Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(44)

Solar Industry(8)

User Experience(67)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(146)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(21)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

The Importance of Data Ethics in Insurance

4 minutes, 38 seconds read

In a world where digitization is rapidly making its way into our everyday life, challenges come as an add on package. Amongst many others, Data and Privacy are the most raised concerns. Be it any sector, consumers need assurance that their data is safe with the company. Insurance is one of the sectors that banks highly sensitive data of its customers. Data breaches, wrongful processing of customer data, using the personal information of customers without consent, etc. puts a dent in the company’s image. We have seen the scandal caused by the data breach at Facebook. 

In September 2018, Facebook announced that an attack on its computer network exposed the personal data of over 50 million users. According to Facebook, hackers were able to gain access to the system by exploiting a vulnerability in the code used for the ‘View as’ feature. The attackers stole the ‘access tokens’, which took over the user’s accounts and got access to other services. 

The need for data protection in Insurance

‘Trust’ is an essential part of the Insurance industry, failure of which can lead to loss of customer loyalty and subsequently loss of business. Insurance companies need to process customer data for calculating premiums, customized policies, claims, etc. 

In India, The Information Technology Act, 2000 (IT Act) and the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011 (SPDI Rules) set out the general framework for data protection. However, given the nature of the Insurance business and intermediaries, the Insurance Regulatory and Development Authority of India (IRDAI) has prescribed an additional framework for the protection of policyholder information and data, which Insurers need to follow in addition to the general framework under the IT Act. 

As India moves towards digitization, the IRDAI and IT Act are not enough to ensure proper compliance of data. The nation needs a comprehensive Data Protection law along with a governing body to oversee the implementation of the law. A draft of the Data Protection Bill was introduced in July 2018 which later was tabled on 11th December 2019 by the Indian Parliament. However, the Bill is being analyzed by a Joint Parliamentary Committee (JPC) in consultation with various groups. Indeed a groundbreaking step for our country, but it might have dangerous implications. The bill gives power to the government to access customers’ private data or government agency data on grounds of sovereignty or public order. 

The question is that will the government adhere to data ethics while processing this private data? The answer is unknown, but this step puts Insurance companies and TPAs under pressure to take steps towards data protection.

How can Insurers ensure data ethics

To ensure the privacy of customers and use data effectively, Insurers and intermediaries can adhere to the following measures-

Implementing risk management and IT security policies

Insurance is the most targeted industry by hackers. Also, with a lot of mobile workforce handling portable devices, monitoring data can be challenging. Companies need to protect data on the endpoint. The software should be installed on the systems directly and encrypting the data on portable devices such as USBs and hard drives. Growing risks in cybersecurity increased demand for Cyber Insurance policies. Cyber Insurance products are another such medium which helps in mitigating risks in the event of a cyber attack or a breach. 

According to a report by Data Security Council of India on Cyber Insurance in India, the Cyber Global Insurance market is prone to grow from a CAGR of 27% from 4.2 Bn to 22.8 Bn from 2017 to 2024. Insurers can also take measures such as setting-up internal policies and regular audits to keep a check on the data compliance. 

Consent mechanism for using policy holder’s data

A company might need data for internal purposes such as upgrading services for its customers. In such cases, companies should mention the purpose and set-up a proper mechanism for taking consent. Insurers can also give a status update on the project for which they used the customer data to keep the trust factor intact.

Using data-centric technologies

Human errors are unavoidable. But a second step validation can be set-up using disruptive technologies such as quantum computing, blockchain, Artificial Intelligence. These technologies not only ensure data security but also help in utilizing the customer data most efficiently.

[Related: 5 Proven Strategies to Break Through the Data Silos]

Ensuring transparency with customers.

In the event of a data breach, the company must inform the customers and take steps to contain the damage. In 2014, Anthem Healthcare was attacked which led to a data breach. They immediately sent out alerts to their customers informing of the possibility of their data leak. Subsequently, they also informed the media after 8 days. Furthermore, they contacted the FBI regarding the attack and hired Mandiant, a cybersecurity firm to assess the level of damage. As an essential part of data ethics, it is equally important to own the mistake and take appropriate measures.

[Related: AI in Insurance: Takeaways from AI for Data-driven Insurers Webinar]

Merits of the case: data ethics in Insurance

Data breaches can occur due to superficial monitoring of data flow; lack of accurate privacy design; poor internal audits; failure in conducting resistance tests; use of outdated security systems. 

The present crisis of COVID-19 has made data all the more vulnerable. As many employees are working from home, data security compliance has been an issue. Data protection bills and authority can act as watchdogs in the Insurance sector to avoid breaches. The Insurance sector should not see the law as a burden for additional compliance but rather an opportunity for long term customer trust. 

If you want to know more about the importance of data, and how to prevent data loss in other organizations that provide financial services, do read Financial services businesses must protect PII. DLP can help.

Cancel

Knowledge thats worth delivered in your inbox

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

By :

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot