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THE DATA REVOLUTION CAN BE VITALIZING OR PERILOUS:DID YOU TAKE THE RIGHT TURN?Information Quality Strategy Class INFQ 7367 MIT / UALR Master of Science in Information Quality

Pieter De Leenheer, PhDCofounder & VP, Research and Education

Sep 28, 2016

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Administration

What do the companies in these groups have in common [1]?• Group A: American Motors, Brown Shoe, Studebaker,

Collins Radio, Detroit Steel, Zenith Electronics, and National Sugar Refining.

• Group B: Boeing, Campbell Soup, General Motors, Kellogg, Procter and Gamble, Deere, IBM and Whirlpool.

• Group C: Facebook, eBay, Home Depot, Microsoft, Office Depot and Target.

Conclusion

• only 12.2% of the Fortune 500 companies in 1955 were still on the list 59 years later in 2014

• life expectancy of a firm in the Fortune 500 • 50 years ago : ~ 75 years• Today: < 15 years and declining

What happened between 1955 and today that caused this ‘creative destruction’?• Name some compelling events in information technology history• Order them chronologically• Try to explain the phenomenon in terms of the events• E.g.,

• Invention of the transistor• First modern computer• Publication of the Internet protocol• Launch of the World Wide Web• Wikipedia• Facebook• …

“Digital Darwinism”• Disruptive evolution* from analogue to digital business models (examples [8])

• * not “transformation” because this would not take into account the “creative destruction” as inherent part. Hence “evolution”• Selection is driven by network effects

• Direct access to the consumer• Minimizing the middleman / transaction cost• Through aggregators like, e.g., Etsy, Uber, Airbnb, TaskRabbit, Spotify, etc.• Perhaps cut out middleman entirely: blockchain and smart contracts?

• Drive new value from data as• Use real-time feedback to improve the “brand”• Generate second revenue streams from “good” data• From consumers as well as any “thing” (via IoT, smart grid, smart cities, via smart contracts)

• Paradox of the “Big Shift” [3]• Consumers (especially Generation Y and Z, born after ~1982) embrace information sharing for almost every aspect of their life: data citizenship [3]• Yet outdated institutional structures continue to inhibit organizational information flows

• Archaic conceptions of data value and data management (see next)• Not adapted to complex network-centric business environments with improved cloud, big data, and security capabilities [2]

• Soon young data citizens will enter into positions in these institutes and may accelerate the big shift

Paradigm Shift from Value Chain to Value Web• “From being organised around the flow of things and the flow of money, the

economy is being organised around the flow of information” P. Drucker, 1992

Enterprise-centric Network-centric

Hierarchical Decentralized

value-in-exchange value-in-use

Process-driven Relationship-driven

operand resource (goods) operant resource (data, consumer)

marketing push consumer pull

technology data services

customer acquisition customer retention

Rethinking Valuation of Information• Enterprise-centric Paradigm

• Cost of hardware / software to build information system at delivery determines the value-in-exchange* [4]

• Cost of enhancing and maintaining data hidden in user salaries

• Network-centric paradigm• Cost plant and equipment becomes marginal with secure cloud and SaaS, just like electricity

and water• Value-in-use of information derived from wider range of parameters captured by data

governance:• Usage (see next): how many times is the model reused ?• Traceability: How can I trace back the business context for this model?• Lineage: what is the history of transformations for this model?• Ownership: who is accountable/responsible/informed/consulted for this model?• Accuracy: how accurate/complete/timely… is this model?

• (*) Use metaphor• Product = Car = information• Plant = car manufacturing plant = Information system• Would you value the information (car) product in terms of the cost of the IS (plant) alone? No,

in terms of the maintenance and history of the car itself….

Misconceptions of Data Governance that impede Data Valuation [5]• Data governance is a published repository of common definitions. • Data governance is a concern of – and hence managed by – IT.• Data governance is just data quality (DQ) and master data

management (MDM). • Data governance is siloed by business function.• Data governance provides no value or participation for the data-

consuming community.

