The approach of making government decisions based on the collected information is not a new idea. It can be dated back to the age when data was not stored electronically.
Traditionally, data is isolated in different organization silos. Government organizations have collected and managed data only to serve their own business interests and to support internal reporting needs. As a result of this culture, extracting intelligence from the data across different government organizations is difficult.
As government decisions become increasingly complex, government organizations are trying to find new data-driven solutions to help government make smarter decisions, improve productivity and deliver greater citizen services.
Data-driven is more than just data sharing or centralizing data. It is to create a culture of data-driven thinking. The following five practices, if they can be accomplished, will help government organizations moving toward this culture.
The start point of any data-driven approach is to understand the organization’s business processes including organizational visions, business practices, information flows and data management systems.
It is also important to understand the regulations and policies affecting the data activities. This includes regulations for data collection, standards for data management and policies for data sharing.
With all the overwhelming data in place, the linkage between the organization functions and the available data elements needs to be established. In addition, the data gaps and the potential solutions to fill the gaps also need to be identified and documented.
A huge volume of data is being collected every day by governments. Many of these data elements have never been shared across jurisdictional boundaries.
Data is widely dispersed in different domains of the government information systems. Connecting all these data “nodes” can maximize the values of the data. Many government programs have started establishing cross-jurisdictional data working groups with the aims to promote collaboration in data activities and problem solving.
Integrating and coordinating different data elements are difficult to manage and a lot of fears of the associated risks behind the concept. However, these technical and communication barriers are still manageable. A successful case is the U.S. Government’s open data website data.gov.
Data is useful only if the embedded intelligence is extracted. Usually, governments can progress through three steps for data-driven decisions.
The first step is to understand the business goal and choose the right metrics. The designed metrics should be quantitative and measurable, and they should not require a lot of time to explain. Successful metrics will not only impact the current decision; it will continue to be a benchmark to track and assess the status of a specific business process in the future.
The second step is to do the research and find the right data. In this era, we hear about Big Data all the time but people rarely talk about right data. If you have the right data, it does not have to be “Big” in volume. A small amount of right data will work well to solve most problems.
The third step is to bring data to life. When creating data visualization, focus on the trend and data correlation instead of simply presenting the results. Trend and correlation are usually the two key factors to influence decisions. In addition, try to use simple visualization design and plain language to clearly deliver the messages, and listen to the feedbacks from the decision makers to improve the approaches.
Mapping is a big subset of data visualization. It allows us to combine multiple layers of geographic information or metrics into one visualization piece, which makes it powerful to quickly draw audience’s attention and to create multiple perspectives from data.
Interactive mapping applications are becoming emerging tools for data visualization and information sharing. With the interactive mapping applications, data can be visualized at any level of granularity. Sensitive data can be aggregated into boundary-level summaries to enable information sharing, while protecting personal and privacy information.
A good mapping product can show how different factors affect the outcomes. Decision makers can use the story told by the maps to plan better government services. For example, overlaying the transit facilities on the population/employment density map can help transit agencies understand locations of transit service demand, proximity of riders to services and areas with service deficiency.
Government organizations collect, produce and purchase a broad range of different types of data to support government programs. Many years ago, a wealth of these high-value datasets, whether that is country-wide census, road congestion monitoring data or satellite/aerial imagery, were still largely unexploited because of confidentiality, privacy or proprietary.
To create new value from data, over the last several years, many government organizations in North America have initialized programs to release government data as open datasets.
In 2009, the U.S. government launched data.gov. By November 19, 2015, a total of 189,062 datasets are published in data.gov.
Government of Canada also launched its first-generation Open Data Portal data.gc.ca in March 2011. Now there are 69 government organizations in Canada that have launched open data programs.
Open Data commitments continue to grow in the years to come because it creates new values and new business opportunities for both government and the community.
In summary, the technologies behind data science are evolving rapidly and have made data-driven government possible. Now it is not a matter of whether it can be done; it is a matter of when and how.
It’s clear that using data in decision-making can be a huge contributor to improve efficiency and accountability of public services. Moving toward data-driven government is a strong and long-term commitment. However, these five best practices can be embraced by government organizations in a shorter term to start the journey.