Technology has opened the door. Now governments have unprecedented opportunities to tap into their own data-rich sources of intelligence to improve performance and reduce costs.

Utilizing available data can equip government leaders with a powerful tool for gathering valuable intelligence to inform planning and decision making. Big data analytics, which refers to the process of examining large data sets – hence the term “big data”– reveals patterns, correlations and trends. Using analytics effectively can yield deep insights that support successful change.

Predictive analytics offers game-changing potential
With our increasingly digitized world generating a burgeoning flow of information, organizations worldwide are capitalizing on this inherent value. Using predictive data analytics – assessing current and historical date to make future predictions – they are developing strategies to gain competitive advantage. Walmart, for example, aggregates and analyzes operational data throughout its operations to forecast consumer buying behavior. Amazon evaluates purchases, website traffic and geographic data to drive “anticipatory shipping” – delivering products to regions before they are ordered – to reduce costs and speed service.

Government has been slower to adopt predictive analytics. This sector has traditionally used analytics historically to measure performance, ensure audit compliance and support enforcement cases. It is, however, predictive analytics that offers game-changing potential: enabling faster, more informed decisions, reducing inefficiencies and improving service.

Some early adopters have achieved outstanding results in health services forecasting, social program effectiveness, fraud prevention, financial forecasting and crime prevention. In the UK, the City of London uses its subway smart cards to predict the impact of transit disruptions. The US government just appointed their first Chief Data Scientist, formerly with LinkedIn, to spearhead data analytics advances specifically focusing on health. The Chicago Department of Public Health uses big data to streamline inspections and predict outbreaks. New York City has dramatically reduced the time required for building inspectors to identify illegal apartments, thus reducing the risk of catastrophic events. In Toronto, planners are using census data and transportation surveys to forecast transit demand. By successfully managing big data, these organizations have become agents of change.

Best practices for successful big data analytics programs
In order to transform disparate information into targeted intelligence that government leaders can act on, the following best practices should be integrated into any analytics initiative.

Start with a plan
To assess the potential outcome of a new project, begin with a plan for how data, analytics, tools and people will work together to deliver value. Look first to existing investments and leverage available analytics services and products; most leading vendors now offer software solutions for big data analytics projects. Experienced analytics consultants can provide support in identifying opportunities with the best potential and delivering successful outcomes.

Develop a strong foundation for data collection and management
Establishing the groundwork for generating useful insights requires integrating information collection throughout core operations, adopting recognized standards for data collection and integration and adopting a collaborative approach to data governance.

Ensure sufficient data management and analytics skills
Skills planning is needed to ensure an organization has sufficient capabilities for the effective execution of a data analytics program. This includes evaluating the capacity of personnel, internal training and external resources.

Capitalize on the cloud
The need for additional infrastructure is no longer an impediment for new analytics projects. Numerous suppliers now offer extensive cloud-based capabilities for data collection and analysis. Cloud-based analytics also delivers an added level of data protection.

Enable self-service for users
Providing self-service access for information users ensures they will benefit from the insights of data analytics. Self-service also promotes the continuous exploration of data, which helps to identify the most valuable information and to refine what is stored and processed. This requires providing employees with intuitive analytics tools to perform custom and iterative analyses.

With the proliferation of success stories, new data sources, technologies and analysis techniques, there has never been a better time to capitalize on the potential of data analytics. Are you ready to transform information into game-changing insights?