SlideShare a Scribd company logo
Data Champions, Online
Data Governance for the CxO in the Digital Era.
Strategies that remove friction from organizational data flow in large organisations with Coert du Plessis,
DataAlchemists, +61406313111 @coertdup
June 2020
DataAlchemists
Still Chasing Master Data in 2020 is like…
Exponential growth in processing = growth in data
Source:
IDC Research, sponsored by
Seagate
https://www.seagate.com/files/w
ww-content/our-
story/trends/files/idc-seagate-
dataage-whitepaper.pdf
Singularity Hubb (Ray Kurzweil/
Moore’s law)
https://singularityhub.com/2016/03/08/will-
the-end-of-moores-law-halt-computings-
exponential-rise/
IDC predicts that the collective sum of the world’s data will grow from 33 zettabytes this
year to a 175ZB by 2025, for a compounded annual growth rate of 61 percent.
Ray’s doubling of compute power/ $
Your list of $ budget demands
#1
We have important data
and not important data
∝
Big corporate isn’t ready for 2x data explosion
If you bravely lift the hood on large corporate data sourcing and integration today, it
looks like spaghetti Macgyvered together with a swiss army knife, wire and Blu
Tack.
We don’t want Macgyvered Data; We want the internet
API Economy is fundamental to automation – a network
API Economy:
From traditional data “stack” to data democracy
People Machine Gate Access Training
Data 1 Data A Data II Data 2F
Networks are built on identities – node by node
API Economy:
From traditional data “stack” to data democracy
#2
Data is a network of
identities
∝
So if the data is a network, how do we organise people?
So if the data is a network, how do we organise people?
Centralised teams Decentralised teams
What if we organised our people like our data, distributed?
Distributed teams
We are used to moving information to authority
Source: L. David Marquette,
Author of “Turn the ship around”
We are used to moving information to authority
Source: L. David Marquette,
Author of “Turn the ship around”
We need to move authority to where the information is
Decisions
… you lead by retaining the risk accountability
Decisions
Risk
Case study using some AI generated faces
Source: AI Generated faces
https://thispersondoesnotexist.com
Business
Analyst
Improvement
CIPO
Chief
Improve
ment
and
Projects
Officer
Case study using some AI generated faces
Source: AI Generated faces
https://thispersondoesnotexist.com
Business
Analyst
Improvement
CIPO
Chief
Improve
ment
and
Projects
Officer
Principal Data
Warehouse
Case study using some AI generated faces
Source: AI Generated faces
https://thispersondoesnotexist.com
Business
Analyst
Improvement
CIPO
Chief
Improve
ment
and
Projects
Officer
Principal Data
Warehouse
Procurement
Analyst
HR consultant
Case study using some AI generated faces
Source: AI Generated faces
https://thispersondoesnotexist.com
Business
Analyst
Improvement
CIPO
Chief
Improve
ment
and
Projects
Officer
Principal Data
Warehouse
CIO
Chief
Informati
on
Officer
Procurement
Analyst
CPO
Chief
Procure
ment
Officer
HR consultant
CPO
Chief
People
Officer
Authority to…
Yes!
Decide where they
spend money to improve
data quality?
Do you mean authority to…
Yes!
Host data in the cloud?
Decide where they
spend money to improve
data quality?
Do you mean authority to…
Host data in the cloud?
Remove data?
Yes!
Decide where they
spend money to improve
data quality?
Do you mean authority to…
Decide where they
spend money to improve
data quality?
Host data in the cloud?
Remove data?
Yes!
Define
their own
data API /
Digital
Twin?
#3
Move the data authority
(decisions) to where the
knowledge (information) is
∝
So how does this work? Customers?
This node is reserved for a small g “god”
It is not IT, or Tech or info sys
These are not customers;
If they can’t buy from anywhere
else but the central god team
In a network, everyone is a customer, and a supplier
This is a data customer
This is a data customer
This is a data customer
#4
Real customers means
real choices and options
∝
Solving for Data Owners simplifies Data Governance 2x+
Beware Conway's law
Source: Conway’s Law
https://www.thoughtworks.com/insights/articles/
demystifying-conways-law
“Any organisation that designs a [data]
system will inevitably produce a design
whose structure is a copy of the
organization’s structure”
Data about … HR
Data about … People
Briefly – Bringing this talk together through data owners
• Don’t get caught up in titles (custodians, stewards, managers, trustees)
– Figure out who is the Data Owners
• Data ownership is MECE! (Mutually Exclusive, Completely Exhaustive)
– Stacks like Russian dolls
– Can split hierarchy, or by region.
– Guaranteed to show org design gaps / grey areas of political risk
– 100% of data covered does not mean 100% of data is important data
• Authority flows down to knowledge
– Risk flows up to senior leaders
• Data Owners are not demi-gods
– They rely on services from Security, IT, Cost accounting and data
cleansing/analytics
• Data Owners are accountable for the data quality of their important data
#5
Data Owners are MECE
∝
Questions∝

