SlideShare a Scribd company logo
1 of 18
Download to read offline
Data
Architecture
OMG – It’s Made of People!
Mark Madsen, Teradata
@markmadsen
https://www.linkedin.com/in/markmadsen/
The Man. The Myth. The Mark.
Fellow
Technology & Innovation Office
President
Autonomous Robotics
Artificial Intelligence
Data work is not easy. Ask any user.
Technology exists to help the
organization to be more productive
Organizations are made of people
Our goal is to make it easy for
organizations (people) to use data
Data architecture is the foundation
on which this work depends
Why This Topic? Have you tried
turning it off
and on again?
What Do We Mean By Data Architecture?
Data Storage? Data Models? Data Technologies?
What Do We Mean By Data Architecture?
Data Storage? Data Models? Data Technologies?
Data Architecture is
Processes, Standards, and Policies
that address an organization’s collection, storage,
management, and use of data.
It tells you something about what and how
but doesn’t dictate implementation.
You should be able to answer these
key questions:
1. What do you collect, and why?
2. Where do you keep data, and why?
3. How do you organize, curate, and
integrate data?
This takes organization and methods
Where to focus?
Do you focus on organizing
books?
That’s the data-first
approach. Organize
everything up front without
knowing how it is used.
Organize the data wrong
and nobody can find or use
anything.
Where to focus?
Do you focus on the
building that stores books?
That’s the technology-first
approach. Don’t organize
anything in advance. Use
technology to sort it out.
You may have a catalog of
all the contents. Good luck
finding what you need.
Focus on the people and what they do.
Not the books.
Not the building.
What people say
I want self-service!
What they mean
Users think “self-service” in
terms of a finished data
product – self service equals
an answer to a question.
What people say
I want self-service!
What developers
hear
Developers think
“self-service” is data access,
which means the user must
be self-reliant.
Hearing a need, ask:
“Why is this an unmet need?”
Bad IT and organizational policies cause more problems
than technology failures or bad data.
Policy is a part of architecture that is ignored.
Shape architecture for people.
Don’t try to force people to technology.
• Get a quick answer
• Solve a one-off problem
• Analyze causes of a problem
• Build a predictive model
• Make repetitive decisions
• Use data in a routine process
• Make a complex decision
• Do experiments and analyze results
• Explain a situation to someone else
• Choose a course of action
• Convince others to take action
Architecture focuses on
what people want to do
How To Understand What Data Is Being Used?
Monitor the data environments.
Capture what data is used.
Catalogs of data don’t tell you anything
about use – and use changes over time.
This means users shouldn’t control
storage. Copies they make outside your
view are invisible.
So: you must give them a place to work
and not restrict it.
Focus on visibility of use
Different Views – Data and Users
The value of data is tied to its use.
This shows relationships between
people and data used.
70% of the data is used and reused
constantly. 30% of the data is used
by one or a few people, often new
data with undetermined value.
Usage information shows where and
how you should focus curation –
what you need to manage based on
the people using data.
Finally: establish curation practices based on data use
Curation is about what data is used, by whom, and for what purposes
Collect, Label, Link Categorize, Organize Index, Catalog, Place
The amount of available data
is vast. You can’t store it all.
You can’t analyze it all.
Choose wisely.
There’s a difference between
organizing datasets and data
modeling. One is oriented to
datasets and their use, and
one to the contents of the
datasets.
An important and oft-ignored
element of data architecture is
making sure the data is
findable and accessible by the
people who need it. This is a
curation task, not a data
management task
Thank you.
©2021 Teradata
Thank you.
©2021 Teradata

More Related Content

What's hot

Operationalizing Machine Learning in the Enterprise
Operationalizing Machine Learning in the EnterpriseOperationalizing Machine Learning in the Enterprise
Operationalizing Machine Learning in the Enterprisemark madsen
 
Everything Has Changed Except Us: Modernizing the Data Warehouse
Everything Has Changed Except Us: Modernizing the Data WarehouseEverything Has Changed Except Us: Modernizing the Data Warehouse
Everything Has Changed Except Us: Modernizing the Data Warehousemark madsen
 
Disruptive Innovation: how do you use these theories to manage your IT?
Disruptive Innovation: how do you use these theories to manage your IT?Disruptive Innovation: how do you use these theories to manage your IT?
Disruptive Innovation: how do you use these theories to manage your IT?mark madsen
 
Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)mark madsen
 
Everything has changed except us
Everything has changed except usEverything has changed except us
Everything has changed except usmark madsen
 
Briefing room: An alternative for streaming data collection
Briefing room: An alternative for streaming data collectionBriefing room: An alternative for streaming data collection
Briefing room: An alternative for streaming data collectionmark madsen
 
Building Data Science Teams
Building Data Science TeamsBuilding Data Science Teams
Building Data Science TeamsEMC
 
Big Data and Bad Analogies
Big Data and Bad AnalogiesBig Data and Bad Analogies
Big Data and Bad Analogiesmark madsen
 
Strata Data Conference 2019 : Scaling Visualization for Big Data in the Cloud
Strata Data Conference 2019 : Scaling Visualization for Big Data in the CloudStrata Data Conference 2019 : Scaling Visualization for Big Data in the Cloud
Strata Data Conference 2019 : Scaling Visualization for Big Data in the CloudJaipaul Agonus
 
Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field Domino Data Lab
 
Intro to Data Science for Non-Data Scientists
Intro to Data Science for Non-Data ScientistsIntro to Data Science for Non-Data Scientists
Intro to Data Science for Non-Data ScientistsSri Ambati
 
Big dataplatform operationalstrategy
Big dataplatform operationalstrategyBig dataplatform operationalstrategy
Big dataplatform operationalstrategyHimanshu Bari
 
Analytics 3.0 Measurable business impact from analytics & big data
Analytics 3.0 Measurable business impact from analytics & big dataAnalytics 3.0 Measurable business impact from analytics & big data
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
 
Building Data Science Teams: A Moneyball Approach
Building Data Science Teams: A Moneyball ApproachBuilding Data Science Teams: A Moneyball Approach
Building Data Science Teams: A Moneyball Approachjoshwills
 
5 Factors Impacting Your Big Data Project's Performance
5 Factors Impacting Your Big Data Project's Performance 5 Factors Impacting Your Big Data Project's Performance
5 Factors Impacting Your Big Data Project's Performance Qubole
 
How to Build Data Science Teams
How to Build Data Science TeamsHow to Build Data Science Teams
How to Build Data Science TeamsGanes Kesari
 
Moving Data Science from an Event to A Program: Considerations in Creating Su...
Moving Data Science from an Event to A Program: Considerations in Creating Su...Moving Data Science from an Event to A Program: Considerations in Creating Su...
Moving Data Science from an Event to A Program: Considerations in Creating Su...Domino Data Lab
 
Big Data - Insights & Challenges
Big Data - Insights & ChallengesBig Data - Insights & Challenges
Big Data - Insights & ChallengesRupen Momaya
 

What's hot (20)

Operationalizing Machine Learning in the Enterprise
Operationalizing Machine Learning in the EnterpriseOperationalizing Machine Learning in the Enterprise
Operationalizing Machine Learning in the Enterprise
 
Everything Has Changed Except Us: Modernizing the Data Warehouse
Everything Has Changed Except Us: Modernizing the Data WarehouseEverything Has Changed Except Us: Modernizing the Data Warehouse
Everything Has Changed Except Us: Modernizing the Data Warehouse
 
Disruptive Innovation: how do you use these theories to manage your IT?
Disruptive Innovation: how do you use these theories to manage your IT?Disruptive Innovation: how do you use these theories to manage your IT?
Disruptive Innovation: how do you use these theories to manage your IT?
 
Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)Bi isn't big data and big data isn't BI (updated)
Bi isn't big data and big data isn't BI (updated)
 
Everything has changed except us
Everything has changed except usEverything has changed except us
Everything has changed except us
 
Briefing room: An alternative for streaming data collection
Briefing room: An alternative for streaming data collectionBriefing room: An alternative for streaming data collection
Briefing room: An alternative for streaming data collection
 
Building Data Science Teams
Building Data Science TeamsBuilding Data Science Teams
Building Data Science Teams
 
Big Data and Bad Analogies
Big Data and Bad AnalogiesBig Data and Bad Analogies
Big Data and Bad Analogies
 
Strata Data Conference 2019 : Scaling Visualization for Big Data in the Cloud
Strata Data Conference 2019 : Scaling Visualization for Big Data in the CloudStrata Data Conference 2019 : Scaling Visualization for Big Data in the Cloud
Strata Data Conference 2019 : Scaling Visualization for Big Data in the Cloud
 
Machine Learning in Big Data
Machine Learning in Big DataMachine Learning in Big Data
Machine Learning in Big Data
 
Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field
 
Intro to Data Science for Non-Data Scientists
Intro to Data Science for Non-Data ScientistsIntro to Data Science for Non-Data Scientists
Intro to Data Science for Non-Data Scientists
 
Big dataplatform operationalstrategy
Big dataplatform operationalstrategyBig dataplatform operationalstrategy
Big dataplatform operationalstrategy
 
Analytics 3.0 Measurable business impact from analytics & big data
Analytics 3.0 Measurable business impact from analytics & big dataAnalytics 3.0 Measurable business impact from analytics & big data
Analytics 3.0 Measurable business impact from analytics & big data
 
Building Data Science Teams: A Moneyball Approach
Building Data Science Teams: A Moneyball ApproachBuilding Data Science Teams: A Moneyball Approach
Building Data Science Teams: A Moneyball Approach
 
5 Factors Impacting Your Big Data Project's Performance
5 Factors Impacting Your Big Data Project's Performance 5 Factors Impacting Your Big Data Project's Performance
5 Factors Impacting Your Big Data Project's Performance
 
How to Build Data Science Teams
How to Build Data Science TeamsHow to Build Data Science Teams
How to Build Data Science Teams
 
Moving Data Science from an Event to A Program: Considerations in Creating Su...
Moving Data Science from an Event to A Program: Considerations in Creating Su...Moving Data Science from an Event to A Program: Considerations in Creating Su...
Moving Data Science from an Event to A Program: Considerations in Creating Su...
 
Big Data - Insights & Challenges
Big Data - Insights & ChallengesBig Data - Insights & Challenges
Big Data - Insights & Challenges
 
Big Data: Issues and Challenges
Big Data: Issues and ChallengesBig Data: Issues and Challenges
Big Data: Issues and Challenges
 

Similar to Data Architecture - Focus on People

Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!
Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!
Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!Polestar Solutions
 
Data Systems Integration & Business Value Pt. 1: Metadata
Data Systems Integration & Business Value Pt. 1: MetadataData Systems Integration & Business Value Pt. 1: Metadata
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
 
Data-Ed: Data Systems Integration & Business Value PT. 1: Metadata
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData-Ed: Data Systems Integration & Business Value PT. 1: Metadata
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
 
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptx
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptxExplorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptx
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptxwindu19
 
Data-Ed: Metadata Strategies
 Data-Ed: Metadata Strategies Data-Ed: Metadata Strategies
Data-Ed: Metadata StrategiesData Blueprint
 
Data science and data analytics major similarities and distinctions (1)
Data science and data analytics  major similarities and distinctions (1)Data science and data analytics  major similarities and distinctions (1)
Data science and data analytics major similarities and distinctions (1)Robert Smith
 
Data Fluency - AUA Conference
Data Fluency - AUA ConferenceData Fluency - AUA Conference
Data Fluency - AUA ConferenceMartha Horler
 
Data-Ed: Trends in Data Modeling
Data-Ed: Trends in Data ModelingData-Ed: Trends in Data Modeling
Data-Ed: Trends in Data ModelingData Blueprint
 
Data-Ed Online: Trends in Data Modeling
Data-Ed Online: Trends in Data ModelingData-Ed Online: Trends in Data Modeling
Data-Ed Online: Trends in Data ModelingDATAVERSITY
 
Learning Data Analytics
Learning Data AnalyticsLearning Data Analytics
Learning Data AnalyticsLearnbay
 
TTG Int.LTD Data Mining Technique
TTG Int.LTD Data Mining TechniqueTTG Int.LTD Data Mining Technique
TTG Int.LTD Data Mining TechniqueMehmet Beyaz
 
Paradigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the tableParadigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the tableParadigm4
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data ScienceSwapnilSaurav10
 
Data modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networksData modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networksDr. Richard Otieno
 
Knowledge Management in Healthcare Analytics
Knowledge Management in Healthcare AnalyticsKnowledge Management in Healthcare Analytics
Knowledge Management in Healthcare AnalyticsGregory Nelson
 

Similar to Data Architecture - Focus on People (20)

Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!
Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!
Guided Analytics vs. Self-Service BI: Choose Your Path to Data-driven Success!
 