Towards a New Ways to Value Information that can change Everything [4,11]?• Infinitely Shareable• Value increases with usage• Information is perishable• Value increases with accuracy• Value increases when combined with

other information (20-80 rule)• More is not necessarily better• Information is not deplorable

Examples of information a.k.a. “Models”• A model is a simplified representation of a part of reality on particular moment or extended over a period of

time• Simplified because of assumptions • Assumptions constrain interpretation• Examples of static models representing states of affairs, events:

• A (set of) record(s)• Web page• Analytic (predictive or correlational) model• Your profile exchanged between sites with Single-sign on• Report• Data quality assessment model (applicable rules)• Business Definition• Traceability model

• Examples of dynamic models, representing observation over time periods• Your Facebook wall• A sensor producing temperature measurements in your fridge

Network-centric approach to valuating information• Information is a network of

users• ~ Metcalfe’s Law: value of the

network is n * log(n), where n is number of peers

• Peers are Information ‘models’• Edges represent reuse between

models

Data Governance is anholistic lens on your ever-expanding data universe

Understand & Explain• Commonalities and differences

in models• Provenance of Business

Traceability• Business Data Lineage• Technical Data Lineage

Monitor & Predict based• Onboarding and approval of CDEs• Data Quality Evaluation• Report Certification and

Watermarking• Helpdesk and Issue Management• Data Access and Usage

Agreements• …

Through a Data Collaboration Platform

Danger of the old paradigm models• Weapons of Math Destruction (WMD) are models

• Threaten to destabilize• Equality• Democracy

• Traits of WMDs• Opaque• Unregulated• Uncontestable• …hence : ungoverned

Data Governance Framework• Three Tiers

• DG Operating Model• Stewardship Applications• Integrations

• 1 single platform • N steward applications• Education and Certification

university.collibra.com

https://compass.collibra.com/display/COOK/Collibra+Body+of+Knowledge

BCBS CCAR Demo• Requirements for a model: report, data point row, data quality rule• Used to perform stress tests on a bank• Not a one-time delivery (value-in-exchange) but a continuous

improvement (value-in-use) through report schedules

The Rise of the Chief Data Officer (CD0) [6]

Data governance & stewardship provide the right level of control and trust in data

Data Infrastructure (IT) Data Consumers (Business)

LEADERSHIPCEO, CFO, VP, Marketing

ROLESData Scientist, Business Analyst

TECHNOLOGYVisualization, Self-service BI

NEED

Data Authority

LEADERSHIPCIO

ROLESInformation Manager, Data Architect, Data Modeler

TECHNOLOGYHadoop, Databases, Data Integration

Data Authority

LEADERSHIPChief Data Officer

ROLESData Governance Manager,

Data Steward

TECHNOLOGYData Stewardship

Platform

CDO Roles [6,9]• Collaboration: inwards / outwards• Data Space: traditional data / big

data• Value Impact: service / strategy

• MIT Sloan & Collibra: http://www.iscdo.org/

18 | ©Collibra 2016

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Further Reading• [1]

https://www.aei.org/publication/fortune-500-firms-in-1955-vs-2014-89-are-gone-and-were-all-better-off-because-of-that-dynamic-creative-destruction/

• [2] http://www.informationweek.com/software/information-management/25-data-management-vendors-worth-watching/a/d-id/1326963

• [3] http://dupress.deloitte.com/dup-us-en/topics/emerging-technologies/the-burdens-of-the-past.html• [4] http://si.deis.unical.it/zumpano/2004-2005/PSI/lezione2/ValueOfInformation.pdf• [5] https://www.collibra.com/blog/unleash-the-data-democracy-5-misconceptions-of-data-governance/• [6] https://www.collibra.com/blog/the-rise-of-the-chief-data-officer-cdo/• [7] https://www.weforum.org/agenda/2016/01/digital-disruption-has-only-just-begun/• [8]

http://www.slideshare.net/boardofinnovation/10-business-models-that-rocked-2010-6434921/48-Train_your_team_inBusiness_InnovationHire

• [9] http://www.mitcdoiq.org/wp-content/uploads/2014/01/Lee-et-al.-A-Cubic-Framework-for-the-CDO-MISQE-Forthcoming-2014-copy.pdf

• [10] https://www.collibra.com/blog/5-reasons-to-get-your-data-governance-certification/• [11] http://mitiq.mit.edu/IQIS/Documents/CDOIQS_201177/Papers/05_01_7A-1_Laney.pdf