Why is Data Analysis Important for Public Health?

07 Jun, 2021
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Data Analysis was not the first thing that came to our minds when we thought of public health. But the pandemic changed it. We started checking for statistics, rates of growth and decrease to search for assurances and estimates on when to go out and when not to.

Some policymakers and doctors used to think Data with its security and privacy concerns should not be analyzed even for solutions to medical problems. However, now, even they are convinced that analyzing data shows solutions to simple and complex health problems alike.

We need Data Analysis, now more than ever.

Today’s world is too complex that human minds need to view it with a thousand pairs of eyes from a thousand perspectives. Interconnected worlds, fragile economies, densely populated metros, a vast population — these new challenges to healthcare systems only get more complex with time. Our lives, economies, technologies, and our medical problems are changing with various virus and flu threats. Medical researchers cannot succeed in the huge task of keeping pace with fast mutations and effectively combat ill-health without the help of data. After all these waves of the Corona Virus Global Pandemic, we now know this better.

This blog shows how Big Data is essential to effectively direct us towards the right cures to global and local health problems and guide us in developing strategies to combat public health crises.

A couple of decades ago researchers relied on data from people receiving medical help as reported by doctors and diagnostic centers. Extrapolating solutions to people not receiving medical help, people from various financial statuses, ethnicities, and geographies, was a huge challenge. Manually collecting and reporting complex data is not feasible.

With the latest data analytics, advancements in technology, wide use of the internet, and connected gadgets, it is possible to collect both medical and non-medical data of many populations. This data includes medical data of diagnostic lab reports, or non-medical data like search history, social media analysis, etc. Though predicting the future through this method still has a margin of error evident in the global handling of the current pandemic, scientists are trying fast to significantly reduce it.

Deep Learning needs Big Data

Our AI (Artificial Intelligence) systems are mostly based on Deep Learning that needs huge amounts of data for accurate analysis and predictions. The more the data fed into an AI system, the more accurate it would identify the kinds, groups, and names of data. For example, the AI systems used in health care to identify cancer cells study a huge number of images of cells from many people from diverse ethnicities. These AI systems that identify anomalies not only help in diagnosing the problem but are also useful in predicting cancer, and in finding a cure. Similar systems that track inflammations or other changes in body cells are used to study, prevent, and cure many other illnesses.

AI systems in scanning machines, x-ray reading machines, and smart equipment used in hospitals and diagnostic labs are built by feeding them with a large number of images of the things the equipment identifies. The more accurate and sophisticated the systems should become, the more data they need to analyse.

Data Analysis – Major concerns and Cure

Even though Big Data and AI are promising, we are all worried about excessive, unauthorized surveillance. They may pose a threat to both national and personal security. However, these problems created by technology can be won by using technology and by making relevant policies. Critics may propose not relying on technology and finding other means to control. But many in the field believe since it is impossible to not use technology and effectively combat these health crises in today’s world, it is extremely important to build reliable and secure software.

In such a scenario where data needs to be collected to build new solutions to health care problems, finding a common ground of ethics that the collaborators agree and adhere to is important. Governments should also study the systems carefully to regulate and curb misuse of Data. The problem is more about policymaking than technical flaws.

Some successful Software Technology solutions

Here are some technology solutions for health problems that convince us why software technology has become indispensable to tackle health crises.

  • , – These websites present analysed pathogen genomes data to help them understand evolutionary changes in various pathogens like SARS, Ebola, Meningitis, etc.
  • Health Apps in smartphones are used to track the mobility and physical fitness of users to predict lifestyle disorders in mostly urban populations. But since smartphones have penetrated deeper into the countryside the data these apps collect has increased many folds, the data can be used better in predicting changes and analysing various diseases. It can then identify and reduce health disparities.
  • Derive Health Data of focus groups from web platforms to make technology-enabled solutions. Surveys and trends on social media platforms can tell us about food habits, the health status of various ethnic and geographical groups. Establishing relations among the collected data can create health profiles and estimate trends and effectiveness of public health measures. It can measure disease frequency and intensity, predict ailments, and help governments plan public health strategies. On the other hand, it can suggest corporates in the health care sector keen on providing effective health care solutions assured growth or investment options backed by research.
  • Contact tracing software was previously used to monitor criminal movements to protect national security. After COVID-19, digital contact tracing systems using apps are being used widely to tell if a person has met a patient and how long he has been in contact. They have been used widely in almost every country and are likely to be used even in the future to monitor and prevent a possible epidemic or spread of other viruses like Ebola, Bird Flu, etc.
  • We can measure the success of various public health measures with Machine Learning Algorithms. For example, researchers estimated the success of Arogyasri, a health welfare scheme by the Telangana government, India, for economically backward citizens. The research helped the government in reaching deserved people better and in allocating the required budget. This is now a common practice all over the world.


Innovations in Software technologies were never limited to Mobile Phones and Smart TVs. Software Products in essential sectors like healthcare have been in use for decades. However, such health care software systems need to be updated regularly to closely follow changes in health profiles and situations like the pandemic. Moreover, big data analytics have wide applications in medical research, diagnosis, evaluating and predicting changes, and in research related to finding cures to diseases like AIDs, COVID, and Cancer. An AI system can be tuned to apply in diverse sectors. This calls for close collaborations between enterprises working in healthcare sectors and software experts, as well as policymakers in ensuring the data the systems use are as accurate as possible.

As I write this blog, the pandemic is slowly subsiding here in India. I’m thanking the software products and systems in logistics, health care, management systems that helped us wade through this wave. It was a very stressful phase, but it was also a phase of speed learning. These learnings will shape our software health systems and make them more accurate in the future.

 For more information on secure Data Analysis systems and futuristic software products for the healthcare sector, please write to us at

Divya Prathima
The author was a java Developer at coMakeIT before turning into a stay-at-home-mom. She slowed down to make art, tell stories, read books on fiction, philosophy, science, art-history, write about science, parenting, and observe technology trends. She loves to write and aspires to write simple and understandable articles someday like Yuval Noah Harari. We are very happy to have her back at coMakeIT and contribute to our relevant and thought provoking content.

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