Data Systems Integration & Business Value Pt. 1: Metadata
Data Systems Integration & Business Value Pt. 1: MetadataData Systems Integration & Business Value Pt. 1: Metadata
Data Systems Integration & Business Value Pt. 1: Metadata
 
Data-Ed: Data Systems Integration & Business Value PT. 1: Metadata
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData-Ed: Data Systems Integration & Business Value PT. 1: Metadata
Data-Ed: Data Systems Integration & Business Value PT. 1: Metadata
 
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptx
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptxExplorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptx
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptx
 
Make data more human
Make data more humanMake data more human
Make data more human
 
Data-Ed: Metadata Strategies
 Data-Ed: Metadata Strategies Data-Ed: Metadata Strategies
Data-Ed: Metadata Strategies
 
Data science and data analytics major similarities and distinctions (1)
Data science and data analytics  major similarities and distinctions (1)Data science and data analytics  major similarities and distinctions (1)
Data science and data analytics major similarities and distinctions (1)
 
Data Fluency - AUA Conference
Data Fluency - AUA ConferenceData Fluency - AUA Conference
Data Fluency - AUA Conference
 
Data-Ed: Trends in Data Modeling
Data-Ed: Trends in Data ModelingData-Ed: Trends in Data Modeling
Data-Ed: Trends in Data Modeling
 
Data-Ed Online: Trends in Data Modeling
Data-Ed Online: Trends in Data ModelingData-Ed Online: Trends in Data Modeling
Data-Ed Online: Trends in Data Modeling
 
1 UNIT-DSP.pptx
1 UNIT-DSP.pptx1 UNIT-DSP.pptx
1 UNIT-DSP.pptx
 
Big data analytics
Big data analyticsBig data analytics
Big data analytics
 
Learning Data Analytics
Learning Data AnalyticsLearning Data Analytics
Learning Data Analytics
 
TTG Int.LTD Data Mining Technique
TTG Int.LTD Data Mining TechniqueTTG Int.LTD Data Mining Technique
TTG Int.LTD Data Mining Technique
 
Paradigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the tableParadigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the table
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Data modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networksData modeling techniques used for big data in enterprise networks
Data modeling techniques used for big data in enterprise networks
 
Knowledge Management in Healthcare Analytics
Knowledge Management in Healthcare AnalyticsKnowledge Management in Healthcare Analytics
Knowledge Management in Healthcare Analytics
 
Making sense of BI
Making sense of BIMaking sense of BI
Making sense of BI
 
365 Data Science
365 Data Science365 Data Science
365 Data Science
 

More from mark madsen

A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range
A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou RangeA Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range
A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Rangemark madsen
 
A Pragmatic Approach to Analyzing Customers
A Pragmatic Approach to Analyzing CustomersA Pragmatic Approach to Analyzing Customers
A Pragmatic Approach to Analyzing Customersmark madsen
 
Building the Enterprise Data Lake: A look at architecture
Building the Enterprise Data Lake: A look at architectureBuilding the Enterprise Data Lake: A look at architecture
Building the Enterprise Data Lake: A look at architecturemark madsen
 
Briefing Room analyst comments - streaming analytics
Briefing Room analyst comments - streaming analyticsBriefing Room analyst comments - streaming analytics
Briefing Room analyst comments - streaming analyticsmark madsen
 
On the edge: analytics for the modern enterprise (analyst comments)
On the edge: analytics for the modern enterprise (analyst comments)On the edge: analytics for the modern enterprise (analyst comments)
On the edge: analytics for the modern enterprise (analyst comments)mark madsen
 
Crossing the chasm with a high performance dynamically scalable open source p...
Crossing the chasm with a high performance dynamically scalable open source p...Crossing the chasm with a high performance dynamically scalable open source p...
Crossing the chasm with a high performance dynamically scalable open source p...mark madsen
 
Don't let data get in the way of a good story
Don't let data get in the way of a good storyDon't let data get in the way of a good story
Don't let data get in the way of a good storymark madsen
 
Don't follow the followers
Don't follow the followersDon't follow the followers
Don't follow the followersmark madsen
 
Exploring cloud for data warehousing
Exploring cloud for data warehousingExploring cloud for data warehousing
Exploring cloud for data warehousingmark madsen
 
Open Data: Free Data Isn't the Same as Freeing Data
Open Data: Free Data Isn't the Same as Freeing DataOpen Data: Free Data Isn't the Same as Freeing Data
Open Data: Free Data Isn't the Same as Freeing Datamark madsen
 
Exploring cloud for data warehousing
Exploring cloud for data warehousingExploring cloud for data warehousing
Exploring cloud for data warehousingmark madsen
 
