Me
 Twenty Six years’ experience in Business Intelligence,
enterprise architecture, strategy/roadmap, design,
development and project/people management.
 Roles played - Head of Business Intelligence, Data
Architect, BI Architect, Solution Architect, Agile Scrum
Master, Project Manager and onsite/offshore Business
Development Manager
 Domain/Industry - Fund/Investment Management,
Lloyds of London Specialist Insurance/ Reinsurance,
Life Insurance and Consultancy
The content in this presentation is my opinion/view of BI. My current/previous employers may have different views.

32. BI Maturity in my view

33. Enterprise BI

34. Ways to deliver it

35. Strengths and Weaknesses

36. The side effect –
Homegrown UDAs

37. Business Driven BI

38. Ways to deliver it
Strategy
•Define the Enterprise BI Strategy
•Set the BI products roadmap
Enable the
Platform
•Open the platform to the users
•Empower the users with self-service
Rapid
Prototypes
•Promote “BI as a Service”
•Identify the high value low size use cases
•Build the rapid prototypes along with the users
Leverage
User-driven BI
•Govern the user-driven BI
•Leverage the popular user-driven BI to build Enterprise BI

39. Ways to deliver it
Demo the existing BI
capabilities
Conduct Workshops
for the new use cases
Deliver the rapid
prototypes with the
users

40. Ways to deliver it

41. Strengths and Weaknesses

42. BI Strategy

43. Finally…
Thank You

44. Oliver Cramer
The Model is the Foundation for Data Warehouse Automation
Agile BI Development Through Automation
London

45. DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation

46. DWH42
About me
• Data Warehouse Architect
• 13 years working in the Business Intelligence area
• Since 2003 working with elementary building blocks for the Data Warehouse
• Blog www.dwh42.de Data Warehouse Automation
• Interested in the exchange of knowledge about Core Data Warehouse
modeling styles

47. DWH42
About me
• TDWI Europe Fellow
• ANCHOR CERTIFIED MODELER Version 2014
• Certified Data Vault 2.0 Practitioner
• Coautor of „Neue Wege in der Datenmodellierung - Data Vault heißt die moderne Antwort“ in
BI-Spektrum 03-2014
• Member of the Boulder BI Brain Trust
• Member of the BI-Podium Advisory Board Germany
• Responsible editor of the TDWI Germany Online Special „Data Vault“
• Organizer Data Vault Modeling and Certification, Hannover
with Genesee Academy (CDVDM course)

48. DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation

49. DWH42
The maturity path of
understanding
• Multiple perspectives on the facts = Data Warehouse as an enabler
to make your own picture of the world from existing data and information!
• One version of the facts = Data Warehouse as a recording device
• One version of the truth = Data Warehouse delivers the truth

50. DWH42
Data: consistency vs. availability
There is a fundamental choice to be made when data is to be 'processed':
• a choice between consistency vs. availability
or
• a choice between work upstream vs. work downstream
or
• a choice between a sustainable (long term) view vs. an opportunistic (short term) view
on data
Ronald Damhof http://prudenza.typepad.com/dwh/2015/11/there-is-a-fundamental-choice-to-be-made-when-data-is-to-be-processed-a-choice-betweenconsistency-vs-availability-or-a.html

51. DWH42
The confusion solution
Lars Rönnback:
"When working with information, confusion is sometimes unavoidable. To be more precise,
when the process of identification cannot give unambiguous results, such confusion arises.
... Push that problem into the future, to solve it when you find the missing pieces, while still
retaining analytic capabilities.
Simply store all the possible outcomes in advance, with different reliabilities, or store the
most likely scenario and correct it later if it was wrong.
http://www.anchormodeling.com/?page_id=360

52. DWH42
Main model requirements
• The model must be capable to absorb
multiple perspectives on the facts!
• The model must be capable of corrections!

