Good governance is built on merit, transparency, and service orientation. In turn, the guiding principles for ensuring merit and transparency are timely decisions based on facts.
Governments around the world are realizing the importance of timely and accurate information for good governance and are striving to manage rapid growth of information needs. They are looking to be more data-driven in providing services to citizens and it does not stop there.
The planning function of governments is the cornerstone of providing good service to its citizens, which translates into the capacity of governments to ingest, manage, and process massive volumes of data. Government organizations must be able to exchange information, interpret structured and unstructured data, and most importantly, employ analytics to proactively address challenges faced in planning and implementation of these services. The question remains how many government organizations are equipped to do this, how many are geared up to face the challenges of good governance in an age where people are drowning in information, and how many are actually working on providing good governance?
- Thanks to advancement in information technology, big-data analytics—the process of examining enormous amount of data and uncovering insights, trends, and patterns—is the norm today. This helps us measure the magnitude of any given problem and make more informed decisions. Governments and organizations can employ big-data analytics to proactively address challenges faced in planning and implementation of services.
Government agencies are increasingly under financial and human resource pressure to cut costs, while at the same time, they are expected to deliver better and more efficient service. Any organization’s most important assets in fulfilling its mission can be broadly broken down into: people, tools and processes, finance, and information or data. Of these, data is the only asset expected to grow in volume and importance.
Realizing the importance of harnessing data and utilizing it for planning and facilitation, the Government of Pakistan established the National Database and Registration Authority (NADRA) in 2000. Its aim was to store complete information related to every citizen of the country. As a first step, NADRA was to print electronically generated, secure, and modern identification (ID) cards for every citizen of Pakistan. The solution had to be flexible enough to allow for the inclusion of data from other government departments at a later stage. NADRA has since evolved over the years and has transformed itself into a leading implementer of IT driven solutions in the government sector, not only in Pakistan but also across the region.
Government agencies are increasingly under financial and human resource pressure to cut costs, while at the same time, they are expected to deliver better and more efficient service.
Today, NADRA serves as a single, authentic point of reference for citizens as well as planning authorities. This means that once a citizen’s record has been verified and entered in the data warehouse, the individual becomes eligible for facilities like passport, identity card, motor vehicle registration, driving license, arms license, and domicile certificate, etc.
NADRA has the potential to use currently available data and supplement it by additional sources of data from other government departments engaged in planning and giving services to citizens. This will enable the different planning and development arms of the government to conduct detailed analysis and project future trends to determine the specific needs of citizens. These may be in areas like education, healthcare, housing, finance, population planning, disaster management, infrastructure development, and manpower development.
Analyzing structured data is one approach to using data. Recent advancements in information technology have also made it possible to collect and use unstructured data through big-data analytics. Big-data analytics combines tools and processes to enable individuals to examine massive data sets by unearthing hidden patterns, trends, citizen preferences, previously unknown correlations, and other useful actionable knowledge.
The analytical insights that NADRA can develop using big data could facilitate improved citizen engagement, operational efficiency in various government functions, cost savings, new revenue opportunities, and effective methods of ensuring compliance with government rules and regulations.
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Within the sea of opportunities to utilize data for effective, timely, and transparent decision making, some of the key big-data analytics for NADRA could be in the following scenarios:
Scenario 1: Crisis and Disaster Analytics
NADRA can assist the National Disaster Management Authority (NDMA) for timely forecast of crises and disasters. This can be possible with use of big-data analytics to identify correlations, clusters, and trends of crisis occurrence by identifying sensitive locations, hotspots, and predicting the next occurrence.
Thus, the analysis generated can be used for crisis preparedness by including pre-incident analysis to prepare for quickest possible response actions including logistical support and movement. Analytics would also cover crisis response analytics based on real-time geographic information systems (GIS), human resource statistics, and other resource analysis.
Post incident analysis like dispatch analysis, proximity of response units, vehicles, and medicines inventory can provide invaluable inputs for developing an effective strategy for future disaster response based on data and analysis, for example, the integration of GIS data with non-GIS data, crisis recovery analytics, volunteer coordination, and logistics, etc.
Scenario 2: Cybersecurity Analytics
Cybercrime is a well-documented and costly threat. Government agency targets represent 14 percent of the typical total cyber attacks. Most security technologies are built to minimize a specific attack and are not built for change. This means there is almost always a time lag between new cyber threats and counter security measures. The growing sophistication or addition of more security tools results in a highly instrumented network that generates high volumes of data.
