The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutureofIT)

© All Intellectual Rights Reserved 2018 Inpuls cvba
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THE FUTURE IS DATA-CENTRIC,
unlocking the potential of
compliant/trusted
value creation
1
JAN HENDERYCKX
INFORMATION STRATEGIST
AVOIDING THE #FAKEDECISION PITFALLS

DISCLAIMER
2
EXAMPLES
MAY LOOK FAMILIAR

4
Managing Partner, Senior Advisor and Trainer with Inpuls cvba
Publications
Database Magazine, IDUG journal, CA journal, BMC journal, Information
Seminars and workshops
IT Works, Adept Events, IRMUK
Involvement in non-profit initiatives
§ President of DAMA Belux Chapter (http://dama-belux.org)
JAN HENDERYCKX
YOUR PRESENTER
CDMP,DGSP
TWITTER/JANHENDERYCKX
/INPULS_INFO
LINKEDIN/JANHENDERYCKX
YOUTUBE INPULS CHANNEL
Making organisations Data Centric

AGENDA 9
• WELCOME
• SETTING THE SCENE
• EFFECTIVE INFORMATION STRATEGIES
• TOWARDS A POLICY BASED APPROACH
• REFERENCE ARCHITECTURE FOR (BIG) DATA GOVERNANCE
• CONCLUSIONS

FROM: DATA MART TO INSIGHT TO ACTION
SETTING THE SCENE DATA PROPULSED? 10
Object
Events
M EASURE
NEXT BEST
ACTION
A CT
Object
Events
Events
Events
U NDERSTAND
SUSTAINABLE TRUSTED INSIGHT?

SETTING THE SCENE 11
What is the definition of “right”

CAN DATA LEAD TO ACTION? 12
12
Value creation
by detecting potential high LTV customers
from sales transactions

CAN DATA LEAD TO ACTION? 13

CAN DATA LEAD TO ACTION? PRACTICAL EXAMPLES FROM THE FIELD 14

Clayton M. Christensen
Harvard Business Review Press; 1st edition (May 1, 1997)
Disruptive innovation
is innovation that creates a new market
and value network and eventually
disrupts an existing market and value
network, displacing established market
leaders and alliances.
The term was defined and phenomenon
analyzed by Clayton M. Christensen
beginning in 1995.
THE DYNAMICS OF DISRUPTION

Non-linear correlation
between investment/effort
and revenue

THE DYNAMICS OF
DISRUPTION
© Rachel Botsman
?

THE FLIPSIDE OF THE COIN CAN DATA CAUSE PROBLEMS? 18
Shares down 1,8%
Agenda
9

CAN DATA CAUSE PROBLEMS?THE FLIPSIDE OF THE COIN
19

THE FLIPSIDE OF THE COIN CAN DATA CAUSE PROBLEMS? 20

21WHY BOTHER CYBER SECURITY / DATA PROTECTION = HOT TOPIC

22WHAT IS NEW? PRIVACY = HOT TOPIC
Current privacy
legislation
No strong
enforcement or
infringements
New Rights for
individuals
New Obligations
for companies
Stronger
enforcement of
infringements
Right to be forgotten
Data portability right
Protection of children
profiling
Location data
Explicit consent
Data protection
assessments
Documentation
requirements
Data protection officer
Data breach notification
Processor obligations
Privacy by default & by
design
Heavy sanctions
Shift burden of evidence
Stronger agencies
accountability
Territorial data transfers
Class actions

INCREASING REGULATORY PRESSURE 23
Regulators move from
reports to data points!

SOME HISTORIC
PERSPECTIVE
Wall Street's Speed War High-Frequency Trading
SPEED IS RELATIVE
1964
New York Stock Exchange installs IBM
computer system; automated quotations
appear in 1965.
1990
Datek introduces “the Watcher,” a PC-based program
that capitalizes on split-second discrepancies in the big
exchanges’ small-order trading systems. Anation of day
traders is born.

SETTING THE SCENE ACTING ON DATA 25

SETTING THE SCENE ACTING ON DATA 26

FINTECH AND DATA SCIENCE
OFFER NEW (DISRUPTIVE) OPPORTUNITIES ENHANCING PERFORMANCE AND ENABLING COSTS REDUCTION 27

SETTING THE SCENE RESULT ORIENTED? 28
Value from data is a VERB
The key is not to OWN but to ACT

Data Excellence
Ability to Measure
Ability to Get Insight
Ability to EngageAbility to Capture
Ability to make
trusted decision
Ability to Sustain
Ability to Comply
SETTING THE SCENES CURRENT CHALLENGES 29
Short time-to-market of change
requests (reduce backlog)
Insight into available data
across the organisation
Easy access to available data
Leveraging Data & Analytics
skills and practices
Easy access to reliable metadata
Insight in
data lineage and data usage
Adequate (scope of) governance
on data & info products
Advance daily of
information delivery
Insight into Data Quality
in relation to its purpose
Fact:
Automation has lead to reasonably
mature administrative data and
functions
But
Support for data propulsed automated
decision taking and transversal data
usage remains challenging

