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

Film by Foursquare. Google: checkins foursquare

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

So…

What IS Big data?

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 statistics

Exhausted by Adrian Sampson on Flickr (CC-BY)

just

Not Business

Intelligence

Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)

just

http://dashburst.com/infographic/big-data-volume-variety-velocity/

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”

Measure real-time

system behaviour

Emil Johansson - EJOH.SE

Collecting data - a new world emerging

Predict future events

http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk

Vizualisation

Created by Oliver O'Brien (UCL Geography/UCL CASA)

Vizualisation

Locals and Tourists #1 (GTWA #2): London by Eric Fischer on Flickr

Vizualisation

Vizualisation/combination

Vizualisation/combination

Manage complex systems

Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center

Avoid unpleasant surprises

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

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