Date post: | 20-Jan-2017 |
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Tomas KratkyCEO
Manta Tools
Nigel HiggsCo-founder
Data To Value
Agenda
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Introduction to services
We help organisations get more value from their Data
Architecture5 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
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
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
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:
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
Integrated Approach
data quality & governance
metrics
data models, glossaries & dictionaries
disparate data & metadata
data profiling & metadata discovery
ontologies controlled vocabularies
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
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 7351www.datatovalue.co.uk
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Enterprise vs Business Driven BI
Raju Sonawane
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.
BI Maturity in my view
Enterprise BI
Ways to deliver it
Strengths and Weaknesses
The side effect –
Homegrown UDAs
Business Driven BI
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
Ways to deliver it
Demo the existing BI capabilities
Conduct Workshops for the new use cases
Deliver the rapid prototypes with the
users
Ways to deliver it
Strengths and Weaknesses
BI Strategy
Finally…
Thank You
Oliver Cramer
The Model is the Foundation for Data Warehouse Automation
Agile BI Development Through Automation
London
DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation
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 Warehousemodeling styles
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, Hannoverwith Genesee Academy (CDVDM course)
DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation
DWH42
The maturity path of understanding
• Multiple perspectives on the facts = Data Warehouse as an enablerto 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
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
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
DWH42
Main model requirements
• The model must be capable to absorb
multiple perspectives on the facts!
• The model must be capable of corrections!
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/
DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation
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!
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!
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.
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/
DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation
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?
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.
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.
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.
DWH42
Agenda
• About me
• Understanding
• The gap
• The model
• Data Warehouse Automation
DWH42
Data Warehouse Automation
The big picture
All these details might make it hard to understandhow this has anything to do withautomation of Data Warehouse.
• The only two steps that can’t get automated are theinformation 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 ...!
DWH42
Data Warehouse Automation
Baseline is that the model must separate keys/identifiers, relationships andattributes / group of attributes forData 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.
DWH42
End
Thanks for the attention!
• Models
• Business Glossary
• Relations
• …
Implementation
• Marts
• Reports
• ETLs
• …
• Enhancement
• Consolidation
• Restructuralization
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Panel discussion
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
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
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