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Is Home the Next Prize for Insurers?

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3 minutes, 15 seconds read

As work from home is proving to be efficient and productive, people are beginning to make their houses more comfortable to make it conducive to good work results. 69% of Indian employees believe that their productivity has improved while working from home. The creation of an organized space and additional expenses on equipment for professional needs are some of the requirements that Indian employees might need to do. As people are slowly adjusting to the ‘better normal’, it is paving a way for a connected living. Even though connected living was in its nascent stage before the pandemic, people will witness its necessity now. As most homes would transform, people are most likely to get home insurance now, therefore homes can be the next prize for insurers. 

Existing gaps between home insurance and customers

Home insurance penetration is just about 1% in India and barely 3% of houses are insured. Despite going through financial tension of repairing and reinstalling certain contents of the house, people are unwilling to buy home insurance. Houses older than 30 years are not insured and coverage for loss of Gold deems unsatisfactory among the customers. These are the most commonly cited reasons for people being hesitant to buy home insurance. Apart from this, one of the common misconceptions is the lengthy claim settlements. As people are gradually adopting more digital-enabled services in the ‘better normal’, home insurance is likely to witness a fundamental shift.

Home Insurance is the next prize for insurers

With remote working, newer risks are likely to prop up. For instance, while using the Zoom platform, a lot of people suffered security issues. Cyber risk and cybercrime coverages are not usually included by most standard home insurance companies but are slowly becoming popular. For instance, State Farm is the only major home insurance company that offers personal cyber insurance in addition to a standard homeowner insurance policy. 

Insurers are recognizing the significance of smart-home services that can help them enhance their offerings and personalize the customer experience. Installation of smart home devices would lead the insurers to become watchdogs of the contents of the house. Connected security systems and smart-home devices also mean low premium, thus allowing insurers to change the value proposition.

The world of connected living will also bring the opportunity of partnerships. For example, AXA partnered with connected device manufacturers to enhance its offering. It has developed a mobile application “MY AXA” with which it can control the smart-home devices. MyFox, Kiwatch, Philips Hue, Orange My Plug are some of the manufactures with which AXA has partnered. Owing to this, customers can get policies at a lower premium. 

Work from home has made people realize the necessity of a conducive environment to work smoothly. A comfortable space and installation of technologies and equipment at home for professional demands are being recognized by people. Owing to this, home insurers can expect calls from their customers who might want to know the coverage of assets. Few contents can also require extra coverage such as electronics, depending on the level of usage. For instance, Lemonade’s contents insurance covers contents with extra coverage on assets such as bikes, jewellery, etc. If a customer wants extra coverage on their camera, they would be required to send pictures of the receipt and camera. In the case the receipt is misplaced, insurers can determine the replacement value based on the current value of the camera. 

Conclusion

People have seen a change in their lifestyle, and are buying products to make their houses comfortable to work in. Content insurance can ease lifestyle by providing extra coverage on valuable assets, and act as watchdogs for physical assets. As priorities are meant to change in the ‘Better Normal’, people are likely to consider home insurance. With the ‘Better Normal’ and modification in work culture, the insurance sector is also likely to transform its services to cater to customer needs.   

Further Readings:

  1. The State of AI chatbots in Insurance 2020 Report
  2. Mantra Labs joins the third annual Insurtech100 list
  3. Contactless Solutions in Insurance
  4. The CIO guide to keeping operations up during pandemics
  5. COVID-19 Lockdown Effects: A Paradigm Shift in Indian Edtech
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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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