The Government of the Future is Built on Data
Government agencies take a cue from the private sector, looking to data to improve operations.
The explosion of electronic data has led many businesses to uncover hidden insights that create revenue and stronger relationships with their customers, employees, and shareholders. Researchers at MIT and Wharton have calculated that data-driven decision making (DDD) is responsible for up to a 5 percent increase in productivity and output, as well as significantly higher profitability and market value.
Businesses across multiple sectors use business analytics to gain a better understanding of customer behavior and make informed decisions to manage performance. However, greater awareness of data's effective applications within government is a more recent phenomenon. In fact, the public sector lags considerably behind the private sector in terms of using data to make strategic and tactical decisions.
Government organizations can utilize analytics to become more efficient and effective in their delivery of services by creating a holistic view of individual citizens, thereby ensuring government programs and services address their needs. DDD can also help improve accountability and transparency, which are demanded by citizens around the world. Using DDD technologies and techniques, the public sector can make decisions that are based on facts rather than assumptions, politics, or hunches.
Data: The new public servant
Since the credit crunch started in 2008, growth has stagnated and many agencies are under huge pressure to cut costs and balance budgets. While government budgets are closely scrutinized by the public, agencies are also looking for ways to innovate on service delivery at the same or lesser cost. E-government initiatives are a common way to achieve both.
However, governments realize that they cannot meet societal needs merely by computerizing service procedures to continue a "government-to-citizen" operational model. A large proportion of the population is well informed and demands the same smart service from government as they do from the private sector. In response to these heightened expectations, governments need to treat their citizens like customers.
Many government agencies have started to integrate communication channels including email, call centers, and websites to deliver coordinated messages. And all of these involve data. More advanced entities have streamlined services and created all-encompassing e-government portals to allow citizens to self-serve for activities such as paying bills, making complaints, filling out forms, etc. Services are more accessible, processes are standardized, and data is shared across different departments and channels. Some portals include forums and blogs for citizens to share their opinions online. Each of these capabilities enables governments to serve citizens well and drive higher rates of citizen satisfaction and trust by promoting awareness and access to services. And following this rapid development of public service modernization, vast amounts of data are now being collected.
Some developed countries have embraced the "citizen-centric" service delivery model by designing services around the needs of different citizen segments. Many segments have been created from basic demographic information. Yet with diverse groups in terms of age, education, ethnic origin, and area of residence, along with the rapidly growing volume of structured, semi-structured, and unstructured data available in a variety of channels, more complex segmentation strategy is necessary.
This presents great opportunity. DDD can be used to accurately identify relevant citizen segments to optimize resource allocation, achieve better engagement with citizens through listening and conversing, and proactively deliver the right service to the right people. In other words, to transform a service model from "What service do you need?" to "What is your situation, and would you like to use services that may be relevant to you in this situation?"
The future starts now
What can leaders do to make the most of the data potential within their agencies? It starts by creating a vision of the ideal experience, then using data to turn the vision into reality. Some government solutions recommendations include:
• Active DDD sponsorship by senior leadership: Any real success must be led from the top, such as the head of a ministry, government agency, or a corresponding public authority. -The best example of this in recent times is the analytics initiative used in the 2012 U.S. Presidential Election overseen by Jim Messina. His team used analytics to understand voter behavior around the country and drive campaign strategy on both the national and local levels. "Measure every single thing in the campaign," Messina told Timemagazine. The initiative received an active blessing from President Obama. Such passionate leadership is required for game-changing programs to work.
• Analytics experts must be project owners and evangelists: Any DDD effort must be led by an individual who inspires users to embrace the language of data, both inside and outside the organization. This involves opening the minds of government officials to visualize capabilities beyond basic reporting and query functions to include statistical analysis, forecasting, predictive modeling, and optimization.
• Overcome data silos and build consolidated data marts: The importance of this step cannot be emphasized enough. As evidenced across different case studies, this is requisite in order to efficiently uncover relationships between disparate data sources. Data stored in different formats and locations should be brought together in one consistent format. And more importantly, data quality should be evaluated on an ongoing basis. Knowledge of domain, data understanding, and technical prowess are the core skill sets needed to make this successful.
• Provide practical analytical training to motivate and empower staff:Employees need the expertise to leverage clean data into insights using a combination of statistical and optimization techniques. They must be allowed to make quick decisions when dealing with constituents based on this data.
The essential elements outlined here don't have to be a big bang, multiyear approach with huge technological and manpower investments. Yes, data-driven decison making is a journey, and many government bodies aren't built to handle such a paradigm shift all at once. But there are creative ways to start small, prove the value of the initiative, and then build it into a larger program. We believe that each government entity that takes small but firm steps in this direction will result in sustainable success for all.