Today, the insurance industry is at a digital transformative phase to enhance the business models. There are few key areas we can expect insurers to embrace as they seek to create more automated, user-friendly processes in Insurance sector.

Use of automations and artificial intelligence

Insurance industry is shifting towards exploring automation of more complex and risky processes rather using of traditional method, which is less effective in case of time and accuracy. Using of emerging technologies like Artificial Intelligence and Machine learning provide the scope of intelligent automation for analysis of huge amount of data generated by IoT and smart wearables devices. These Analysis and cross checking of data help understanding the better customer insights, fraud detections, claims verification and processing.

With the more refined automated technologies and capability of analysing more data, insurance companies like AIG started employing smart drone for automated property assessment and claims processing, which not only helps in accurate assessment but reduces the operational cost also.

Redefining of Insurance distributions

For better user experience, insurers have already generalized the new channel of distribution such as online research, comparison platforms and chatbot for better interaction and understanding, which already impacted in the market of personal insurances. The new direct distribution channels and online comparison platform for direct small insurances are likely to be more effective in coming days.

Companies like Allstate is already allowing small business owner to buy policies in just five minutes, or P2P platform like Gather giving the opportunity to small business owner to self insure and coverage is offered through a captive which is owned by the businesses it insures.Thus offering greater transparency and reducing cost in policies for these type of enterprise.

Insurance through value chain disaggregation

As the market is growing, the specialization in sectors is becoming more popular. As insurers move into advanced and extreme digital stages there is more use of data, automation, connectivity, ecosystem integration, new development methodologies, and a smarter use of IT resources. Some of these companies are providing customer interface with a unique value propositions, some companies provides tools for specialized software solutions for the insurers.

Companies like PolicyBazar provides insurance comparison and gives customized suggestions and recommendations based on the customer needs and choices, using their artificial intelligence.

Data analytics to improve profitability and better customer experience

The exponentially greater data availability and better analytical capability of softwares provide the base of making decision. Cross checking and analysing on the large amount of data coming from various unstructured resources such as social media real time data through various connected devices, helps in better risk management to drive greater profitability as well as better customer experience. Applying a combination of techniques such as predictive modeling, text mining, databases searches and exception reporting, insures are able to understand better customer insight, fraud analytics which help them in making insight driven strategies and risk mitigation strategies.

Sensors, Detectors, and Telematics for building data

IoT or internet of things refers to the physical objects that are embedded with sensors, which gather information about specific objects and transmit it. These transmitted data are then analyzed as discussed earlier.

In insurances, using of IoT technologies is becoming more popular. In case of home insurances, smart homes is one of the fastest growing segment. Insurances companies are giving more discount on policies for an internet connected Home/Smart home.

Various wearable devices are also in demand as it enables life and health insurers to better engage with customers while obtaining real time insight into risk. Aditya Birla Health Insurance is offering their policyholders health benefits and rewards for connecting their approved apps and wearable devices to their health app so they can track one’s activity.

Property and casualty insurance companies like AIG , are going to use smart drone for better property assessment.

Blockchain Technology for fraud detection

In coming days Distributed Ledger Technology(DLT) or Blockchain Technology is going to be leveraged across all sector including Insurance for its revolutionary way of sending, receiving and storing information in a secure and decentralized way. Using of Blockchain technology in insurance will improve the quality of service, increase in the volume of data from new data sources, automate claims, also will reduce the operational costs. It has the potential to ease out fraud detection and risk prevention as per a report from EY.

Once insurance and blockchain technology are interconnected, key business process like policy management and claims management are likely to transformed and new business model are expected to emerge using Blockchain.

Augmented Reality/Virtual Reality in Insurance

Though Augmented Reality is leveraged by many other sectors, like in social media or in gaming and other sectors, insurance sector still is limited to areas like marketing or training by simplifying complex explanations, meant for customers and employees. How about a 3D modeling and simulations help customers in making insurance claims easier and faster? Or how about before you go for the home insurance a simulation helps you pinpoint all the areas under insurance rather than reading the lengthy document?

There are big challenges ahead for insurers. With more changing technologies, executives will need to carefully consider the opportunities.

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

By : Sampada

Published on Dec 19, 2024

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:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, 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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