More Related Content

What's hot

Mgi the-age-of-analytics-full-report
Mgi the-age-of-analytics-full-reportMgi the-age-of-analytics-full-report
Mgi the-age-of-analytics-full-report
Pip Barton
 
Summary of Insights Learned from the Data Science Program Team Training
Summary of Insights Learned from the Data Science Program Team TrainingSummary of Insights Learned from the Data Science Program Team Training
Summary of Insights Learned from the Data Science Program Team Training
Fred Chiang
 
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making
DigitYser
 
Action Intelligence for Social Good
Action Intelligence for Social GoodAction Intelligence for Social Good
Action Intelligence for Social Good
Fred Chiang
 
Building a Data Driven Organization
Building a Data Driven OrganizationBuilding a Data Driven Organization
Building a Data Driven Organization
IT Weekend
 
The Business of Big Data - IA Ventures
The Business of Big Data - IA VenturesThe Business of Big Data - IA Ventures
The Business of Big Data - IA Ventures
Ben Siscovick
 
7 Big Data Challenges and How to Overcome Them
7 Big Data Challenges and How to Overcome Them7 Big Data Challenges and How to Overcome Them
7 Big Data Challenges and How to Overcome Them
Qubole
 
Trends in Big Data & Business Challenges
Trends in Big Data & Business Challenges   Trends in Big Data & Business Challenges
Trends in Big Data & Business Challenges
Experian_US
 
Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...
Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...
Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...
Chief Analytics Officer Forum
 
Don't Forget the 'H' in HR: Ethics, Trust & People Analytics
Don't Forget the 'H' in HR: Ethics, Trust & People AnalyticsDon't Forget the 'H' in HR: Ethics, Trust & People Analytics
Don't Forget the 'H' in HR: Ethics, Trust & People Analytics
David Green
 
The Strategic CFO
The Strategic CFO The Strategic CFO
The Strategic CFO
State Street
 
"Big Data Dreams"
"Big Data Dreams""Big Data Dreams"
"Big Data Dreams"
Michael DeAloia
 
Big, small or just complex data?
Big, small or just complex data?Big, small or just complex data?
Big, small or just complex data?
panoratio
 
Creating a Data-Driven Organization -- thisismetis meetup
Creating a Data-Driven Organization -- thisismetis meetupCreating a Data-Driven Organization -- thisismetis meetup
Creating a Data-Driven Organization -- thisismetis meetup
Carl Anderson
 
Being a Data Driven Business
Being a Data Driven Business Being a Data Driven Business
Being a Data Driven Business
Ali Sarrafi
 
Democratizing Big Data (Updated)
Democratizing Big Data (Updated)Democratizing Big Data (Updated)
Democratizing Big Data (Updated)
Jeff Kelly
 
Democratizing Big Data
Democratizing Big DataDemocratizing Big Data
Democratizing Big Data
Jeff Kelly
 
Ocient Preso
Ocient PresoOcient Preso
Ocient Preso
IanBertram5
 
10 Enterprise Analytics Trends to Watch in 2020
10 Enterprise Analytics Trends to Watch in 202010 Enterprise Analytics Trends to Watch in 2020
10 Enterprise Analytics Trends to Watch in 2020
MicroStrategy
 
The Route to Real-Time Business
The Route to Real-Time BusinessThe Route to Real-Time Business
The Route to Real-Time Business
SAP Technology
 

What's hot (20)

Mgi the-age-of-analytics-full-report
Mgi the-age-of-analytics-full-reportMgi the-age-of-analytics-full-report
Mgi the-age-of-analytics-full-report
 
Summary of Insights Learned from the Data Science Program Team Training
Summary of Insights Learned from the Data Science Program Team TrainingSummary of Insights Learned from the Data Science Program Team Training
Summary of Insights Learned from the Data Science Program Team Training
 
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making
DISUMMIT Keynote presentation from Kirk Borne - From Sensors to Sense-Making
 
Action Intelligence for Social Good
Action Intelligence for Social GoodAction Intelligence for Social Good
Action Intelligence for Social Good
 
