What happens when data start living their own life?
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What happens when data start living their own life?

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Trying to make sense of the deluge of data, and of decisions based on data.

Trying to make sense of the deluge of data, and of decisions based on data.

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What happens when data start living their own life? What happens when data start living their own life? Presentation Transcript

  • What happens when data start living their own life? Daniel Kaplan [dkaplan@fing.org] Charles Népote [cnepote@fing.org]
  • Data used to be Data used to be ad hoc ad hocconstructions toconstructions to fill variables in fill variables in programs programs Siloed within programs Inconsistent within the organization Highly contextual Maximized Badly maintained Regulated with a focus on processes
  • Data used to be Data used to be ad hoc ad hoc constructions to constructions to fill variables in fill variables in programs, until… programs, until…The digitization of The digitization of A new world of A new world ofdaily life and of thedaily life and of the innovation and innovation and physical world physical world co-opetition co-opetition Natively digital content Open Innovation User-generated content Highly cooperative and complex value chains Traces Loosely coupled Captas organizations/projects/services Web of things A web of APIs and mashups Location… Cross-channel communications
  • The digitization of The digitization of A new world of A new world ofdaily life and of thedaily life and of the innovation and innovation and physical world physical world co-opetition co-opetition Pivots around Pivots around Data take on Data take on Identity // Identitya life of their owna life of their own Services // Services Data Data Produced « just in case » Needs data that are… Open-ended … meaningful & reliable … consistent Mixed and mashed … documented Circulated, lent, sold … linked Infinitely re-used … accessible in highly diverse contexts … cheap … reusable…
  • Pivots around Pivots around Data take on Data take on Identity // Identitya life of their owna life of their own Services // Services Data Data 1st-order consequences Needs A deluge New possibilities infrastructures of data that for [real-time] knowledge (identity/cloud/ requires management, production, semantics/ curation, analyses, decisions, security…) filtering… forecasting…
  • Pivots around Pivots around Data take on Data take on Identity // Identitya life of their owna life of their own Services // Services Data Data 2nd-order consequences Retaining control over Power shifts, Questioning algorithmic decisions Big questions « raw » data
  • 3 Areas for Concrete Applications « Smart » CitiesPersonal Data Open Data
  • The Open Data Transformation
  • Open Data? The Basics Data accessible on the web Machine-readable "Raw"Non-exclusive, non-discriminatory licensing agreements The Advanced Version
  • Driving forces behind Open [Public] Data Economic Institutional ▋ Liberalization ▋ Lack of money ▋ Growth found in ▋ Achieve more with service-based less, produce innovation non-tax revenues▋ "Information wants ▋ Transparency & to be free" participation drive Open Data ▋ Web of data Complexity ▋ ▋ Semantic web ▋ Demand for ▋ Web 2.0 participation & ▋ Web of things empowerment ▋ Open source ▋ Consumerism ▋ Datamining ▋ Low trust in ▋ Dataviz institutionsTechnological Societal
  • A few building blocks Reference docs Government Mapping End-user IT Local data info businesses govts.Observation "Grey" Other Public data Data docs businesses Actors services Production Directory Research Media data data Financial data Citizens NGOs Transparency, accountability Reveal Produce Improved Efficiency, Facts information services productivity Uses Innovative New Provide Improve services Outcomes knowledge Interfaces services Service Citizen Create new coproduction empowerment services Democratic participation <Special thanks to Tim Davies, Practical Participation>
  • Open Corporate Data
  • "Collaborative" Open Data
  • Challenges Standards (& adoption)Volume/Diversity Tools, Platforms of Open Data Uses, Ecosystem
  • Who’s Smarter in the Smart City?
  • What do we expect from the « Smart City »? Efficiency “Trillions of digital devices, connected through the Internet, Productivity are producing a vast ocean of data. Savings And all this information –
from the flow of markets to the pulse of societies –
 can be turned into knowledge. (…)EnvironmentCompetitivity With this knowledge we can reducecosts, cut waste, and improve the efficiency, productivity and quality of everything Growth from companies to cities. (…)Attractiveness Given all this low-cost technology and networking, whatQuality of life wouldn’t you enhance ? What wouldn’t you connect ? What Cultural life information wouldn’t you mine for insight ? What service wouldn’t you provide for a customer, citizen, student or Services patient ? The answer is, we will do all these things. Because Security we can — and because we must.”Transparency IBMParticipation…
  • 1- System? System of Systems? System^^n?
  • 2- Beyond « Service »
  • While "they" try to make the city smart…
  • The « city »?Large urban Are all services Smart Cities operators? Alike?Everybody? 3- Who becomes smart in the smart city?
  • Risks and dangers Misunderstandings Soft authoritarianism Innovation inhibition Obsolescence Less resilience
  • Shared Personal Data:Revolutionizing customer relationship
  • Personal Data Are the Lifeblood of Contemporary Marketing
  • Theyre also its poison
  • There Is Another Way
  • Lets Take Up a New Challenge: Empowering Consumers by Sharing With Them All the Personal Data that Businesses Own About Them Graphic: MyDex"If I Know Something About You, You Know It, Too!"
  • Everyone Stands to Benefit
  • The New Market for Personal Information Management Services Collecting, gathering, producing, Knowing oneself better, storing, referencing, classifying… and acting upon it ones data Comparing offers, Sharing (or not), checking,expressing ones needs, updating ones data group buying…Managing ones relationship with Analyzing, visualizing, modelizing, organizations… And with other comparing… ones personal data consumers
  • This Is Much More Than a Weird Idea AMEE / Avoco Secure / billmonitor / British Gas /Callcredit / EDF Energy / E.ON / Garlik / Google / LloydsBanking Group / MasterCard / Moneysupermarket.com / Mydex / npower / RBS / Scottish Power / Scottish Southern Energy / The UK Cards Association / Three / Visa / Google…
  • Are You Ready to TurnCustomer Relationship on its Feet? Experimenting consumer empowerment, through the sharing and reuse of the personal data that organizations own about them http://fing.org/?-MesInfos-les-donnees-personnelles-
  • What happens when data start living their own life? Daniel Kaplan [dkaplan@fing.org] Charles Népote [cnepote@fing.org]