Wake up and smell the data
Wake up and smell the dataWake up and smell the data
Wake up and smell the datamark madsen
 
Big Data Wonderland: Two Views on the Big Data Revolution
Big Data Wonderland: Two Views on the Big Data RevolutionBig Data Wonderland: Two Views on the Big Data Revolution
Big Data Wonderland: Two Views on the Big Data Revolutionmark madsen
 
Using Data Virtualization to Integrate With Big Data
Using Data Virtualization to Integrate With Big DataUsing Data Virtualization to Integrate With Big Data
Using Data Virtualization to Integrate With Big Datamark madsen
 
One Size Doesn't Fit All: The New Database Revolution
One Size Doesn't Fit All: The New Database RevolutionOne Size Doesn't Fit All: The New Database Revolution
One Size Doesn't Fit All: The New Database Revolutionmark madsen
 

More from mark madsen (15)

A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range
A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou RangeA Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range
A Brief Tour through the Geology & Endemic Botany of the Klamath-Siskiyou Range
 
A Pragmatic Approach to Analyzing Customers
A Pragmatic Approach to Analyzing CustomersA Pragmatic Approach to Analyzing Customers
A Pragmatic Approach to Analyzing Customers
 
Building the Enterprise Data Lake: A look at architecture
Building the Enterprise Data Lake: A look at architectureBuilding the Enterprise Data Lake: A look at architecture
Building the Enterprise Data Lake: A look at architecture
 
Briefing Room analyst comments - streaming analytics
Briefing Room analyst comments - streaming analyticsBriefing Room analyst comments - streaming analytics
Briefing Room analyst comments - streaming analytics
 
On the edge: analytics for the modern enterprise (analyst comments)
On the edge: analytics for the modern enterprise (analyst comments)On the edge: analytics for the modern enterprise (analyst comments)
On the edge: analytics for the modern enterprise (analyst comments)
 
Crossing the chasm with a high performance dynamically scalable open source p...
Crossing the chasm with a high performance dynamically scalable open source p...Crossing the chasm with a high performance dynamically scalable open source p...
Crossing the chasm with a high performance dynamically scalable open source p...
 
Don't let data get in the way of a good story
Don't let data get in the way of a good storyDon't let data get in the way of a good story
Don't let data get in the way of a good story
 
Don't follow the followers
Don't follow the followersDon't follow the followers
Don't follow the followers
 
Exploring cloud for data warehousing
Exploring cloud for data warehousingExploring cloud for data warehousing
Exploring cloud for data warehousing
 
Open Data: Free Data Isn't the Same as Freeing Data
Open Data: Free Data Isn't the Same as Freeing DataOpen Data: Free Data Isn't the Same as Freeing Data
Open Data: Free Data Isn't the Same as Freeing Data
 
Exploring cloud for data warehousing
Exploring cloud for data warehousingExploring cloud for data warehousing
Exploring cloud for data warehousing
 
Wake up and smell the data
Wake up and smell the dataWake up and smell the data
Wake up and smell the data
 
Big Data Wonderland: Two Views on the Big Data Revolution
Big Data Wonderland: Two Views on the Big Data RevolutionBig Data Wonderland: Two Views on the Big Data Revolution
Big Data Wonderland: Two Views on the Big Data Revolution
 
Using Data Virtualization to Integrate With Big Data
Using Data Virtualization to Integrate With Big DataUsing Data Virtualization to Integrate With Big Data
Using Data Virtualization to Integrate With Big Data
 
One Size Doesn't Fit All: The New Database Revolution
One Size Doesn't Fit All: The New Database RevolutionOne Size Doesn't Fit All: The New Database Revolution
One Size Doesn't Fit All: The New Database Revolution
 

Recently uploaded

Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝soniya singh
 
办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一
办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一
办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一F La
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Cantervoginip
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhijennyeacort
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptSonatrach
 
办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一
办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一
办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一F La
 
How we prevented account sharing with MFA
How we prevented account sharing with MFAHow we prevented account sharing with MFA
How we prevented account sharing with MFAAndrei Kaleshka
 
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一fhwihughh
 
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...soniya singh
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.natarajan8993
 
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...dajasot375
 
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024thyngster
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改yuu sss
 
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptxAmazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptxAbdelrhman abooda
 
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degreeyuu sss
 
PKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptxPKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptxPramod Kumar Srivastava
 