Building a Data Driven Organization
Building a Data Driven OrganizationBuilding a Data Driven Organization
Building a Data Driven Organization
 
The Business of Big Data - IA Ventures
The Business of Big Data - IA VenturesThe Business of Big Data - IA Ventures
The Business of Big Data - IA Ventures
 
7 Big Data Challenges and How to Overcome Them
7 Big Data Challenges and How to Overcome Them7 Big Data Challenges and How to Overcome Them
7 Big Data Challenges and How to Overcome Them
 
Trends in Big Data & Business Challenges
Trends in Big Data & Business Challenges   Trends in Big Data & Business Challenges
Trends in Big Data & Business Challenges
 
Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...
Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...
Walt Disney presentation at the Chief Analytics Officer Forum East Coast USA ...
 
Don't Forget the 'H' in HR: Ethics, Trust & People Analytics
Don't Forget the 'H' in HR: Ethics, Trust & People AnalyticsDon't Forget the 'H' in HR: Ethics, Trust & People Analytics
Don't Forget the 'H' in HR: Ethics, Trust & People Analytics
 
The Strategic CFO
The Strategic CFO The Strategic CFO
The Strategic CFO
 
"Big Data Dreams"
"Big Data Dreams""Big Data Dreams"
"Big Data Dreams"
 
Big, small or just complex data?
Big, small or just complex data?Big, small or just complex data?
Big, small or just complex data?
 
Creating a Data-Driven Organization -- thisismetis meetup
Creating a Data-Driven Organization -- thisismetis meetupCreating a Data-Driven Organization -- thisismetis meetup
Creating a Data-Driven Organization -- thisismetis meetup
 
Being a Data Driven Business
Being a Data Driven Business Being a Data Driven Business
Being a Data Driven Business
 
Democratizing Big Data (Updated)
Democratizing Big Data (Updated)Democratizing Big Data (Updated)
Democratizing Big Data (Updated)
 
Democratizing Big Data
Democratizing Big DataDemocratizing Big Data
Democratizing Big Data
 
Ocient Preso
Ocient PresoOcient Preso
Ocient Preso
 
10 Enterprise Analytics Trends to Watch in 2020
10 Enterprise Analytics Trends to Watch in 202010 Enterprise Analytics Trends to Watch in 2020
10 Enterprise Analytics Trends to Watch in 2020
 
The Route to Real-Time Business
The Route to Real-Time BusinessThe Route to Real-Time Business
The Route to Real-Time Business
 

Similar to Why CxOs care about Data Governance; the roadblock to digital mastery

Thinkful DC - Intro to Data Science
Thinkful DC - Intro to Data Science Thinkful DC - Intro to Data Science
Thinkful DC - Intro to Data Science
TJ Stalcup
 
Intro to Data Science
Intro to Data ScienceIntro to Data Science
Intro to Data Science
TJ Stalcup
 
Using Data Riches A tale of two projects - Ajay Vinze
Using Data Riches A tale of two projects - Ajay VinzeUsing Data Riches A tale of two projects - Ajay Vinze
Using Data Riches A tale of two projects - Ajay Vinze
Institute of Contemporary Sciences
 
2017 06-14-getting started with data science
2017 06-14-getting started with data science2017 06-14-getting started with data science
2017 06-14-getting started with data science
Thinkful
 
The value of our data
The value of our dataThe value of our data
The value of our data
EnterpriseGRC Solutions, Inc.
 
Data mining with big data implementation
Data mining with big data implementationData mining with big data implementation
Data mining with big data implementation
Sandip Tipayle Patil
 
Connecting and Exploiting Big Data
Connecting and Exploiting Big DataConnecting and Exploiting Big Data
Connecting and Exploiting Big Data
R A Akerkar
 
Big Data for the Next Big Idea in Financial Services (Whitepaper)
Big Data for the Next Big Idea in Financial Services (Whitepaper)Big Data for the Next Big Idea in Financial Services (Whitepaper)
Big Data for the Next Big Idea in Financial Services (Whitepaper)
NAFCU Services Corporation
 
The Bigger They Are The Harder They Fall
The Bigger They Are The Harder They FallThe Bigger They Are The Harder They Fall
The Bigger They Are The Harder They Fall
Trillium Software
 
The cycle of data
The cycle of dataThe cycle of data
The cycle of data
José Luis Valdivielso
 
Data2030 Summit Data Megatrends Turner Sept 2022.pptx
Data2030 Summit Data Megatrends Turner Sept 2022.pptxData2030 Summit Data Megatrends Turner Sept 2022.pptx
Data2030 Summit Data Megatrends Turner Sept 2022.pptx
Matt Turner
 
Data Structure and Types
Data Structure and TypesData Structure and Types
Data Structure and Types
Anjani Phuyal
 
Big Data in Practice.pdf
Big Data in Practice.pdfBig Data in Practice.pdf
Big Data in Practice.pdf
Tom Tan
 
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...
Cloudera, Inc.
 
