This article was originally published on govinsider.asia and reproduced with permission from GovInsider.
Every year, nearly two million people throng the metro train lines of Mecca to visit holy sites across the city during the Hajj pilgrimage.
The local government must keep these crowds moving efficiently and ensure their safety, while coping with a huge surge in pressure on the city’s infrastructure. In recent years, it has relied on real-time data to cut overcrowding at stations along the pilgrimage route and allocate the right number of staff to each station. The city has also linked up different parts of the government, alerting first responders to any emergencies as soon as they happen.
Data, when used intelligently, can help governments transform and dramatically improve citizen services. Here are four ways you can use data more cost-effectively and efficiently.
Manage data cost-effectively
Government agencies need to collect massive amounts of data to make good policy decisions. “You need a lot of data points,” says Ivan Phoon, Practice Advisor of Big Data and Analytics at DXC Technology. This includes “structured data” like survey responses in text form, but also more unstructured ones like social media. “Sometimes you need to collate across multiple agencies,” he adds.
Managing and maintaining all this data can be expensive if not done right. The most cost-effective way to do it is with a solution called a “data lake”, Phoon says. It pools and stores information from public agencies across the whole of government, ranging from relevant trusted agency data and population censuses to crowd-sourced data such as Facebook comments in a single database. This “provides a convenient repository for all available data, so that I can find it when I need it”, he explains.
Public officials across agencies can use the data in the lake to extrapolate and predict for policy studies, saving cost and time on data collection. The data is collected once and used multiple times for both current and future use cases. “Once you’ve done that, it’s really to develop different kinds of modelling scenarios to do ‘what if’ analyses, and different types of extrapolation so that you can see what can potentially happen in 10 to 20 years,” Phoon continues.
However, agencies will need to ensure this does not become a convenient dumping ground and that data can be found easily. Data must be arranged in a way so that non-technical users are able to find what they need quickly. Agencies can create “a catalogue of sorts, for example, so people understand how to navigate the data lake”, he adds.
Governments also need to set data standards across agencies to allow data to be integrated more efficiently. Governments should “centrally define certain standards of data sharing,” Phoon says, to allow agencies to use data that belong to others. “Across government, if you don’t have the same standards, it’s harder to analyse data gathered across agencies.”
Standards can be implemented early by building them into procurement systems. For instance, the Scottish government built a procurement hub which provides vendors with consistent data standards and practices. Through the programme, 106 Scottish public sector bodies now share the same data collection services, and examine procurement spending through the same lens.
Build the right teams
Agencies need the right people who will find ways to use data in a meaningful way. Agencies can set up an innovation task-force with “a leader who understands not just business, but also the technology applications that can enhance strategic, tactical and day to day operations”, Phoon suggests. Officials in this team should understand how technology can be used daily, and also have the foresight to identify and innovate for the problems it can solve.
The Singapore Government, for instance, has recently assigned Chief Digital Strategy Officers in ministries and agencies with the mandate to lead and implement digitisation plans across the enterprise. They will work with Chief Information and Technology Officers who will provide technical expertise.
An important step in planning a data analytics roadmap is an “enterprise data architecture”, says Phoon. This ensures that everyone involved is on the same page, by providing a consistent framework to manage data from different sources. “It serves as a consistent anchor that will allow teams to work more efficiently together,” he says.
The management also needs to buy into the importance of using data analytics. “You need executives at the top management level to be able to envision and support where data can lead to; those driving forces will then expand to the ground,” Phoon notes.
Find critical skills
With governments looking to overcome a shortage of data science specialists, they must work with the private sector and institutes of higher learning to ensure they have access to the latest skills. “Data analytics is a very broad field, and it has changed a lot recently because of big data, cloud, AI and machine learning,” Phoon remarks.
As tech is a rapidly innovating industry, local education institutions often cannot quickly churn out graduates with the necessary practical expertise and experiences in data analytics. Businesses can help remedy this shortage by readily providing data experts, who are equipped to advise and work with governments. “DXC Technology brings to government agencies a creative pool of people who can help them accelerate data projects that may be difficult,” says Phoon. “They can actually accelerate their initiatives working with additional trusted partners like us without having to find and train new people.”
DXC has designated teams across the world that survey local governments, allowing the company to develop a deep understanding of local sensibilities and data analytics needs. “We are able to share proven practices across our other clients in governments,” Phoon adds.
Agencies just need to keep four things in mind to make data analytics more accessible to everyone: costs, standards, teams and skills.