There is a need to develop a comprehensive data architecture system to combat cyber threats using big-data technologies. Structured and historic data can be combined like past signatures and intrusion patterns with other compliance data to quickly identify and respond to threats.
Scenario 3: Suspected Communication Analytics
Social media and internet have become very effective means of communication in modern life. Unfortunately, it has also proved effective for unscrupulous elements too.
As a result, the identification of suspected communication has become the utmost priority for national security and defense of any country as terrorist threats loom over every society. Suspected communications are hard to decipher unless put into the right context. By the application of big-data analytics and advanced analytical techniques on web logs and tweets etc., which are usually in unstructured data formats, social networks can be developed which can easily lead to the identification of suspected communications. Extracting information from data visualization is much easier than from tabular reports, leading to a reduction in timing for making a decision which can prove to be of critical nature in case of security threats.
Scenario 4: Social Services Delivery Analytics
Social sector reforms entail a deep understanding of socioeconomic demographics and more importantly ensuring that such reforms are implemented in a transparent and efficient manner. One key element where big-data analytics can help Pakistan is the management of disbursement of social security payments under schemes such as the Benazir Income Support Scheme (BISP) and financial aid through Bait-ul-Maal.
Both structured and unstructured analytical models can be developed based on the use of data sets gathered from different sources. Analysis can be made of trends and correlations associated with services. For example, geographic and demographic data can be combined with frequency of social service events (such as home-based visits, request for assistance, reports of domestic violence, child support enforcement, or determination of school truancy) to better understand social environment and sensitivities. This can help in mapping population migration trends to plan social services in an efficient manner.
Furthermore, analytics can help in proactively reaching out to potential recipients of social services. At the same time, their alignment with service programs can be monitored and compliance ensured.
Scenario 5: Sentiment Analytics
In present day scenarios, any action by government and any issue of concern to the general public is widely discussed and debated upon on social media forums such as Twitter and Facebook. Search engine results reflect these sentiments. In fact, these channels have become instrumental in not only rapid dissemination of information but also in influencing public opinion as has been witnessed during the Arab Spring. Big-data analytics involving data gathered from various social websites can help give insights on public sentiments related to an event, political or social etc. An understanding of public sentiment can help the government take corrective and proactive measures to address the situation at hand as well as identify the triggers of such issues.
Government organizations must be able to exchange information, interpret structured and unstructured data, and most importantly, employ analytics to proactively address challenges faced in planning and implementation of their services.
At the same time, such analysis can help government and its various departments understand feedback about the working and efficiency of different departments which would support improvement in performance of these departments.
Scenario 6: Crime Classification and Hotspot Analytics
Big-data analytics can also aid governance in public safety related matters. NADRA can support law enforcement agencies in identifying crime hotspots. This will help police officials set daily intelligent checkposts and beats reconfigurations, leading to reduction in street crimes. This can be achieved by using data from various sources to find hidden patterns in crime data.
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The above analytics can be used to intelligently predict the next possible type of crime in a particular locality or during a particular timeframe to generate dynamic crime hotspot lists, efficient patrolling routes, crime patterns, and future potential crime trends.
Scenario 7: Psycholinguistic analytics
Another very important area of analytics in present day scenario is the use of data from different websites and social media forums to identify extremist content on social media being spread by radical groups. Such analytics would help to timely identify radicalization of thoughts, tweets, and posts on social media that are spreading extremism and identify people with radical disposition based on their posts.
NADRA has the potential to use available data and supplement it by additional sources from other government departments engaged in planning and providing services to citizens.
Furthermore, the analytics can be used to identify common topics and trends in online posts as well as to map the social networks of extremists. This can also help find profiles on social media platforms that are used by terrorist groups to spread messages and tag individuals on social media based on their psycholinguistic profiles.
Above are just a few examples of how NADRA or other federal and provincial organizations can take advantage of big-data analytics to facilitate the public, plan efficient utilization of resources, and implement effective public safety and security programs.
The success or failure of organizations will depend on how well they are able to use data to take timely, verifiable and proactive decisions.
Khuram Rahat is Managing Director Teradata Pakistan, Afghanistan and Bangladesh.