Typical for India, the edges of the canal were littered with empty plastic bags, soda cans, and dark orange pesticide containers that swirled together in a corner where the water met a bridge.
But alongside the discarded trash floated something else: a bloated body. It was the third body that had been sighted that day, locals told us.
I've encountered many devastating scenes like this, since I first started visiting farming families across the state of Punjab almost a decade ago. I go as part of a medical exchange program where American physicians learn how biopsychosocial factors -- such as government policies, the environment or economics -- impact health globally. Our group also partners with the local Baba Nanak Education Society, to gain a grassroots perspective.
In some cases, locals retrieve these bodies and return them to their families. Often, they are not recognizable and left to decompose in the river. By village consensus, the cause of death in this case was suicide. Though I'm not sure the body was ever identified.
During these visits, we speak to families affected by the epidemic of farmer suicides. Those left behind -- parents, wives and children -- share their immense grief in the wake of a death. They describe debt passed from one deceased son to another, resulting in multiple suicides in one family.
The families tell us that children, particularly girls, are pulled out of school because they can no longer afford the cost of education. Young girls show us their carefully guarded treasure of dowry goods, woven with their own hands, to decrease the ultimate financial burden on their families.Take a data-first approach. Once we realize the importance of these data management products, we should redesign them with a data-first approach. Although they're data products, they shouldn't be built with only a workflow-centric approach in mind, in which effort is purely put into making a better user interface with some element of machine learning. The data that currently goes into and comes out of these systems isn't typically used effectively because the systems are often built on a weak data backbone. We should reinvent these products by ensuring that the data capture, storage, processing and distribution happen in the most effective manner with impeccable data lineage and easy data governance. As an example, data reconciliation is one of the most important processes in any organization. The quality of data that goes into reconciliation platforms is usually top-notch and what comes out is fully reconciled data: the single source of truth. Do we harvest it? Do we use this source for regulatory, client and financial reporting? Often, the answer is "no."
4. Consider distributed ledger technology. Distributed ledger technology (DLT; i.e., blockchain) can process, store and distribute data with effective traceability and immutability. We can use DLT to build an enterprise data backbone to manage all the data life cycle events. DLT can help make data lineage, data ownership and data governance easier and facilitate building a federated and collaborative model of data management. It can also allow multiple data sources to contribute to data management, instead of relying only on a centralized data management platform.
We've spent enough time and energy building new data infrastructure, big data stores for aggregating data and moving the often costly infrastructure to the cloud waiting for the magic to happen. It's time to take a step back and look at the basics of our data management products—to reimagine, rethink and reinvent them so the data in motion can be effectively harvested. Rather than sending data from multiple platforms in the business ecosystem to the data lake and keeping our
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