Recently OpenGov had a
fascinating discussion with Mr. Bernie Kruger, Business Intelligence and Data
Science Lead, from the Transport Accident Commission (TAC), Victoria,
Australia on getting the foundations in place for realising the potential of
data insights and exploring advanced applications at the same time.
The TAC is a Victorian
Government-owned organisation whose role is to promote road safety, improve the
State's trauma system and support those who have been injured on the roads,
through an insurance scheme. The TAC covers transport accidents directly caused
by the driving of a car, motorcycle, bus, train or tram. The TAC can provide
support services for people injured in a transport accident as a driver,
passenger, pedestrian, motorcyclist, or in some cases, a cyclist.
The TAC can also pay income
support while people recover, and in the case of some serious injuries a lump
sum payment may also be payable.
Mr. Kruger has a twofold role
at TAC. He is the lead for the business intelligence, as well as the data
science function. Within the business intelligence space, there are sub-teams.
One of them is the business analyst team that is the interface between the
business and the business intelligence analysts. Then there is the data
management team, which is responsible for all the data manipulation and working
out the data ethics. There is a reporting team, and finally a modelling and insights
team. The modelling team takes care of model governance and model
implementation, after the models have been developed.
“When its not a repeatable
piece of work but the business is coming up with a specific question that we
need to respond to with a report or a finding, the insights team produces
information and report it back to the business,” said Mr. Kruger.
Business intelligence focuses
on the operational aspects, while the data science function looks at more of
the strategic applications, including advanced areas such as machine learning,
text analytics, natural language processing, social network analytics and etc.
Previously, the data science
area was separate from the business intelligence. Mr. Kruger was leading data
science, and then he was asked to lead business intelligence as well. Though
the two have been brought under one umbrella, Mr. Kruger said that they have
separate identities. Business intelligence follows the traditional governance
processes and methods, while data science is agile and involves quickly
developed proofs-of-concept.
Data in TAC’s 2020 strategy
Data insights are a critical
enabler of the TAC 2020 strategy
and beyond. The agency plans to establish an enterprise-wide approach to
translating data. This will allow research and data to be shared across the
organisation which will provide insight and inform decision making.
TAC’s 2020 vision has two components. The first is ‘Towards Zero’,
which is about reducing the number of lives lost, as well as the number of
seriously injured people towards zero. Data is a key enabler here, informing
the team’s strategies to address that on four levels: safer cars, safer routes,
safer speeds and safer drivers.
In some cases, TAC uses the data directly to analyse and look for
specific patterns. And in other cases, the data research for things like accident
research is outsourced to the universities. A lot of that data comes from TAC’s
partners, such as, Victoria Police
and VicRoads.
The second component of the vision is getting people’s lives back on
track after an accident. Mr. Kruger said, “How do we respond to their needs,
what they need to get back to work, to get healthy and resume their lives. So
that is a core area of client-centricity where we use that data to advise us on
our clients’ needs.”
The challenges
Mr. Kruger went on to outline the challenges faced in the use of data.
“The environment in TAC grew organically grew over time and it was
mostly reactive as opposed to pro-active. We utilised whatever we had available
but as we get more modern and the nature of data changes, the type of data and
the speed which we generate data changes, we realised that there are a few
inadequacies,” Mr. Kruger said.
One of the primary problems is data governance. Part of data governance
is data quality, as well as data ownership. Who owns the data, is it business
or is IT? This is further complicated with the move to the cloud. This also
leads to weak data lineage and problems with data quality.
Because of problems with data, if the system changes and reports have
to be altered, it’s difficult to know the downstream impact on reporting areas.
In addition, a lot of information is in people’s heads and it is a challenge to
keep documentation up-to-date with all the changes.
Then TAC was using a specific software suite as a ‘Swiss Army Knife’,
for everything from loading the data to cleaning, storage etc. Business
software was being used for things it was not designed to do. And being married
to one software suite like this can be a trap. The total cost of ownership
(TCO) can turn out to be very high.
On top of all this, often there is inadequate buy-in from senior
management. They do not consider data to be an asset. It is rather seen as an
operational tool. The BI (business insights) team/ data scientists are viewed
as service providers in many organisations. This kind of culture results in an
us vs. them mentality and hampers collaboration.
