Big data, machine learning, artificial intelligence, data lakes, analytics – a whole new language has emerged on all things data in government circles. Some of this is genuinely exciting. But a lot more is overrated; overhyped, even.
Governments will always have a lot of complex problems to solve, and anyone bearing ‘solutions’ that claim to be ready straight out of the box will tend to get a hearing. But many governments have learned the hard way that buying your way to a technology revolution can be a long, expensive road with a dead end.
This is especially true in the case of health where, even though there is no shortage of ‘solutions’, in many cases data are not translated into insights for those involved in generating it. Having the right institutional structure and the right incentives, however, can help.
Build a Team Not an App
Therefore, rather than simply get out their wallets, some governments around the world have been prepared to think harder about how to get the most value from the genuine improvements better use of data can bring to public services. Those that have been most successful have invested not just financially, but in making changes that improve data infrastructure, internal processes, or public services to make the most of what they have.
As one would expect in an evolving field, there is variation in the strategies and tactics adopted by such government initiatives. Some teams have placed a different emphasis on whether to be led more by technology choices or by opening up data. However, across them all, several common trends are beginning to emerge among the teams currently having the most significant impact on public life. They tend to blend together a multidisciplinary set of skills within the team, working across organizational silos rather than creating new ones. They also focus on ensuring their choices about technology and data are made to serve user needs. A study commissioned by the Inter-American Development Bank presents a summary of the main choices and characteristics of successful data analytics teams in government.
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Big Data can Potentially Aid Governments for Good
The impact that in-house data analytics teams are having in government is often quiet but profound. They can impact the economy. In New York City, the Mayor’s Office of Data Analytics created a Business Atlas for entrepreneurs with detailed information about economic activity, demographics, foot traffic and other key business metrics, and reduced time-to-open for new food service establishments by an average of 45 days.
They can improve resilience. In the wake of Hurricane Patricia in Mexico, open data curated by the government’s data analytics team was used by some 400 global volunteers to map 9,000 kilometers of roads and 90,000 buildings and hotels and potential mudslide zones, providing over 1 million people with vital information in real-time.
And they can improve vital public services. In Singapore, analysis by the data analytics unit revealed a hidden pattern between incidents causing delays: one particular train on the Circle Line emitting a signal that was jamming the signaling mechanism of the tracks.
In healthcare, more examples are springing up every day. From telesonography to biometrics, the potential of data analytics to save lives is constantly being explored.
These examples are perfect demonstrations of how an analytics team operating with cross-agency collaboration, plus the right data and skills, could deliver tangible service improvements quickly, and at scale. The combination of data analytics and open communications is a compelling partnership in terms of achieving public and political visibility.
The Authentic Data Revolution
Data analytics initiatives share some qualities in their approach, but there is no single set of ‘right answers’ to creating an effective data analytics team in government. In fact, teams can be set up with quite different governance structures, appropriate to the institutional context of the government they work in. Where a data analytics team sits in the organizational structure, who they report to, what their mandate and scope are, what external partnerships and stakeholders they have, and what skills make up the team can all differ from case to case.
What they share is that they are built on the foundations of a multidisciplinary team able to ship quickly, call on political support and sponsorship, work with a mandate to focus ruthlessly on user needs and able to build the data literacy of their colleagues in public service.
With the investment of time, money and political capital to put that together, governments can harness the data revolution in a way that takes them much further than the hype.
In what ways is your government harnessing the power of big data to go above and beyond, and do so transparently? Leave your comment below or mention @BIDgente on Twitter.
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