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Multicolour Jelly Belly beans in Sugar! by MsSaraKelly on Flickr (CC-BY)
Big data Webcoast 2015
Per Olof Arnäs
@Dr_PO
Slides on slideshare.net/poar
Jawbone measures sleep interruption during earthquake
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/
892 by benmschmidt on Flickr (C)19th century shipping visualized through the logs of Matthew Fontaine Maury (1806-1873), US Navy
Shipping movements in
the 19th century
2011 2013 2015
”Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications.”
- Wikipedia
2015
Not Business
Intelligence
Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)
just
http://www.scdigest.com/ontarget/14-01-21-1.php?cid=7767
Speculative shipping Package item(s) as a package for
eventual shipment to a delivery address
Associate unique ID with package
Select destination geographic area for package
Ship package to selected distribution geographic area without completely
specifying delivery address
Orders satisfied by item(s)
received?
Package redirected?
Determine package location
Convey delivery address, package ID to delivery location
Assign delivery address to package
Deliver package to delivery address
Convey indication of new destination geographic area and package ID to
current location
Yes
Yes
No
No
smile! by Judy van der Velden (CC-BY,NC,SA)
7Big Data Best Practice Across Industries
Usage of data in order to:Increase Level of TransparencyOptimize ResourceConsumption Improve Process Qualityand Performance
Increase customersloyalty and retentionPerforming precisecustomer segmentationand targetingOptimize customerinteraction and service
Expanding revenuestreams from existingproductsCreating new revenuestreams from entirelynew (data) products
Exploit data for: Capitalize on data by:
New Business Models
Customer Experience
OperationalEfficiency
Use data to: • Increase level of
transparency• Optimize resource
consumption • Improve process quality
and performance
Exploit data to: • Increase customer
loyalty and retention• Perform precise customer
segmentation and targeting • Optimize customer interaction
and service
Capitalize on data by: • Expanding revenue streams
from existing products • Creating new revenue
streams from entirely new (data) products
New Business ModelsCustomer ExperienceOperational Efficiency
Figure 4: Value dimensions for Big Data use cases; Source: DPDHL / Detecon
2.1 Operational Efficiency
For metropolitan police departments, the task of tracking down criminals to preserve public safety can sometimes be tedious. With many siloed information repositories, casework often involves making manual connection of many data points. This takes times and dramatically slows case resolution. Moreover, road policing resources are deployed reactively, making it very difficult to catch criminals in the act. In most cases, it is not possible to resolve these challenges by increasing police staffing, as government budgets are limited.
One authority that is leveraging its various data sources is the New York Police Department (NYPD). By capturing and connecting pieces of crime-related information, it hopes to stay one step ahead of the perpetrators of crime.6 Long before the term Big Data was coined, the NYPD made an effort to break up the compartmentalization of its data ingests (e.g., data from 911 calls, investigation reports, and more). With a single view of all the informa-
tion related to one particular crime, officers achieve a more coherent, real-time picture of their cases. This shift has significantly sped up retrospective analysis and allows the NYPD to take action earlier in tracking down individual criminals.
The steadily decreasing rates of violent crime in New York7 have been attributed not only to this more effective streamlining of the many data items required to perform casework but also to a fundamental change in policing practice.8 By introducing statistical analysis and georaphical mapping of crime spots, the NYPD has been able to create a “bigger picture” to guide resource deployment and patrol practice.
Now the department can recognize crime patterns using computational analysis, and this delivers insights enabling each commanding officer to proactively identify hot spots of criminal activity.
6 “NYPD changes the crime control equation by the way it uses information”, IBM; cf. https://www-01.ibm.com/software/success/cssdb.nsf/CS/JSTS-6PFJAZ7 “Index Crimes By Region”, New York State Division of Criminal Justice Services, May 2013, cf. http://www.criminaljustice.ny.gov/crimnet/ojsa/stats.htm8 “Compstat and Organizational Change in the Lowell Police Department”, Willis et. al., Police Foundation, 2004; cf. http://www.policefoundation.org/
content/compstat-and-organizational-change-lowell-police-department
2.1.1 Utilizing data to predict crime hotspots
DHL 2013: ”Big Data in Logistics”
http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk
Vizualisation
Manage complex systems
Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center
Domain knowledge critical!
See for instance: Waller, M. A. and Fawcett, S. E. (2013), Data Science, Predictive Analytics, and Big Data: A Revolution
That Will Transform Supply Chain Design and Management. JOURNAL OF BUSINESS LOGISTICS, 34: 77–84
Data scientists - the new superstars
"Data Science Venn Diagram" by Drew Conway - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Data_Science_Venn_Diagram.png#mediaviewer/File:Data_Science_Venn_Diagram.png
Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Cross-disciplinary
Cross-industries
Cross-borders