Data Mining With Big Data
Data Mining With Big DataData Mining With Big Data
Data Mining With Big Data
Muhammad Rumman Islam Nur
 
Overview of mit sloan case study on ge data and analytics initiative titled g...
Overview of mit sloan case study on ge data and analytics initiative titled g...Overview of mit sloan case study on ge data and analytics initiative titled g...
Overview of mit sloan case study on ge data and analytics initiative titled g...
Gregg Barrett
 
Data Science and Decision Making
Data Science and Decision MakingData Science and Decision Making
Data Science and Decision Making
Luciano Vilas Boas
 
Big data
Big dataBig data
Big data
promediakw
 
Integrate Big Data into Your Organization with Informatica and Perficient
Integrate Big Data into Your Organization with Informatica and PerficientIntegrate Big Data into Your Organization with Informatica and Perficient
Integrate Big Data into Your Organization with Informatica and Perficient
Perficient, Inc.
 
Big data introduction, Hadoop in details
Big data introduction, Hadoop in detailsBig data introduction, Hadoop in details
Big data introduction, Hadoop in details
Mahmoud Yassin
 

Similar to Why CxOs care about Data Governance; the roadblock to digital mastery (20)

Thinkful DC - Intro to Data Science
Thinkful DC - Intro to Data Science Thinkful DC - Intro to Data Science
Thinkful DC - Intro to Data Science
 
Intro to Data Science
Intro to Data ScienceIntro to Data Science
Intro to Data Science
 
Using Data Riches A tale of two projects - Ajay Vinze
Using Data Riches A tale of two projects - Ajay VinzeUsing Data Riches A tale of two projects - Ajay Vinze
Using Data Riches A tale of two projects - Ajay Vinze
 
2017 06-14-getting started with data science
2017 06-14-getting started with data science2017 06-14-getting started with data science
2017 06-14-getting started with data science
 
The value of our data
The value of our dataThe value of our data
The value of our data
 
Data mining with big data implementation
Data mining with big data implementationData mining with big data implementation
Data mining with big data implementation
 
Connecting and Exploiting Big Data
Connecting and Exploiting Big DataConnecting and Exploiting Big Data
Connecting and Exploiting Big Data
 
Big Data for the Next Big Idea in Financial Services (Whitepaper)
Big Data for the Next Big Idea in Financial Services (Whitepaper)Big Data for the Next Big Idea in Financial Services (Whitepaper)
Big Data for the Next Big Idea in Financial Services (Whitepaper)
 
The Bigger They Are The Harder They Fall
The Bigger They Are The Harder They FallThe Bigger They Are The Harder They Fall
The Bigger They Are The Harder They Fall
 
The cycle of data
The cycle of dataThe cycle of data
The cycle of data
 
Data2030 Summit Data Megatrends Turner Sept 2022.pptx
Data2030 Summit Data Megatrends Turner Sept 2022.pptxData2030 Summit Data Megatrends Turner Sept 2022.pptx
Data2030 Summit Data Megatrends Turner Sept 2022.pptx
 
Data Structure and Types
Data Structure and TypesData Structure and Types
Data Structure and Types
 
Big Data in Practice.pdf
Big Data in Practice.pdfBig Data in Practice.pdf
Big Data in Practice.pdf
 
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...
 
Data Mining With Big Data
Data Mining With Big DataData Mining With Big Data
Data Mining With Big Data
 
Overview of mit sloan case study on ge data and analytics initiative titled g...
Overview of mit sloan case study on ge data and analytics initiative titled g...Overview of mit sloan case study on ge data and analytics initiative titled g...
Overview of mit sloan case study on ge data and analytics initiative titled g...
 
