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DIGITAL TRANSFORMATION #4 (Example of DT : DVLA (Driver + Vehicle…
DIGITAL TRANSFORMATION #4
Example of DT : DVLA
Driver + Vehicle Licensing Agency: UK Government Organisation
Database of drivers and vehicles for the whole of the UK
42 million driving license holders
3.3 million vehicles registered during 2016
Identifies untaxed vehicles [reminders, fines etc]
There are currently around 500,000 ANPR cameras in operation around the UK, capturing images of 30 million license plates every day
Speed cameras: matching cars to their keepers
Record vehicle and driver details: new registrations, provide data to authorities, cars being sold/scrapped
CRUD: create, read, update, delete
DVLA : New Vision
Profit: they have useful data that could possibly be sold
Competition: they don't really have any for core activities
Customer expectations: need a better online offering [less clumsy organisation to deal w]
Planetary issues: their main activity involves cars. Could, for example, introduce VED [tax] based on emissions
DVLA : IT History
A database was set up from 1996-1999 by Capita [a large outsourcer]
In 2013 the new CEO of the DVLA said that they had no choice but to outsource in the past bc the complexity and challenge was too great. However, this is no longer the case
The outsourcing contract ended in Sept 2015
DVLA looked at new models for IT outsourcing
2-year project to bring IT back in-house
New CEO Oliver Morley had a different vision
DVLA : Digital Transformation Aims
Want to become a hub for digital motoring
Transform their IT estate: deliver our digital transformation ambitions + create the best online service for customers
Digital transformation enables new types of innovation + creativity, rather than supporting traditional methods
Need to be own systems integrator to give ability to change
They still have plenty of partners, but have direct relationships
They coordinate delivery and have mixed development teams
DVLA : Digital Transformation New Services + Side Effects
In 2004 customers started paying car duty online + by phone
Since 2012 anyone can request info for £2.50: 6.8million records sold to car parking firms in 12 months to April 2019
50% of NHS organ donation registrations through DVLA
Webchats + chatbots for customer service
First voice services rolling out [Alexa vehicle checks]
2019: register a vehicle or trailer online; apply for tachographs online
DT Side Effects
DVLA are continuing their development
Lots of government involvement in DVLA insourcing
Lessons have been learned to be shared across government
DVLA are accountable to government: e.g can't just create a mobile app to track lorries bc they feel like it - risks are higher
In 2006 2.2million V5C documents were stolen - still being used to illegally register vehicles
Example of Digital Transformation : UBER
Founded in 2009 in LA
Designed to link supply and demand: private drivers + customers
80% of fare to driver, 20% to Uber
Rapid growth: 63 countries, 300 cities, more than 8million users worldwide
Value of the company is over 50billion dollars
What do the systems do?
Match driver to rider: sets price, manages interaction, manages payment
Security and authentication
Location aware: wait time for taxis, driver knows where you are
UBER : Innovations
Dynamic pricing using Big Data
A multiplier on the map
Drivers go where they get more
Some riders will wait
Different Types: UberX [Standard], UberPOOL [schedules shared rides], Lux, MOTO etc - adapts to local conditions + markets
Real time ID check using AI
Validates driver screening checks
Defends against hacking accounts
Microsoft's face API
Context has a lot of variance which makes it harder for AI
Currently partnering w TomTom
Investigating best drop-off/pick-up points [Big Data, AI, GIS]
Investigating autonomous vehicles [Big Data, AI, GIS]
Failings : Unforeseen Risk of Radical Change + Future w/ Autonomous Vehicles
Assault: reporting to transport for London, not the police - lost licence in London + some other places. They can fight it but the world will function without them
Greyball: hides Uber data from known law enforcement
Taxi lobby is powerful
The world is diverse - they must adapt regulations
Employees or contractors: compare to cab service
Autonomous Vehicles
Currently 80% goes to driver + only 20% to Uber
Efficient use of energy
Utilisation of vehicles