Government leaders, particularly CIOs, have been at the forefront of enabling rapid, emergent responses through technology-enabled measures and improved data analytics. The good news is that, while not perfect, some amazing feats were achieved during this time.
For many government organisations, analytics has expanded beyond traditional reporting and policy support functions to include a focus on delivering actionable and timely insights. By exploiting technological advances, they can deliver faster, more accurate decisions at the front line, where significantly greater value is being achieved through embedding analytics in operations.
Service delivery becomes more personalised, responsive and timely across the whole value chain — from sensing to decision-making to action.
The focus of governments on digitally augmenting their workforces has led operationalised analytics to be one of the top trends that Gartner recommends government CIOs should include in their strategic planning over the next 12–18 months if they haven’t already.
Predictive decision support
Operationalised analytics is the strategic and systematic adoption of data-driven technologies, such as artificial intelligence (AI) and advanced analytics, at each stage of government service delivery or decision-making processes. It represents a shift from the dashboard reporting of lagging indicators to predictive decision support.
Decision-makers — from front line to executives — can make better context-based operational decisions in real time. Proactive business processes are generated that leverage AI and advanced analytics to improve the quality of the citizen experience.
Gartner predicts that by 2024, 60% of government AI and data analytics investments aim to directly impact real-time operational decisions and outcomes.
There are two critical differences between operationalised analytics and traditional analytics. First, operationalised analytics provides information support at the time of decision, embedded within the normal operational workflows. It isn’t a separate activity.
Second, timeliness is critical. Information on which a decision is made should be current and contextualised at the time of that decision. For relatively stable information, such as geographic information systems used for address finding in public safety emergency response, the volatility is low and the information only intermittently updated. For volatile, dynamic information, such as personal circumstance or even weather and traffic conditions, current information and contextualised insight are critical.
This contextualised insight moves beyond simply presenting relevant information to predicting likely or possible outcomes based on current circumstances. Operationalised analytics is being used to support decision-making in human and social services, government revenue collection, public safety, law enforcement and intelligence services, and continues to expand.
Meeting need for better data and analytics
The need for better data and analytics has been developing for some time. According to Gartner’s 2020 CIO survey, 61% of government CIOs said that AI and machine learning (ML) capabilities were either already in place or would be targets of investment within 24 months.
Traditionally, analytics came from a history of reporting to provide management information. Information flowed up from operations and was summarised for management decisions. Management then decided on a response, by reallocating resources or changing policy; this then flowed down into the front line and was operationalised.
This created a long time lag between the collection of information and government response. This was further inhibited by the complexity of metrics and measurement in government — there’s no simple alignment with cost, profit and market share as found in commercial organisations.
Measuring success in government is further complicated by the fact that the missions of different government organisations often interact — health with human services, taxation with benefits claims and financial support, road safety with traffic flow optimisation and emissions.
Historically, there were two main blockers: a lack of experience with powerful analytics and an immaturity in the technology that limited the ability to make them usable outside historical analytics/reporting areas. These blockers have been significantly eroded.
At the same time, COVID-19 has forced a realisation of the value of analytics in operations. It demonstrated the ability to share data across the silos of government and the benefits it can deliver. The technologies involved have been available for sufficient time to become usable at a much greater scale by more people. AI/ML techniques are also advanced enough to allow them to be reliably embedded in operations.
Government organisations can now improve the quality, consistency and timeliness of their services and decision-making. The focus of service delivery can shift from reactive to proactive. Knowledge workers can also be freed up by reducing the effort spent doing repetitive administrative tasks or collecting data available by other means.
These developments started accelerating before the pandemic, but addressed more demanding citizenry requiring services from agencies with constrained budgets. This was driving AI-based automation (eg, intelligent process automation) and risk-based case assessment based on advanced analytics.
Government leaders will continue to deal with challenges associated with oversight, control, accountability and the ability to explain the basis of decisions. Operationalised analytics allows people to remain central to the process and benefit substantially from additional tools and insights.
Analytics, a core business function
For governments to scale and reap the benefits of digital transformation, CIOs must integrate AI and data and analytics capabilities with service delivery and operational processes. All of this while ensuring governance covers data ethics, usage and quality. This makes data and analytics a core business function, which must be connected, continuous and appropriate in the context of its use at all times.
Data generated by citizen-facing applications, ecosystem partners, the Internet of Things (IoT) and back-office systems requires a flexible analytics architecture that supports real-time analysis and AI-based decision support.
The classic output from many analytics initiatives — the executive/public performance dashboard — will still exist, but will also evolve in response to these new insights.
Government organisations must execute an analytics everywhere strategy that steadily advances the impact and expands the use of real-time analytics capabilities.
How to take action
Start by developing a compelling future-state vision of the business value and public benefit of operationalised analytics. This can be achieved by building a concise data and analytics strategy aligned with desired business outcomes. Sustain this with adaptive governance practices.
Demonstrate the effectiveness and efficiency opportunities of operationalised analytics by conducting pilot projects that have immediate impacts on productivity or morale, amplifying human talents and reducing errors. Common examples include contact centre services or tasks, field services support or even corporate services tasks, such as HR, payroll or finance.
Finally, build a roadmap for capability development by assessing AI and analytics capabilities within your organisation and across your government. Plan to close gaps by contracting with suitable service providers or academic institutions and leveraging cloud-based AI and analytics services.