Tuesday, October 14, 2014

How could government agencies in South Africa benefit of greater use of analytics

In some of the most developed countries, there is pervasive use of analytics for a variety of purposes. According to latest US General Accounting Office report one can see high level and types of usage across different departments, with departments of defense and of homeland security being slightly ahead of others. Primary purposes of using analytical technologies in the government sector are improving service or performance, detecting fraud, waste, and abuse, analyzing scientific and research information, managing human resources; detecting criminal activities or patterns; and analyzing intelligence and detecting terrorist activities. This is motivated by growth in the volumes and availability of data collected by government agencies and by advances in analytical technologies that can be deployed on such information. Another contributing factor is decreasing cost of storage which means that larger amounts of data can be kept cheaper than ever before. 

So, question is how can local adoption and consumption of analytics in the public sector be increased to the levels close to usage of so called “first world” countries? There is tremendous need in South Africa for analytically-enabled applications across the board. Imagine benefits of “early warning” systems (EWS) that can alert before the crisis allowing for fast response times. This is applicable in all government departments – from early warning detection systems in Eskom’s production units that could “ring bell” just before unplanned outage! Or early warning detection system that would indicate water pump failure like the one now that caused week-long water shortages in areas around Johannesburg.

Word "crisis" is often used in context of South Africa’s public health delivery. Everyone knows that staff shortages are major contributor to the poor state of affairs in area of public health. What is not clearly known is the magnitude of the difference and ranking order between different hospitals in different areas. Some of the government policies and programs are not only ineffective in reducing problems but directly contributing to underlying cause. That means that real-time awareness and feed-back are painfully lacking. This is precisely where analytical technologies can massively assist, so that governmental programs and policies much better represent reality.
There was a case in one province where the school was built on one side of the river, while majority of learners reside in rural villages on the other side of river. For most part of the year river is not difficult to cross, but for one month of the year this river is heavily flooded and dozens of children die every year by being swept by heavy flood-streams. This could have been prevented through analytically enabled decision making.  

Think of a  case of a children hospital where analytics have found mismatches between causes and outcomes of injuries. If stated injury is caused by the fall from the bed and this specific type injury with its symptoms is highly unlikely to be caused by the same cause – well, could it be that parents are lying and child has been abused? After further scrutiny of such cases – that is exactly what have been discovered. As a result - this specific children’s hospital has implemented policy that for any injury pattern discovered, where stated cause of injury doesn’t match with the expected set of symptoms – that social workers should be alerted to have closer look at such family.

Another example is that certain government hospitals are persistently far above the average of instance of infant mortality. The fact that some of these hospitals are worst on on-going basis suggest that there is some negative pattern at work that is causing the mortality numbers to be worse than elsewhere. Analytics can potentially extract such negative pattern and by breaking this pattern through appropriate measures and actions – one can reduce this problem to average or below average levels – and this reduction of the problem can be directly attributable to actionable analytics.

And then there is massive problem of fraud, waste and abuse where analytics can be used for detection and ultimately – prevention. But, what is the main reason for slow adoption of cutting-edge analytical technologies in public sector? Yes, there may be issues with data quality and access, issues with a shortage of skills and lack of analytical technologies – but the biggest challenge is lack of motivation. Neither, penalty for doing nothing, nor award for doing something is strong enough for needed change of management culture. That is why improvement is hard to come by. There are some pockets of excellence in public sector that proves that analytical technologies can effectively be used to vastly improve service delivery and performance, reduce the fraud, better represent reality for better decision making and ultimately make idealistic concept of “smart and just city” more achievable. In other words, there is strong case for greater usage of analytics where communities are built on sustainable economic development and high quality of life, with lesser crime, greater and quicker justice delivery and with wise management of natural resources, and last bit not least – more effective transformation and empowerment of previously disadvantaged sectors of society - far more of a reality for tomorrow than what it is today.
Goran Dragosavac

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