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The Impact of Covid-19 on the Global Economy and Insurance

3 minutes, 35 seconds read

The pandemic COVID-19 or the well known “Coronavirus” is gradually stretching its limbs throughout the world. COVID-19 has now spread to more than 180 countries with its epicentre in China. Coronavirus confirmed cases reported globally, adds up to 8,60,181 (1st April 2020) and is still on the rise. With the death toll of 42,345(1st April 2020) the insurance companies have to take it on the chin. 

Public gatherings have been banned in several places. For instance, Mipim — the world’s largest property fair is postponed to the later part of the year. Similarly, the Mobile World Conference in Barcelona is cancelled altogether. From the IPL to the world’s premier basketball league to a 250-year-old parade and sprawling festivals, all national and international events are either cancelled or kept at hold indefinitely. Almost every business (likewise insurance) is impacted with corona outbreak and any business cannot rebound in a day.

Referring to the 2008 financial crisis when credit markets seized up, Mr Muri- Wood said, “The only thing we’ve ever had which was bigger than this was the banking crisis.” 

Businesses, Corona and Insurance

Many businesses have insurance policies that are meant to kick in when disaster strikes. But few of those policies are likely to cover pandemic outbreaks. Business interruption insurance, the coverage typically availed by the companies, as part of their property policies, pays cash to make up for lost revenue when a business has to halt operations unexpectedly.

Despite the fact that most policies won’t pay out if people cancel their travel due to coronavirus; in February, Post Office Insurance saw a year on year rise in sales of policies of 168% and CoverForYou saw a 150% increase.

Queries on new policies have sharply spiked up to 60% since fresh cases of Covid19 reports.

“Globally, we have seen such cases that impact large populations there is an increased push from consumers to get themselves covered. We have seen the same happen here as well in the wave of fresh cases being detected” 

Pankaj Verma, head marketing & underwriting operations, SBI General Insurance.

After the epidemics of SARS in 2003, Ebola in 2014 and Zika in 2015 — insurance companies realized that business-interruption claims could become unwise if they covered closures related to outbreaks of disease. Since then, insurers have taken steps to exclude epidemics from their policy.

Though epidemics are excluded from many business insurance policies, as recession threatens the global economy along with rising insolvencies, all sorts of companies, from airlines to retailers are coming under strain.

The insurers refused to comment, but Atradius said it is expected that corporate insolvencies will grow 2.4% globally in 2020, majorly resulting from the coronavirus outbreak.

The harsh reality

Perhaps, it’s too late to buy coverage for the current outbreak. Insurance companies do agree to take the brunt of the situation and pay the decontamination cost after the outbreak, but would tightly limit the amounts.

With unprecedented turmoil the industry created by the outbreak caused global airlines to cancel thousands of flights. Companies could choose a policy that would cover the deaths from an epidemic, when it passed a pre-estimated threshold, or when a government body — anywhere in the world — ordered a lockdown or travel ban. The policies are intended as custom contracts, so the company would choose according to their own risks.

Coface chief executive Xavier Durand mentioned that hotels and airlines will have to take the maximum brunt of the epidemic outbreak, while Euler Hermes saw coronavirus costing $320 billion of trade losses every quarter this year.

This indicates that companies will have to bear the losses themselves. It can be either directly or in the form of self-insurance funds (large companies often set aside some funds for emergencies).

LV, the insurance giant in the UK, have stopped selling travel insurance with immediate effect as a result of the coronavirus outbreak.

“We can’t insure a burning building,” Mr Ryan Christian Ryan of the risk advisory firm Marsh says.

The bottom line

The Coronavirus have adversely impacted the economy worldwide. From time to time, violent demonstrations slowed down the flood of travellers to a trickle and transactions grind to a halt. 

McKinsey anticipates recession until the end of Q2 because of large-scale quarantines, travel restrictions, and social-distancing leading to a sharp fall in consumer and business spending. However, because of banks’ strong capitalization and macroprudential supervision, a full-scale banking crisis is averted.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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