Data Science and Decision Making
Data Science and Decision MakingData Science and Decision Making
Data Science and Decision Making
 
Big data
Big dataBig data
Big data
 
Integrate Big Data into Your Organization with Informatica and Perficient
Integrate Big Data into Your Organization with Informatica and PerficientIntegrate Big Data into Your Organization with Informatica and Perficient
Integrate Big Data into Your Organization with Informatica and Perficient
 
Big data introduction, Hadoop in details
Big data introduction, Hadoop in detailsBig data introduction, Hadoop in details
Big data introduction, Hadoop in details
 

Recently uploaded

一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
aqzctr7x
 
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business PlanInfluence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
jerlynmaetalle
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Sm321
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Timothy Spann
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Bill641377
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
apvysm8
 
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series DatabaseThe Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
javier ramirez
 
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
74nqk8xf
 
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performanceAnalysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
roli9797
 
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.
rwarrenll
 
Everything you wanted to know about LIHTC
Everything you wanted to know about LIHTCEverything you wanted to know about LIHTC
Everything you wanted to know about LIHTC
Roger Valdez
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
bopyb
 
The Ipsos - AI - Monitor 2024 Report.pdf
The  Ipsos - AI - Monitor 2024 Report.pdfThe  Ipsos - AI - Monitor 2024 Report.pdf
The Ipsos - AI - Monitor 2024 Report.pdf
Social Samosa
 
Learn SQL from basic queries to Advance queries
Learn SQL from basic queries to Advance queriesLearn SQL from basic queries to Advance queries
Learn SQL from basic queries to Advance queries
manishkhaire30
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Walaa Eldin Moustafa
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Lars Albertsson
 
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Timothy Spann
 
State of Artificial intelligence Report 2023
State of Artificial intelligence Report 2023State of Artificial intelligence Report 2023
State of Artificial intelligence Report 2023
kuntobimo2016
 
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
Social Samosa
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Aggregage
 

Recently uploaded (20)

一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
 
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business PlanInfluence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
 
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series DatabaseThe Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
 
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
 
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performanceAnalysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
 
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.
 
Everything you wanted to know about LIHTC
Everything you wanted to know about LIHTCEverything you wanted to know about LIHTC
Everything you wanted to know about LIHTC
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
 
The Ipsos - AI - Monitor 2024 Report.pdf
The  Ipsos - AI - Monitor 2024 Report.pdfThe  Ipsos - AI - Monitor 2024 Report.pdf
The Ipsos - AI - Monitor 2024 Report.pdf
 
Learn SQL from basic queries to Advance queries
Learn SQL from basic queries to Advance queriesLearn SQL from basic queries to Advance queries
Learn SQL from basic queries to Advance queries
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
 
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
 
State of Artificial intelligence Report 2023
State of Artificial intelligence Report 2023State of Artificial intelligence Report 2023
State of Artificial intelligence Report 2023
 
