Q&A: Using data visualization for evidence-driven policy decisions

Australian Institute of Health and Welfare gets real-time access to data

What types of injuries result in hospitalization? How does dental care in remote areas compare with that in urban regions? Is Australia's medical workforce growing to meet the demands of society?

When Australian policy makers ask these types of questions, the Australian Institute of Health and Welfare (AIHW) supplies the answers. As the country's national agency for information and statistics about health and welfare, AIHW aims to improve the well­being of citizens through better use of information and statistics. Governments and community leaders use information from AIHW to discuss, debate and design policies for health, housing and community services.

Warren Richter, Senior Executive for ICT & Business Transformation at AIHW, recently took some time to discuss the importance of visual analytics in the policy development process, and to describe how big data is affecting the agency's work.

The Australian government's new statistical data integration arrangements enable more information to be made available for research and analysis. This is going to be of great benefit to the community over time. We are using SAS and SAS Visual Analytics to explore this data.”

Warren Richter
Senior Executive for ICT & Business Transformation

Why is data exploration important for The Australian Institute of Health and Welfare?

Our mission is clear: To provide authoritative information and statistics to promote health and well­being. That's what we're all about. We collect, analyse, and disseminate information in the areas of health, age ­care services, child­care services, housing assistance, child welfare, and other community-­related sectors. We have also produced some performance indicators and targets for national agreements.

Today, it's not so much what we do with analytics; it's a question of what we want to do. We have a long history of linking large and complex data sets for research and statistical purposes, and we have recently become one of the first two organisations accredited as an integration authority under the very strict Australian government arrangements for integrating data sets containing sensitive information about individuals. That means, after approval by an independent ethics committee, we are able to produce detailed information for research and analytical purposes.

We take this role and our responsibility to preserve privacy and confidentiality very, very seriously. We undertake around 90 data integration operations every year, some of which are extremely complex.

These accreditation arrangements enable the Australian government to make more data available for research and analysis. This is going to be of great benefit to the community over time.

We are using SAS and SAS® Visual Analytics to explore this data.

There is a neat convergence of data and capability here – rigorous confidentialisation and accreditation arrangement to free up data combined with the very exciting capabilities of in­-memory analytics packages such as SAS Visual Analytics.

How does big data influence your work?

Statistical agencies like ours have dealt with big data for a long time, and we can continue to do the traditional analyses with existing tools. But the availability of large and more complex data sets is transforming what we are able to do. In our case, it's come about not through the Internet, but through the willingness of the Australian government to make more data available for analysis under strict conditions.

But getting data from the Internet is also going to be relevant to us, because we are starting to explore opportunities to access real­-time data as a byproduct of administrative operations as they occur, not just as they occurred in the past 12 months or so. Some statistical agencies around the world are taking direct feeds from point of sales terminals, for example, so you're measuring the economy as it's happening. We think there will be opportunities to do similar things in the health sector.

Why did you select SAS for visual analytics?

It boiled down to value for us: whether SAS Visual Analytics could handle the size and complexity of the data sets, whether it was easy to use, and then, of course, whether it supported the analytical techniques and visualisation approaches that we require.

Instead of focusing on every last whiz­bang, push­button feature, it was more important for us to be able to use SAS as an extended platform so we can manipulate the underlying data sets and expose the analyses behind the visualisations. It's about the value for money and enhancing our existing data exploration capabilities.

Increasingly, we're being asked by government agencies to develop such things as clearinghouses of information – not exactly data warehouses but dashboards – that expose a particular sector or area within a sector for access by decision makers. SAS supports that vision.

We also want to support decision makers and policy analysts in our client agencies, such as the Department of Health and Ageing. We need to work with agencies on policy problems, providing them with the data they need, when they need it and helping them draw insights from that data with visualisations. We don't want to continue just doing what we've traditionally done, which is to report on something. We're getting ready to support them as they explore and understand the data and to help them apply the right analyses. We want to provide more value ­add than we currently do.

Essentially, we aim to help analysts to get the information they need in real­ time as they do their jobs, rather than make them wait 18 months for a report, which may not even fully answer the question at hand.

Can you give a few specific examples of policy areas that will be using SAS Visual Analytics?

Increasingly, we're supplementing more of our publications and cubes with visualisation. And we plan to extend it to develop some new service offerings for our clients to support their decision making.

One area that AIHW is already using visual analytics is in the development of new approaches for presenting decision makers with information about mental health services. We are very excited about the way we can quickly and easily produce dashboards with rich visualisations from very complex and rapidly changing data sets and make them available online. We will be extending this capability to other subject areas very quickly.

How might visual analytics be used to identify new types of questions and explore data differently?

You know, if you have a small data set and you want to do some visuals using old­ fashioned, run­ of ­the ­mill analytical techniques, you can do that fairly easily. Even in a spreadsheet, you can run a simple regression on a small data set, but it's not as easy when you've got a very large and complex data set to explore. It's very valuable to be able to say, "Here's the data – bang – you've got it. Let's start to look at it without having to determine what sampling or subsetting technique to use, and determine if that is valid." We just don't have to worry about that now.

Before visualisation, you had to know exactly what analysts were looking for before you could build your cubes. Now, we can make the whole data set available to everyone all of the time, subject to privacy and confidentiality considerations of course. It's terrific to be able to get something going very quickly across large and complex data sets as they are created.

In conclusion, can you summarise your long-­term goals for visual analytics?

We want to use the data that we currently have to shed more light on issues, to describe the real­ world better by using visualisations, and to support our key clients directly via visual analytics as they make policy recommendations and formulations using real­ world data as it is created. We think we can help them formulate better policy proposals by giving them a much more intimate relationship with and a better understanding of the data. The ability to access a very large and complex data set easily and to do a what ­if train of thought analysis together with our clients is very exciting and we are looking to develop this as an ongoing high-value-add service.


To add to AIHW's armoury of analytical services and take advantage of recent accreditation to access sensitive data sets. Data exploration capabilities that handle the size and complexity of the data sets, while being easy to use and supporting the analytical techniques and visualisation required by AIHW.


SAS® Visual Analytics


Analysts are able to get the information they need in real time as they do their jobs, rather than make them wait 18 months for a report, which may not even fully answer the question at hand. AIHW is now able to extend the analytical services provided to clients.

Результаты, описанные в этой истории, относятся к конкретной ситуации заказчика, его бизнес-моделям, исходным данным и вычислительным средам. Опыт каждого клиента SAS уникален и отличается техническими параметрами создаваемой системы, поэтому все заявления носят ситуативный, а не общий характер. Фактические результаты, экономия, производительность и изменения в ключевых показателях эффективности могут варьироваться в зависимости от конфигурации решения и бизнес-условий каждого заказчика. SAS не гарантирует и не утверждает, что каждый заказчик получит такие же результаты, как описаны здесь. Единственными гарантиями для продуктов и услуг SAS являются те, что заявлены в письменном соглашении по соответствующим продуктам и услугам. Ничто из описанного в данном материале не может расцениваться как дополнительные гарантии. Заказчики поделились с SAS своими достижениями и результатами в соответствии с условиями договора или после подведения итогов успешного внедрения программного обеспечения SAS. Наименования продуктов являются торговыми марками соответствующих компаний.

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