04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationshipsccctableauusergroup
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfgstagge
 
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Jack DiGiovanna
 

Recently uploaded (20)

Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Defence Colony Delhi 💯Call Us 🔝8264348440🔝
 
办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一
办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一
办理(Vancouver毕业证书)加拿大温哥华岛大学毕业证成绩单原版一比一
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Canter
 
Call Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort ServiceCall Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort Service
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
 
办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一
办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一
办理(UWIC毕业证书)英国卡迪夫城市大学毕业证成绩单原版一比一
 
How we prevented account sharing with MFA
How we prevented account sharing with MFAHow we prevented account sharing with MFA
How we prevented account sharing with MFA
 
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
 
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
High Class Call Girls Noida Sector 39 Aarushi 🔝8264348440🔝 Independent Escort...
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
 
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
 
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
 
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptxAmazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
Amazon TQM (2) Amazon TQM (2)Amazon TQM (2).pptx
 
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
 
PKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptxPKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptx
 
04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdf
 
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
 

Data Architecture - Focus on People

  • 1. Data Architecture OMG – It’s Made of People! Mark Madsen, Teradata @markmadsen https://www.linkedin.com/in/markmadsen/
  • 2. The Man. The Myth. The Mark. Fellow Technology & Innovation Office President Autonomous Robotics Artificial Intelligence
  • 3. Data work is not easy. Ask any user. Technology exists to help the organization to be more productive Organizations are made of people Our goal is to make it easy for organizations (people) to use data Data architecture is the foundation on which this work depends Why This Topic? Have you tried turning it off and on again?
  • 4. What Do We Mean By Data Architecture? Data Storage? Data Models? Data Technologies?
  • 5. What Do We Mean By Data Architecture? Data Storage? Data Models? Data Technologies? Data Architecture is Processes, Standards, and Policies that address an organization’s collection, storage, management, and use of data. It tells you something about what and how but doesn’t dictate implementation. You should be able to answer these key questions: 1. What do you collect, and why? 2. Where do you keep data, and why? 3. How do you organize, curate, and integrate data?
  • 7. Where to focus? Do you focus on organizing books? That’s the data-first approach. Organize everything up front without knowing how it is used. Organize the data wrong and nobody can find or use anything.
  • 8. Where to focus? Do you focus on the building that stores books? That’s the technology-first approach. Don’t organize anything in advance. Use technology to sort it out. You may have a catalog of all the contents. Good luck finding what you need.
  • 9. Focus on the people and what they do. Not the books. Not the building.
  • 10. What people say I want self-service! What they mean Users think “self-service” in terms of a finished data product – self service equals an answer to a question.
  • 11. What people say I want self-service! What developers hear Developers think “self-service” is data access, which means the user must be self-reliant.
  • 12. Hearing a need, ask: “Why is this an unmet need?” Bad IT and organizational policies cause more problems than technology failures or bad data. Policy is a part of architecture that is ignored.
  • 13. Shape architecture for people. Don’t try to force people to technology.
  • 14. • Get a quick answer • Solve a one-off problem • Analyze causes of a problem • Build a predictive model • Make repetitive decisions • Use data in a routine process • Make a complex decision • Do experiments and analyze results • Explain a situation to someone else • Choose a course of action • Convince others to take action Architecture focuses on what people want to do
  • 15. How To Understand What Data Is Being Used? Monitor the data environments. Capture what data is used. Catalogs of data don’t tell you anything about use – and use changes over time. This means users shouldn’t control storage. Copies they make outside your view are invisible. So: you must give them a place to work and not restrict it. Focus on visibility of use
  • 16. Different Views – Data and Users The value of data is tied to its use. This shows relationships between people and data used. 70% of the data is used and reused constantly. 30% of the data is used by one or a few people, often new data with undetermined value. Usage information shows where and how you should focus curation – what you need to manage based on the people using data.
  • 17. Finally: establish curation practices based on data use Curation is about what data is used, by whom, and for what purposes Collect, Label, Link Categorize, Organize Index, Catalog, Place The amount of available data is vast. You can’t store it all. You can’t analyze it all. Choose wisely. There’s a difference between organizing datasets and data modeling. One is oriented to datasets and their use, and one to the contents of the datasets. An important and oft-ignored element of data architecture is making sure the data is findable and accessible by the people who need it. This is a curation task, not a data management task
  • 18. Thank you. ©2021 Teradata Thank you. ©2021 Teradata