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
 

Why CxOs care about Data Governance; the roadblock to digital mastery

  • 1. Data Champions, Online Data Governance for the CxO in the Digital Era. Strategies that remove friction from organizational data flow in large organisations with Coert du Plessis, DataAlchemists, +61406313111 @coertdup June 2020 DataAlchemists
  • 2. Still Chasing Master Data in 2020 is like…
  • 3. Exponential growth in processing = growth in data Source: IDC Research, sponsored by Seagate https://www.seagate.com/files/w ww-content/our- story/trends/files/idc-seagate- dataage-whitepaper.pdf Singularity Hubb (Ray Kurzweil/ Moore’s law) https://singularityhub.com/2016/03/08/will- the-end-of-moores-law-halt-computings- exponential-rise/ IDC predicts that the collective sum of the world’s data will grow from 33 zettabytes this year to a 175ZB by 2025, for a compounded annual growth rate of 61 percent. Ray’s doubling of compute power/ $ Your list of $ budget demands
  • 4. #1 We have important data and not important data ∝
  • 5. Big corporate isn’t ready for 2x data explosion If you bravely lift the hood on large corporate data sourcing and integration today, it looks like spaghetti Macgyvered together with a swiss army knife, wire and Blu Tack.
  • 6. We don’t want Macgyvered Data; We want the internet
  • 7. API Economy is fundamental to automation – a network API Economy: From traditional data “stack” to data democracy
  • 8. People Machine Gate Access Training Data 1 Data A Data II Data 2F Networks are built on identities – node by node API Economy: From traditional data “stack” to data democracy
  • 9. #2 Data is a network of identities ∝
  • 10. So if the data is a network, how do we organise people?
  • 11. So if the data is a network, how do we organise people? Centralised teams Decentralised teams
  • 12. What if we organised our people like our data, distributed? Distributed teams
  • 13. We are used to moving information to authority Source: L. David Marquette, Author of “Turn the ship around”
  • 14. We are used to moving information to authority Source: L. David Marquette, Author of “Turn the ship around”
  • 15. We need to move authority to where the information is Decisions
  • 16. … you lead by retaining the risk accountability Decisions Risk
  • 17. Case study using some AI generated faces Source: AI Generated faces https://thispersondoesnotexist.com Business Analyst Improvement CIPO Chief Improve ment and Projects Officer
  • 18. Case study using some AI generated faces Source: AI Generated faces https://thispersondoesnotexist.com Business Analyst Improvement CIPO Chief Improve ment and Projects Officer Principal Data Warehouse
  • 19. Case study using some AI generated faces Source: AI Generated faces https://thispersondoesnotexist.com Business Analyst Improvement CIPO Chief Improve ment and Projects Officer Principal Data Warehouse Procurement Analyst HR consultant
  • 20. Case study using some AI generated faces Source: AI Generated faces https://thispersondoesnotexist.com Business Analyst Improvement CIPO Chief Improve ment and Projects Officer Principal Data Warehouse CIO Chief Informati on Officer Procurement Analyst CPO Chief Procure ment Officer HR consultant CPO Chief People Officer
  • 21. Authority to… Yes! Decide where they spend money to improve data quality?
  • 22. Do you mean authority to… Yes! Host data in the cloud? Decide where they spend money to improve data quality?
  • 23. Do you mean authority to… Host data in the cloud? Remove data? Yes! Decide where they spend money to improve data quality?
  • 24. Do you mean authority to… Decide where they spend money to improve data quality? Host data in the cloud? Remove data? Yes! Define their own data API / Digital Twin?
  • 25. #3 Move the data authority (decisions) to where the knowledge (information) is ∝
  • 26. So how does this work? Customers? This node is reserved for a small g “god” It is not IT, or Tech or info sys These are not customers; If they can’t buy from anywhere else but the central god team
  • 27. In a network, everyone is a customer, and a supplier This is a data customer This is a data customer This is a data customer
  • 28. #4 Real customers means real choices and options ∝
  • 29. Solving for Data Owners simplifies Data Governance 2x+
  • 30.
  • 31.
  • 32.
  • 33. Beware Conway's law Source: Conway’s Law https://www.thoughtworks.com/insights/articles/ demystifying-conways-law “Any organisation that designs a [data] system will inevitably produce a design whose structure is a copy of the organization’s structure”
  • 35. Data about … People
  • 36. Briefly – Bringing this talk together through data owners • Don’t get caught up in titles (custodians, stewards, managers, trustees) – Figure out who is the Data Owners • Data ownership is MECE! (Mutually Exclusive, Completely Exhaustive) – Stacks like Russian dolls – Can split hierarchy, or by region. – Guaranteed to show org design gaps / grey areas of political risk – 100% of data covered does not mean 100% of data is important data • Authority flows down to knowledge – Risk flows up to senior leaders • Data Owners are not demi-gods – They rely on services from Security, IT, Cost accounting and data cleansing/analytics • Data Owners are accountable for the data quality of their important data
  • 37. #5 Data Owners are MECE ∝

Editor's Notes

  1. A topic that is crucial to the automated and data future
  2. BHP 1SAP chase.
  3. Your budget is static or slowly linear in growth. You will NEVER have enough money The real rub is my master data is just slow moving transaction data… my last name, my address, my gender… BHP a truck is not a truck, different TURBO, Tray
  4. And someone has to decide which is important data.
  5. Linked by identities – People, Machines and processes
  6. Linked by identities – People, Machines and processes It is not Identity and Access Management – whilst they go well together, the one part is 100% a security job, and the other, is 100% a strategic organisation and data governance decision. Identity is so much more than a secure tool … The internet DNS is the identity management equivalent of your data…
  7. Risk moves up. In fact, if you don’t know who owns data, just check who owns the risk. BHP had a reasonably well publicized issue with a 3rd party payroll provider mixing up a handful of payslips. That risk was born by the Chief People Officer.
  8. Headcount report
  9. Lower the friction, increase the speed of decisions and data use and lift the value and employee satisfaction.
  10. This shot from Apollo 8, 1968 – Nature has no borders. Started the environmental movement and earth day…. Data is the same.
  11. Erase the artificial organisational borders, and look at the substance… the ocean, the green the desert the mountains and the snow.
  12. Russian dolls, by region or by hierarchy
  13. If you don’t know who owns the data, just imagine who would own the catastrophic data exposure or error risk.
  14. E.g. in BHP Engineering is centralised, but the reality is it is run regionally.
  15. And cascade to the knowledge / information.