Digital transformation and big data. Line H. Clemmensen Associate Professor, Head of Continuing Education DTU Compute

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1 Digital transformation and big data Line H. Clemmensen Associate Professor, Head of Continuing Education DTU Compute

2 Agenda Digital transformation From industry readiness to practical implementation Big data From definition to the future value of data Examples 2

3 From industry readiness to practical implementation DIGITAL TRANSFORMATION 3

4 Digital transformation of businesses Source: Boston Consulting Group (2015) 4

5 Strategy depends on current digitisation phase Source: Bain & Company (2015) 5

6 Industry 4.0 digitalisation into cyber physical systems Source: Christoph Roser at AllAboutLean.com 6

7 Agile development (end-to-end) Source: Three Factors Driving the Uberization of Talents by Albert Meige Jan 29, 2016 DIGITAL TRANSFORMATION 7

8 A well-defined validation set and a single evaluation metric speeds up the process Idea Evaluate Code Experiment Reference of interest for dev teams: Andrew Ng s Machine Learning Yearning

9 From definition to the future value of data WHAT ABOUT BIG DATA?

10 Types Data - of Bigness problems in Volum e? Supervised learning: The computer is presented with example inputs and their desired outputs. Relies on valid label assignments and a useful response. The goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm. The computer learns a structure from the input data. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle). Another example is learning to play a game by playing against an opponent. 3 DTU Compute, Technical University of of Denm Denmark Line H. Clemmensen 27/08/2015

11 Big data and the hype cycle,

12 2017, Hype cycle of emerging technologies 12

13 So, what s new? Gartner s hype cycle,

14 Performance Data size and model improvement (deep learning) NN: Neural Net, ML: Machine Learning Small NN Medium NN Large NN Traditional ML Small data Medium data Large data Lager data 14

15 The future Value of data (The 5th V) Workshop with Future Agenda at DTU Business Privacy and ownership of data Open data Digital literacy Artificial Intelligence rise of the machines? Fake data 15

16 Privacy and ownership Right to an explanation : - Interpretation - Certifications Source: Beacon Source: Medium.com Preserving privacy through e.g.: Encryption Statistical disclosure control Privacy preserving algorithms Source: 16

17 Open data Findable Accessible Interoperable Reusable Source: theodi.org 17

18 Source: dr.dk Digital literacy Source: vizexperts Source: Network World.com 18

19 Artificial intelligence 19

20 Fake data Issues - Who owns the truth? - Missing data - Erroneous data - Misleading data - Fabricated data Institutional consensus vs public consensus - Wikipedia - Media - Standards/certifications? - EU/UN? Source: Global Marketing Alliance (2017) 20

21 Digital transformation and big data Summing up Infrastructure End-to-end development (enablement) 5Vs Volume Velocity Variety Veracity Value 21

22 Examples of research intelligence augmentation Source: Gronskyte et al, Computers and Electronics in Agriculture,

23 Examples of research - interpretations Source: Welling et al, ArXiv, 2016 Source: Clemmensen et al, Technometrics,