AGENDA 30
• WELCOME
• CONCLUSIONS

What is effective?
BEING DATA PROPULSED
IBM IBV 2013

What’s the bottleneck?
BEING DATA PROPULSED

WHAT’S THE EFFECTIVE? 33
GOVERNING
INNOVATION AND
VALUE CREATION
UNDERPIN YOUR
INFORMATION
STRATEGY
WITH THE
PROPER
CAPABILITIES
EMBED
THE CHANGE
IN THE
ORGANISATION

ELEMENTS THAT
CAN SCALE NON-LINEAR
Capture data Run AlgorithmStore the data
INFORMATION STRATEGY ELEMENTS
Use the data

CAPTURE DATA DO WE HAVE THE DATA? 35
Ability to Measure
AAbility to Capture
Ability to make
trusted decision
Ability to Sustain
Ability to Comply
I Think
Do I have
the data to
back up my
decision?

CAPTURE DATA DO I HAVE ENOUGH CONTEXT 36
CONTEXT MATTERS

USE THE DATA 37
Focus on the quality of your small data
You can’t “statistical relevant”
yourself out of the quality of
master and reference data.
Can we do self-service if
we can’t trust the data?

SETTING THE SCENE DIGITAL TWIN 38
Really don’t need new slippers

SETTING THE SCENE DIGITAL TWIN 39
Expected LTV
10.000 €
Real LTV x €

THE DATA, INFORMATION AND INSIGHT SPACE 40
Self Service insight
will fail
without proper
Information – and
data governance
Gartner Says,
By 2018, Half of Business
Ethics Violations Will Occur
Through Improper Use of Big
Data Analytics
COMPLIANCY
COMMITMENT &
TRANSPARENCY

STORE THE DATA 41
Diversify your
data management platform:
Value is not always proportional to value
But how many tools do we
really need?

GOVERNANCE
MODEL
Be cost effective
GOVERNANCE MODEL
Are we putting the right
person on the right job?

Data Analytics
Data Scientist
Ad-hoc Data Sources
Data
Exploration
Area
Data Integration
Governed Data Sources
Information/Insight
visualisation
Put the TOOLS and PROCESSES in place to allow
people to be EFFECTIVE and EFFICIENT
GOVERNANCE MODEL

GOVERNANCE
MODEL
Stay true to your values
GOVERNANCE MODEL
Are we going to
monetize the
data?

GOVERNANCE
MODEL
Is MYdata
special?
AGILE DATA GOVERNANCE MODEL
How much common ground do we need
between producer and consumer?
Can we survive with only SCHEMA ON READ?

GOVERNANCE
MODEL
INNovate
and
INDustrialise
GOVERNANCE MODEL
Is there any value if only a
small percentage of your
company can apply the
outcome?

EFFECTIVE INFORMATION STRATEGIES IDENTIFY THE GAPS 47
47
Is the
information
correct?
Do we have
the data?
Are we allowed
to use the
data?
Can we use
the
information?
Data Excellence
Ability to Measure
Ability to EngageAbility to Capture
Ability to make
trusted decision
Ability to Sustain
Ability to Comply
Short time-to-market of change
requests (reduce backlog)
Insight into available data
across the organisation
Easy access to available data
Leveraging Data & Analytics
skills and practices
Easy access to reliable metadata
Insight in
data lineage and data usage
Adequate (scope of) governance on
data & info products
Advance daily of
information delivery
Insight into Data Quality
in relation to its purpose

EFFECTIVE INFORMATION STRATEGIES APPROACH 48
Business
Capability
What should we be able
to do? Process
Business
Product
How do we create
business value Outcome
Information
Domain
What information is
required?
Information
How will we steer
the process
Insight
GAPS ?
CONNECTED
CUSTOMERS
CUSTOMER
CENTRICITY
COMPLIANCY
COMMITMENT &
TRANSPARENCY

AGENDA 49
• WELCOME
• CONCLUSIONS

50
Governance

51
Minimum Viable Protection
Policy
Most regulations apply to a specific type of data!
Governance

COLLECT
USE
IMPACT ON VALUE CHAIN PRODUCER AND CONSUMER MODELS
52
PRODUCER CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Ingestion Usage

PRODUCER AND CONSUMER MODELS
• Can they be loosely coupled?
IMPACT ON VALUE CHAIN
53
DATA POINT
Request
Contract
Ingestion
?
Consent
Lawfulness
Send
Marketing
Message
?
Lawfulness
Contractual necessity
CONSUMER
Send
Signed
Contract
Usage
?

