SOME 2,500,000,000,000,000,000 bytes of data. That’s the amount of data the world produces each day -- in quintillions. More than 90 percent of the world’s data today were generated only in the past three or four years.
Today, the world uses this huge amount of data to understand the past, solve the current problems and reimagine the future. The application of data especially in government will have limitless possibilities and potential for providing responsive, efficient, effective, competent and trustworthy public services. The big question to ask is, is our government data-driven in making decisions or simply doing plain and simple guess work most of the time?
In their 2019 research, “A data-driven public sector: Enabling the strategic use of data for productive, inclusive and trustworthy governance,” Charlotte van Ooijen, Barbara Ubaldi and Benjamin Welby presented the idea of the government data value cycle, which identified the stages that data need to undergo so that it achieves its maximum public value. The cycle helps track the journey from handling data, including raw, isolated and unstructured dataset, to identifying and understanding the relationships between those data, resulting in information and knowledge, to serve as the basis for governments to take action and decisions, whether strategic, tactical or operational.
This data-driven public sector (DDPS) model presents four phases of data in government, namely the collection and generation of data; the storing, securing and processing of data; the sharing, curating and publishing of data; and the use and reuse of data.
Government collects huge amounts of data and datasets daily from frontline public employees who have direct mandate for data collection, which comes in many forms and from multiple sources. From various government transactions, health information, business permit and licensing transactions up to citizen’s feedback mechanisms. If our governments are smart, daily feed from different sensors, using Internet of Things (IoT) devices can easily generate data in real-time from the streets.
Data can also be actively requested as part of the design of a public service, like forms collecting information from the public or logged in customer relationship management software following subsequent follow-up enquiries. Covid-19-related surveys and contact tracing applications generate many data on which decision-makers can develop their strategies.
There are data produced as the output of government activity, such as procurement and supply contracts which could serve as a basis for creating more transparent and accountable systems. There are data that are held by the private sector working in conjunction with the government to deliver goods and services. All these data can serve as a basis for effective policy-making and good governance.
Using data analytics, governments can use data for descriptive analytics to understand the current situation; diagnostic analytics to know what went wrong and how things happened; predictive analytics to create models for future use as solutions, and prescriptive analytics to develop and prescribe solutions in the forms for government measures, policies and programs.
Once data are identified, collected and generated, they must then be stored, secured and processed. This phase is highly important on the role of data and public trust. Here, legalities and legal basis may have to be considered especially on handling requests for access and agreements to share data that are not openly available. Countries who have clear policies about data-sharing and interoperability between government institutions have squarely addressed this issue. The Philippines has yet to strengthen its data-sharing policies for all institutions to be inter-operable.
The final phase of the cycle, the use and reuse of data, is considered as a clear opportunity for generating visible public value. For data to be an asset, it has to generate public value. But the use and reuse of data can only be of public value, if the process is supported by a broader ecosystem of data governance.
The DDPS shows that by improving the management and application of data at each stage, policy makers and public officials can increase their effectiveness by enhancing their data capabilities and ultimately generating greater public value.
Potential public value includes gathering insight on existing policy activity; understanding the issues facing stakeholders; foreseeing new trends and needs; delivering higher quality services; designing and adapting innovative approaches; monitoring ongoing implementation activities; and managing the resources being used to address a particular challenge.