Is Ghana ready for Artificial Intelligence (AI)? This question was posed to me by a respected Multimedia anchor when I appeared on Joy Business TV to talk about Big Data Analytics (BDA) during my recent visit to Ghana. My answer was simple- you cannot think and talk AI when the fundamentals of your data economy are weak.
Like it or not, we are living in a data-driven world today where data impacts every facet of our lives. Let me also state that COVID-19 is not just a healthcare problem but inextricably linked to other sectors of the economy. Our struggle with the distribution of meals to 400,000 most vulnerable people should be a wake-up call for us to revisit the importance of our data ecosystem.
It is important to note that COVID-19 goes beyond medicine. It is a complex system involving biology, human behaviour, companies, and governments, and it’s influenced by healthcare, economics, governance, and geopolitics. The common denominator among these sectors is data.
I applaud the Ghana Health Service (GHS), Noguchi Institute, and all allied services for the great work they are doing to combat the current pandemic. This article is not meant to take a swipe at any organization’s effort but rather to discuss how a strong data economy can complement the on-going efforts to fight COVID-19 comprehensively.
It is true that credible organizations like the WHO, CDC, and Johns Hopkins University disseminating vital statistics daily, but to get insights into this data and generate foresight, it is essential to have the fundamental means of analyzing such data. That brings to mind the importance of having the tools and resources of analyzing such data.
Our COVID cases are low, but the long-term impact could be monumental, so we need to start developing our predictive models to fight the disease comprehensively. The truth is that the current COVID fight is being won by countries that successfully leveraged their internally generated data along with data from organizations like WHO, CDC, etc.
For example, China and Germany were quick in mobilizing emergency efforts early on to contain the virus and increase hospital capacity. They utilized technologies, including AI, robotics, and big data analysis, in combination with medical treatment and healthcare management techniques structured in a sophisticated way.
We all know the characteristics of the people at most risk for excess mortality related to COVID-19, including the aged, diabetics, and those with chronic respiratory impairments. However, as a nation, do we have a centralized anonymized database of these COVID commodities for health analytics.
There is a need for researchers and developers to collaborate in using artificial intelligence, machine learning, and natural language processing to track and contain coronavirus, as well as gain a more comprehensive understanding of the disease. We cannot do this with a weak data economy characterized by poorly internally-generated, no matter the sophisticated analytical tools and intellectual horsepower at our disposal.
I spent most of my time during my recent visit to Ghana preaching about the importance of Big Data Analytics (BDA) and its applications in areas like healthcare, epidemiology, journalism, finance, etc. I was also privileged to have organized the first-ever BDA seminar in Ghana, training over 70 participants on the tools and techniques involved with BDA.
This visit also afforded me the opportunity to learn at-firsthand, our readiness to catch up with the rest of the world in making data-driven decisions. Yes, we have the data, but it is mostly siloed and segmented, making it difficult for robust analysis. Thus, when we have data siloed among different MDAs without a proper governance structure, it will be difficult to perform any advanced analytics to drive the necessary results.
This brings to the force the importance of the needed human capital competent in the area of computation and AI, people who understand the biological and biomedical implications, and people who understand population models.
During the BDA seminar referenced above, a colleague mentioned how his wife presented some data collected from Ghana at an international workshop only to realize that it is full of errors. This brings me to the point about data integrity.
Data is the digital oil of the 21st century; thus, the entire value chain from capturing, thorough cleaning to securing needs to be safely guarded. We can no longer allow lowly motivated staff and service personnel to be in charge of data. The other fact is that 80 percent of data analysis effort is expensed in cleaning the data, so it is essential to ensure accuracy at the time of the collection.
In the life insurance industry where I work, we are making drastic changes to our underwriting processes as a result of excess mortality and early contestable claims as a result of Covid-19. Such changes could not be possible without a credible data system.
Insurance companies in the US are utilizing non-medical data to make mortality decisions because we have enough data and have modelled such data to understand the correlation between the two- medical and non-medical issues.
Ghana has some of the brightest and smartest mathematicians comparable to what we can find in other tech hubs like India, so I see an excellent opportunity for our country to be the next destination of analytics business. All we need to do is to appreciate some of the significant obstacles to the adoption of data analytics.
Not sure how data is governed by different healthcare institutions and administrative departments like the NHIS. The integration of these data sources would require developing a new infrastructure where all data providers collaborate to fight a common problem.
COVID-19 should make us think about how we collect, secure, and utilize our data. The on-going effort of the present administration to digitize the economy is a good step towards strengthening our data economy and should be continued.
There is the fear that the pandemic will stick around for years, so it is about time we developed models (other than relying on WHO, etc.) to track and manage our response. Again we have done better in our response so far, but we should use credible real-time data going forward.
Eugene Frimpong is a Senior Underwriting Consultant with 17 years of experience in the US Financial Services industry. He is a doctoral student of Data Analytics with a dissertation emphasis on the readiness of developing economies to adopt big data analytics. Email Address: firstname.lastname@example.org
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