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Fast Data in the IoT

Fast Data in the IoT. Bas Geerdink. About Me. IT Manager / Chapter Lead Academic background in Artificial Intelligence and Informatics Working in IT since 2004, previously as developer and architect At ING since June 2013 Twitter: @ bgeerdink

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Fast Data in the IoT

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  1. Fast Data in the IoT Bas Geerdink

  2. About Me • IT Manager / Chapter Lead • Academic background in Artificial Intelligence andInformatics • Working in IT since 2004, previously as developerand architect • At ING sinceJune 2013 • Twitter: @bgeerdink • LinkedIn: https://nl.linkedin.com/in/geerdink

  3. About ING

  4. Opportunitiesfor banking in the IoT

  5. The IoT offers a lot of opportunities for the banking industry Source: The Bank of Things – Accenture, 2014

  6. In a way, banks are already at the center of the IoT

  7. The use cases forbanks in the IoT canbedivided in twocategories • The bank as facilitatorof IoT services, byprovidingmicrotransactionsand open APIs • Real-time, automatic pay per use of gas/water/energy consumption • On-demandbuying of groceriesandother items in the ‘smart home’ • Direct, automatic payments of gas/electricity in gas stations in the ‘connectedcar’ • The bank as provider of IoT services byusingitsowndevices as the source of data • Personal assistence, e.g. real-time financeadvise • Alertingandother means of signaling in the ‘smart home’ • Location-based services, e.g. buyingadvisefor a house based on Google Glass data • Individual marketing andadviseby making use of beacons in shops and branches

  8. ING’s data-drivenstrategy

  9. ING’sThink Forward strategy was launched in March 2014

  10. There is a lot of data in the bank, in manysizesandshapes

  11. There are three types of data streams at ING BIG DATA FAST DATA

  12. Architecture and Technology

  13. All data streams follow the samepatternand pass throughpipesand filters Source: A Reference Architecture forBig Data Solutions – Bas Geerdink, 2013

  14. ING’s Data Lake is based on IBM’s Data Reservoir reference architecture

  15. ING’s Data Lake is based on IBM’s Data Reservoir reference architecture Core Systems Analytics Tooling GovernanceCatalog Real-time Streaming Reports Archive Exploratory Data Batch & Incremental Feeds Enterprise Datawarehouse

  16. ING’s Data Lake is based on IBM’s Data Reservoir reference architecture Core Systems Analytics Tooling GovernanceCatalog Real-time Streaming Reports Archive Exploratory Data Batch & Incremental Feeds Enterprise Datawarehouse

  17. The technologydepends on the data size, speed andusageNote: components are examples, technology is under constant evaluationand subject to change Core Systems Analytics Tooling GovernanceCatalog Real-time Streaming Reports Archive Exploratory Data Batch & Incremental Feeds Enterprise Datawarehouse

  18. Conclusions

  19. Summary andConclusions • The IoT offers a lot of opportunitiesfor the banking industry • Banks can act as facilitator or provider for IoT-driven services • Someexampleuse cases are: automatic payments, microtransactions, buyingsuggestions, financial advise • Sensory data includeslocation data, household equipment, data fromcars, ING’sinfrastructure • ING has a data-drivenstrategy, aimed at providing the best customer services • It makes sense to invest in aninfrastructureforany kind of data processing, including IoT streaming data: the data lake

  20. Thankyou bas.geerdink@ing.nl @bgeerdink

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