In some of the most developed countries, there is pervasive use of analytics for a variety of purposes. According to the latest US General Accounting Office report, one can see high level and types of usage across different departments, with departments of defence 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; analysing scientific and research information; managing human resources; detecting criminal activities or patterns; and analysing 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 the cost of storage, which means larger amounts of data can be kept cheaper than ever before.
So, the question is, how can local adoption and consumption of analytics in the public sector be increased to the levels close to the usage of so-called 'first world' countries? There is a tremendous need in South Africa for analytically-enabled applications across the board. Imagine the benefits of early warning systems (EWS) that can "ring a bell" just before a crisis for fast response times.
This is applicable in all government departments - from an early warning detection system in Eskom's production units that could "ring a bell" just before an unplanned outage, or an early warning detection system that would indicate water pump failure, like the one now that caused week-long water shortages in areas around Johannesburg.
In short - wherever there is the potential for crisis - the concept of EWS is applicable. Nowhere is the word 'crisis' used as often as in context of South Africa's public health delivery. Everyone knows that staff shortages are major contributors to the poor state of affairs in the area of public health. What is not clearly known is the magnitude of the difference and ranking order between different hospitals in different areas. And that is exactly where analytics can assist, so the priority of the response is given where the problem is most prevalent. Some of the government policies and programmes are not only ineffective in reducing problems, but directly contribute to the underlying cause. That means real-time awareness and feedback are painfully lacking.
This is precisely where analytical technologies can massively assist, so governmental programmes 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 the majority of learners came from rural villages on the other side of the river. For most part of the year, the 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-stream. This could be prevented through analytically enabled decision-making - because similar event causality happened before in same region.
Or, the case of a children's hospital where analytics have found mismatches between causes and outcomes. If the stated injury is caused by a fall from the bed and this specific type of injury and symptoms is highly unlikely to be caused by the same cause, well, could it be that parents are lying and the child has been abused? After further scrutiny of such cases, that is exactly what has been discovered. As a result, this specific children's hospital has implemented a policy that for any injury pattern discovered, where the stated cause of injury doesn't match the expected set of symptoms - social workers should be alerted to have a closer look at such a family.
Another example is that certain government hospitals are persistently far above the average instance of infant mortality. The fact that some of these hospitals are worse on an ongoing basis suggests there is some negative pattern at work that is causing the mortality numbers to be worse than elsewhere. Analytics can potentially extract such a 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 the needed change of management culture. That is why improvement is hard to come by. There are some pockets of excellence in the public sector that prove 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 the idealistic concepts of "smart and just city" more achievable.
In other words, there is a 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 but not least, more effective transformation and empowerment of previously disadvantaged sectors of society - far more of a reality for tomorrow than it is today.
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