The value of the data is seldom measured. Organisations are not aware
of the monetary value they can attach to certain data of a certain quality.
Moreover, there is chronic underinvestment in IT.
Dealing with the challenges
How is TAC addressing these challenges? Firstly, many data management
activities are being automated, enabling greater focus on high value analytics,
leading to better reporting and better insights.
Everyone is taken along on the data journey, not just the executives or
IT. The benefits of data are shared. People are shown what is involved in a day
for analyst and the challenges involved in extracting insights from data.
A Data, Reporting & Analytics competency centre has been set up to
bring together analytics pockets dispersed throughout the organisation.
Mr. Kruger said that solutions have to be designed together with
business. Data scientists should not just get the requirements and design the
solution. The business has to be a part of the process. If everyone is not
on-board, projects are going to fail.
A working group was created with representatives from various business
divisions as well as IT and business intelligence. A review was conducted of the current
environment.
Mr. Kruger added, “We also met with consultation companies. We came up
with a future state and we did a proper analysis of what is required to support
our strategy to 2020 and beyond, so that is sustainable and can adapt to the
pace of data. The challenge for us was then of how to bridge that gap. We knew
what we needed to do; why we need to do it, but how to do it was a bit of a
problem.”
So, options were worked out over a period of 3 months to understand how
to get from the current stage to the next stage. This included software
options, integration suites vs. individual applications, business intelligence
requirements vs. data science, cloud vs. on-premise, enterprise data warehouses
vs. data lakes. Skills and capabilities, as well as governance issues were
taken into consideration.
Advanced applications (AI, machine
learning and predictive analytics)
Often organisations jump on the bandwagon of machine learning,
predictive analytics, without building the foundations. It is driven by a fear
of falling behind. “We have to do it because everyone else is doing it.”
Other organisations work to get the foundations in place and only then,
do they build AI and machine learning on top.
A hybrid approach is followed at TAC. The team understands the
importance of getting the foundations in place and is working towards it. At
the same time, while data governance and data quality issues are being
resolved, in parallel the team explores and experiments with the advanced
applications.
Mr. Kruger discussed a few examples. TAC is using machine learning or
predictive applications to create client profiles for qualifying them for
pre-approved medical services. This eliminates the need for any additional
approvals and makes it easier to meet clients’ needs.
Another application being explored is when clients are referred to
vocational training service providers; the clients are scored and classified
according to levels of need. Then TAC can make baseline predictions regarding
the amount of time required for the client to get back to the workforce. That
information is passed on to the service provider as a starting point.
TAC also funded research to find
early indicators and drivers for mental health problems or persistent pain, as
they can significantly hamper the recovery process. In conjunction with that,
TAC developed data intelligence tools to pick out behavioural patterns from the
massive amount of data they hold on accident, injuries and launch of claims,
services TAC is paying for and more, to identify incidences of those indicators,
which could be predictive of debilitating pain or mental health problems down
the road.
TAC is also in the process of
applying text analytics, analysing the clients’ phone calls with TAC and file
notes to understand correlations with early indications of these problems. The
idea is to predict what the clients are going to require at the earliest stage
possible and then respond quickly and efficiently to those needs.
Another trial project Mr. Kruger talked about is the TAC Sofihub Smart
Home where data insights are being used to enable more effective long-term care
of the patients.
“A lot of our long-term support clients either live in accommodations supplied
by us and many of them require 24/7 support and people to look after them. One
of the things that we’re trying at the moment is the Sofihub Smart Home, where
there are sensors in the house, for example, sound sensors, vibration sensors to
see if a client has fallen and can’t get up or have taken their medication etc.
The idea is that it will utilise AI to understand what the person’s normal
patterns are, when they need to be alerted to take their medication or alert
the client or caretaker if there are long periods of inactivity,” he explained.
With AI support, clients can manage their daily lives without having a person
present 24/7. This project is still in a trial phase and privacy concerns are
being examined.
Projects such as the ones discussed above demonstrate the benefits that
can be derived from data to business and to the senior management. They help
get buy-in.
Mr. Kruger said, “So, we get the best of both worlds. The fact that we
have problems with data quality and we are having to get all the other things
in place, does not stop us from doing these applications because we have to explore
the mechanisms of doing those. And hopefully it will be incorporated into the
newer architecture and platforms”.
