How data analytics can change government

In an election year, all thoughts turn to changing governments. One of the most effective catalysts for change is, in fact, data analytics.

When used effectively, data analytics can help to 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 large amounts of data about 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 four key questions: what happened, why it happened, what will happen, and 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 are tasked with finding 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.

Decision-makers have to rely on whatever information is available to make the tough choices. Historically, 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 increasingly detailed data on 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 submissions 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, where local hospitals may need more resources or where police may need more numbers.

Modern technology allows governments to combine all these datasets to tell a complete story, leading to much better decision- and policy-making. When you can collect, unify and analyse all the data that surrounds your department, government is empowered to uncover the key insights that matter most.

In 2016 and beyond, data analytics will play an increasingly key role in government. It will require a continued effort in analysing increasingly large datasets. These 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.

Teradata provides end-to-end solutions and services to let government agencies create unified data ecosystems. The Teradata Unified Data Architecture, for example, integrates different platforms into a comprehensive analytics solution that enables fast, deep and powerful data management, storage and exploration. With this architecture, agencies can complement and augment their legacy systems rather than abandon them.

These technologies can help all areas of government. For example, healthcare fraud can be a significant problem. However, by combining medical claims data with other publicly-available data — such as bankruptcies, liens, judgements or even repeated address changes — into an integrated data model or unified data architecture, advanced analytics can be used to make fraud easier to track and even proactively predict the likelihood of its occurrence.

Teradata’s Aster analytics engine helps run analytics that could only be done manually in the past. It can be used to analyse older, transcribed handwritten records or typed reports through sophisticated text analytics capabilities and sentiment analysis automatically, decreasing time to results. With less expensive storage available through sophisticated techniques, all within the unified data architecture, government agencies would be able to analyse all the data, rather than just representative samples of it.

Data analytics and cloud storage will make a strong combination for government. The reduced costs mean government agencies will be able to do more with less. It means agencies will be able to avoid setting up and maintaining their own infrastructure. Because that’s done by the cloud provider, the agency 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, government can be ready for advanced analytical capabilities and harness large influxes of complex data to further agency initiatives.

There are three key success factors in using data to improve government:

• Think through the strategy: create a proof of concept, or collaborate with other organisations that have already invested in solutions.
• Rethink how you can use existing data in new and meaningful ways.
• Solicit the help of parliamentary officials to break down data-sharing silos among agencies.

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