53. DWH42
Our problem -> rendering
knowledge
Dave Snowden: 7 Principles of Knowledge Management / Rendering Knowledge:
1. Knowledge can only be volunteered, it cannot be conscripted.
2. We only know what we know when we need to know it.
3. In the context of real need few people will withhold their knowledge.
4. Everything is fragmented.
5. Tolerated failure imprints learning better than success.
6. The way we know things is not the way we report we know things.
7. We always know more than we can say, and we always say more than we can write down.
http://cognitive-edge.com/blog/rendering-knowledge/

54. DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation

55. DWH42
The gap
The big gap between modelers and business people is the
language we speak!
The modelers mantra:
• We have to close the gap!
• They will never close the gap!
• They will not move in our direction!

56. DWH42
The logic
• We aspire to be logical modelers, to create the best logical
model!
• Are the business people logical? Are they like Spock from the
Starship Enterprise? Are they from the planet Vulcan?
• No, they are humans from the planet earth like we are!

57. DWH42
The advancement
• The model must have a fully communication orientation (in
this case business speech) (is that logical modeling?)
• For this reason the model must support homonyms and
synonyms!
• A synonym is a word or phrase that means exactly or nearly
the same as another word or phrase in the same language.
• In linguistics, a homonym is one of a group of words that
share the same pronunciation but have different meanings,
whether spelled the same or not.

58. DWH42
Fully communication orientation
-> Business model
From Quipu:
• This business model does not normally exist in any source
system: it must be developed in close cooperation with the
business to reflect the terms and definition of the data that
the business chooses to work with. It identifies the business
keys that identify the various business entities and their inter-
relations. It also specifies all relevant attributes and facts
related to these business entities that are required for
management reporting, (predictive) analysis, etc.
http://www.datawarehousemanagement.org/

59. DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation

60. DWH42
The model
Model requirements:
• It must have integration points.
• It must support identification.
• It must support relationships / Unit of Work relationships!
• It must support dynamic relationships.
• It must support storing attributes from different origins / integration of attributes is not
necessary!
• It must categorize attributes for identification.
• It must be a model with historization capabilities. And history of history?

61. DWH42
The model
Model requirements:
• Support of data provenance!
Data provenance refers to the ability to trace and verify the creation of data,
how it has been used or moved among different databases, as well as altered
throughout its lifecycle.
• It must follow standards.
• It must follow naming conventions.
• It must follow patterns.

62. DWH42
The model
Model requirements:
• It must be scalable.
• It must be readable and understandable!
• It must be searchable. Crawler!
• It must be partition able.
• It must be extendable.
• The possibility to model extensions without destruction of current entities!
• It must be version able.

63. DWH42
The model
Model requirements:
• It must support the separation of concerns!
• It must have a raw data area.
• It must have a integration area.
• It must have a rule area.
• It must have a area for sensible data.
• It must have a business area.
• It must support temporal and business perspectives.

64. DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation

65. DWH42
Data Warehouse Automation
The big picture
All these details might make it hard to understand
how this has anything to do with
automation of Data Warehouse.
• The only two steps that can’t get automated are the
information modeling process and the semantic mapping exercise.
• Is that statement subject to change?
• Today the rest, before applying of rules, is the domain of data warehouse automation!
• And what can be done ...!

66. DWH42
Data Warehouse Automation
Baseline is that the model must separate
keys/identifiers, relationships and
attributes / group of attributes for
Data Warehouse Automation.
It must be fully communication oriented, so we
can close the gap to the business.
In the end we can focus on asking better
questions. This is the next generation of Data
Warehouse Automation.

67. DWH42
End
Thanks for the attention!

69. • Models
• Business Glossary
• Relations
• …
Implementation
• Marts
• Reports
• ETLs
• …
• Enhancement
• Consolidation
• Restructuralization

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81. Panel discussion

82. Connected Data London 2016
The leading conference bringing together
the Linked and Graph Data communities
12th of July – Central London
www.connected-data.london @Connected_Data

83. Connected Data London
MeetUp
Join us at our informal MeetUp event. Listen to short
talks delivered by Connected dData experts and share
your ideas with like minded Connected Data fans!
7th of June – Central London
http://www.meetup.com/Connected-Data-London/ @Connected_Data

84. Thank you for coming!
Please fill out our survey before
leaving
@DataToValue
@Manta_tools
https://www.linkedin.com/company/data-to-value-ltd
https://www.linkedin.com/company/manta-tools