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Government needs to reap the benefits of data analytics

The Australian government collects massive amounts of data on people, but data does not equal insight. Analytics creates value from data that is more than the sum of its parts.

Government needs to reap the benefits of data analytics

To deliver citizen-centric services in a coordinated, efficient and equitable manner, governments must break departmental data silos. To do this, government agencies need a fresh approach to managing citizen data that focuses on integration.

When used effectively, data analytics can save lives, improve efficiencies, reduce costs and help governments deliver better citizen services.

Data analytics is what makes big data come alive. The Australian government collects massive amounts of data on people through various agencies and touch points. Without analytics, the government can store and retrieve this data, but it cannot gain insights. Analytics, using a number of different technologies, creates value from data that is more than the sum of its parts.

Data analytics can answer 4 key questions:

  1. What happened
  2. Why it happened
  3. What will happen
  4. How to make it happen.

Descriptive analytics answers what has happened. Diagnostic analytics answers why it happened. Predictive analytics tells us what will happen. Prescriptive analytics reveals how to make something happen.

Governments must find answers to big questions, such as how to overcome the drugs problem, how to improve public health, or how to stop terrorism. While individual stories give context to these challenges, government agencies need sound data analytics to shape their response to the problem.

Historically, decision-makers have had to rely on whatever information was available to make the tough choices. But what they had access to was not necessarily the best information. Sometimes the people who were asking the questions didn’t have access to the right data.

To address the issue of illicit drugs, governments can work with police labs to track the types of illegal substances police are finding on the street, and how often they’re finding them. The results may show a steady rise in heroin supply and a decline in cocaine, for example. By soliciting data and input from law enforcement and public health agencies, governments can visualise the data by creating geospatial maps, colour-coordinated line graphs with trend data, or even a time-lapse map that shows how the drug epidemic has evolved over time.

They can then see where treatment centres may be needed, or where local hospitals may need more resources, or where police may need more numbers.

For the first time, governments have access to technology that lets them combine all these data sets to tell a complete story, leading to much better policy.

Data analytics will play a key role in government. It will require a continued effort in analysing increasingly large datasets. And large datasets are arriving with greater speed than ever before, and with wider variety and complexity.

With more data to analyse, agencies can easily be overwhelmed as they try to maintain system performance and data integrity while protecting sensitive information. Additionally, with most agencies having legacy systems in place, it can be difficult to integrate new systems and different vendors into their existing analytic ecosystem.

Data analytics and cloud storage will make a strong combination for government. Lower costs mean government agencies will be able to do more with less. Agencies will be able to avoid setting up and maintaining their own infrastructure. While that’s being handled by the cloud provider, agencies can convert capital expenditure into operating expenditure.

Legacy systems can prove a barrier to agencies looking to put cloud first. However, a unified data architecture can solve this. This is an ecosystem that lets users harness all of the agency’s data. It’s also referred to as a logical data warehouse because it brings the right type of analytical processing to the underlying datasets. Further, it allows users to combine and analyse data from multiple disparate platforms into a single query.

A logical data warehouse can subsume older platforms, so agencies that choose not to transform and migrate data into an integrated data model can still leverage their legacy systems.

With a unified data architecture that can accompany legacy systems, complemented by cloud technologies, government agencies can better manage the vast amounts of data accelerating toward them. The future of data analytics does not have to be daunting. With a unified data architecture, inside or outside the cloud, governments can be ready for advanced analytical capabilities and harness large influxes of complex data to further agency initiatives.

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