Notes from Policy in the Data Age: Data Enablement for the Common Good

Policy in the Data Age: Data Enablement for the Common Good


By Karim Tadjeddine and Martin Lundqvist

Digital McKinsey August 2016

By virtue of their sheer size, visibility, and economic clout, national, state or provincial, and local governments are central to any societal transformation effort, in particular a digital transformation. Governments at all levels, which account for 30 to 50 percent of most countries’ GDP, exert profound influence not only by executing their own digital transformations but also by catalyzing digital transformations in other societal sectors

The data revolution enables governments to radically improve quality of service

Government data initiatives are fueling a movement toward evidence-based policy making. Data enablement gives governments the tool they need to be more efficient, effective, and transparent while enabling a significant change in public-policy performance management across the entire spectrum of government activities.

Governments need to launch data initiatives focused on:

  • better understanding public attitudes toward specific policies and identifying needed changes
  • developing and using undisputed KPIs that reveal the drivers of policy performance and allow the assignment of targets to policies during the design phase
  • measuring what is happening in the field by enabling civil servants, citizens, and business operators to provide fact-based information and feedback
  • evaluating policy performance, reconciling quantitative and qualitative data, and allowing the implementation of a continuous-improvement approach to policy making and execution
  • opening data in raw, crunched, and reusable formats.

The continuing and thoroughgoing evolution taking place in public service is supported by a true data revolution, fueled by two powerful trends.

First, the already low cost of computing power continues to plummet, as does the cost
of data transportation, storage, and analysis. At the same time, software providers have rolled out analytics innovations such as machine learning, artificial intelligence, automated research, and visualization tools. These developments have made it possible for nearly every business and government to derive insights from large datasets.

Second, data volumes have increased exponentially. Every two years the volume of digitally generated data doubles, thanks to new sources of data and the adoption of digital tools.

To capture the full benefit of data, states need to deliver on four key imperatives:

  • Gain the confidence and buy-in of citizens and public leaders
  • Conduct a skills-and-competencies revolution
  • Fully redesign the way states operate
  • Deploy enabling technologies that ensure interoperability and the ability to handle massive data flows

Because data-specific skills are scarce, governments need to draw on their internal capabilities to advance this revolution. Civil servants are intimately familiar with their department’s or agency’s challenges and idiosyncrasies, and they are ideally positioned to drive improvements—provided they are equipped with the necessary digital and analytical skills. These can be developed through rotational, training, and coaching programs, with content targeted to different populations. The US is building the capabilities of its employees through its DigitalGov University, which every year trains 10,000 federal civil servants from across the government in digital and data skills.

More generally, governments should train and incentivize civil servants to embed data discovery and analytics processes in their workplaces. That means that all civil servants’ end-user computing platforms must feature data discovery and analytics tools.

governments must carry out a major cultural shift in order to break down silos and barriers. Such a transformed culture is characterized by a “test and learn” mind-set that believes “good enough is good to go.”

Cultures that facilitate governments’ data transformations are also characterized by
open, collaborative, and inclusive operating models for data generation and data usage. They facilitate the participation of public agencies, private-sector companies, start-ups, and society as a whole.