PRODUCER AND CONSUMER MODELS
• Can Schema on Read be applied without Policy on Ingest?
IMPACT ON VALUE CHAIN
54
Policy on
Read?
Policy on
Ingest? DATA POINT
DATA POINT
Need to assure sufficient metadata to apply the policies is captured at ingestion
Need to have
minimal schema
to understand
the applicability
of the policies
DATA POINT
DATA POINT

55
PRODUCER CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Ingestion Usage
CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Usage
PRODUCER
CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Usage
PRODUCER
Need to manage the
entire supply chain,
not just the first step

56
Chief Data Officer Collateralized Debt Obligation
PRODUCER CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Ingestion Usage
CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Usage
PRODUCER
CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Usage
PRODUCER
CDO

AGENDA 57
• WELCOME
• CONCLUSIONS

ARCHITECTURAL REQUIREMENTS AND CONTROLS
58
Need to move from ASSET centric
to
DATA centric
DCAP

ARCHITECTURAL REQUIREMENTS
AND CONTROLS
59
COLLECT
STORE
USEPREPARE ANALYSE
Create Value

IMPACT ON VALUE CHAIN COLLECT, STORE, ANALYSE AND USE
COLLECT
STORE
ANALYSE
USE
PREPARE
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For What Purpose?
Do we really need this?
Does the data subject agree
to the processing?
Is the data correct?
Preparation =
processing
How long can we keep the data?
Can I trace back the origin?
Can I link to the context?
For What Purpose?
Does the data subject
agree to the processing?
Is the data correct?
Is the processor applying
suitable safeguards?
Does the data stay in the
EER?
Do we have consent?
Is the data still PII?
….
Can we apply
profiling?
Is there BIAS in my
model?
Is the data S-PII

PRIVACY ENHANCING AND/OR RISK REDUCING SOLUTIONSARCHITECTURAL REQUIREMENTS AND CONTROLS
61
Share External
Delete
Use
Serve
Archive
Access
External
Attacker
Attacker,
Insider
Store
Policy
Anonymise
Data
Receiver
Pseudononymise
Encrypt
Share External
Delete
Use
Serve
Archive
Access
External
Attacker
Attacker,
Insider
Store
Policy
Anonymise
Data
Receiver
Pseudononymise
Encrypt
Data Masking
Data Access
Pseudonymize
Encrypt
Anonymise

POLICY ON INGESTARCHITECTURAL REQUIREMENTS AND CONTROLS
62
DCAP: What
data am I
ingesting?
COLLECT
ISO 27000
Classify
Label

63

ARCHITECTURAL REQUIREMENTS AND
CONTROLS 64

65

AND CONTROLS
66
COLLECT
STORE
USEPREPARE ANALYSE
PRODUCER CONSUMERDATA POINT
Ingestion
Terms & Conditions
DATA SHARING
AGREEMENT
DATA POINT
Ingestion
Terms & Conditions
DATA SHARING
AGREEMENT
PRODUCER
What Metadata
should I capture?
What quality rules should be
applied ?
PRODUCER CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Ingestion Usage
CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Usage
PRODUCER
CONSUMER
DATA SHARING
AGREEMENT
DATA POINT
Usage
PRODUCER

AND CONTROLS
67
STORE
PREPARE
Provision
PII?
Purpose Specific Marts?
Anonymous Data?
Share External
Delete
Use
Serve
Archive
Access
External
Attacker
Attacker,
Insider
Store
Policy
Anonymise
Data
Receiver
Pseudononymise
Encrypt
Pseudonymize
Encrypt
Anonymise
DCAP:
How do we
reduce the risk?

AND CONTROLS
68
USEANALYSE
GDPR: Art. 30
Self-Service
For What Purpose?
Does the data subject
agree to the processing?
Ability to Filter records based
on Processing grounds?
Purpose Specific Marts?
Anonymous Data?
Include Consent in Data
Access Layer?
Data Masking
Data Access

AGENDA 69
• WELCOME
• CONCLUSIONS

CONCLUSION
70

•Use Policy based approach
•Make the distinction between data- and information
governance
•Make the policy executable through capabilities
•Have the proper metadata architecture to support
the approach
•Work risk based
CONCLUSIONS
71

72
Sustainable Information Readiness
INFORMATION READINESS
Gather Serve Dispose
Maintain
Govern
Steer
This Information is safe to
run processes
Define
This Information is safe
to take decisions
Industrialise
Refine ValidateProvision Define Hypothesis testing
Governance

73
Information Governance
Information and Sustainable insight
at your fingertips, of the right quality, at the right time,
in the right context, using the right form, for the right person
This
Information
is safe to
take
decisions
This
Information
is safe to
run
processes
Operational
Insight
INFORMATION
READINESS
Information
Data
Informatie
Analyse
Inzicht Creatie
Performance Management
Hypothese Toetsing
Budgetting & Forecasting
Metrics & Scorecards
Purchasing
Order Mgt.
Vendor
After Sales
Customer
Conclusion

74
Q & A SESSION
WE’LL BE ANSWERING QUESTIONS NOW
Q A&
THANKS FOR LISTENING
YOUTUBE INPULS
CHANNEL
TWITTER/INPULS_INFO
T +32 3 443 17 43
F +32 3 443 17 49
INFO@INPULS.EU
DUWIJCKSTRAAT 17,
2500 LIER
BELGIUM
LINKEDIN/INPULS

75

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www.itworks.be
Presented at:

The Future of IT
20 September 2018 in Brussels

The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutureofIT)

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