Geneva, Switzerland, 20-21 March 2012

Innovation in big data analytics: from lab to market to standards

Filippo Dal Fiore, PhDM.I.T. Senseable City Lab

& Currentcitydalfiore@mit.edu

ITU Workshop on ICT Innovations

(Geneva, Switzerland, 20-21 March 2012)

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What the Senseable City Lab does

Data analyses and visualizations Smart objects

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What the Currentcity spin-off does

Data analytics applications

Our specialty within big data analytics: collective sensing

Aggregated and anonymized data from telecom and web 2.0 networks

is used as a proxy for human presence and activities, on an historical basis and in real-

time.

How we pursue innovation in the Lab

Industrial collaborations in which we “tweak” with proprietary data/sensorsReliance on open-source SW solutionsScalability and standardization issues not of immediate relevanceCreative, “keep options open” approach

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How we pursue innovation in the spin-off

Emphasis on user acceptance, revenue generation, future scalabilityExplorative, “keep options open” approachDifficulty to spot markets make the standardization question prematureBenefiting from previous standards (i.e. GSM/UMTS), but only up to a certain point

Big data analytics: a Babel of data and operations on them

Multi-level value chain:data generation data storage data query & extraction data analysis data fruitionThe possibility for multiple combinations are “infinite”: standardization to be applied at multiple levels

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Big issues in big data analytics

Data ownershipData securityData privacy Data formats & legacy data systemPatchwork of laws and regulations

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The need for standardizationin big data

Data reside within different companies and is silo-related (i.e. finance/health-care/energy)Need for (individual/urban) data portfolios“Data banks” as gateways for new data markets (data transactions as monetary transactions)

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In absence of standards, big data is managed in big pooled

datasets, but this lead to:

Increased vulnerabilily (i.e. hacker attacks)Proliferation of consent and data use agreements;Inflexible, stale and inaccurate datasets; (source: Helmore, 2012)

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Conclusions and Recommendations

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