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Building Digital Dexterity in Insurance

6 minutes, 18 seconds read

The years 2018-19 are banner years for insurers. Economic growth and higher investment income played an important role in setting the stage for customer-centric development in the insurance sector. 

But, are insurers dexterous enough in deploying technologies to enhance customer experience & improve operations? 

According to Gartner – “Digital Dexterity is the ability and desire to exploit existing and emerging technologies for better business outcomes.” Here’s an outlook on building digital dexterity in insurance workplaces.

Why Insurers Need Digital Dexterity?

Accenture reports – 42% of the digitally active customers & 22% of the quality seeking customers are willing to use computer-generated advice for buying insurance. Reason – it’s fast and convenient. 

Surprisingly, less than 40% of insurers have a holistic digital transformation strategy, World InsurTech Report 2019 states. Furthermore, the gap between Insurer awareness of the need for ‘change’ and their digital maturity is significant.

WITR-X-Capegemini-2019-report-on-digital-maturity

Source: Capgemini Financial Services Analysis, 2019; WITR 2019 Executive Interviews, 2019.

The point is – the world is shifting towards the digital model. The sooner Insurers harness AI-based technologies to streamline their operations, the higher their chances of acquiring new-age tech-savvy customers.

How to Achieve Digital Dexterity in Insurance?

Betting on technology and expecting a serious outcome is not the solution, especially in the insurance industry which is highly customer-facing and experience-oriented. There needs to be a thorough strategy to eliminate resource wastage and increase back and front-office operational efficiency. 

For instance, Amazon uses its data capabilities for the benefit of consumers. By harnessing Machine Learning technology, the company provides personalized product recommendations to consumers. Through well-informed customer service interactions, Amazon contributes to employee satisfaction and productivity. 

Being dexterous can help Insurers achieve a mean & lean business model.

#1 Automation

The traditional process requires poring over lengthy documents, handwritten notes, and more to keep up with the ever-changing regulations. Also, most insurance customer-facing processes, such as policy renewal, go through several stages.

Eliminating dependencies and automating routine processes can help the insurers scale without adding staff. For example, AIA HongKong has reduced the average claims handling time by 40%.

Read our Case Study: How AIA HongKong saves 60% through Claims Automation.

Automation in insurance can solve some of the most pressing operational challenges like agent onboarding, claims settlements, underwriting, policy distribution, and document processing along with data entry and migration.

#2 Enterprise Mobility

With the growing number of smart devices in the workplace, determining how to integrate technology with work processes and business objectives can improve operations. Enterprise mobility involves several technologies like – 5G, blockchain, AI, cybersecurity, mobile device management, wearables, cloud, and IoT. 

According to Oxford Economics Maximizing Mobile Value Report, 80% of Executives believe — workers cannot work effectively without a mobile device

The ease of communication, resource accessibility, and affordability are the prime reasons for the wide-spread use of mobile devices. That’s why organizations are open to employees using their mobile phones for calling customers, accessing emails, file transfers, and much more. Executives agree that the real benefit of mobility lies in solving operational challenges.

Millennials spend at least two hours a day on their smartphones, with 78% of the time in apps. Reaching the customers where they are and in a way they prefer is indeed a great operational catch.

Also read – How AI can improve 5 front-office operations in insurance.

#3 Value Added Services (VAS)

Considering insurance as a commodity, customers cannot differentiate the products that are available at the same price and insurers who offer similar services. By definition, a value-added service can be any offering at little or no cost to promote the primary business. 

According to McKinsey, the estimated market for insurance VAS (especially in Europe & North America) is $2 billion. VAS holds enormous potential in the risk mitigation sector. Predictive analytics, knowledge sharing, risk training and reporting, self-insurance, and crisis advisory are some of the additional services that insurers can leverage utilizing data and analytics.

How Digital Dexterity Can Benefit Insurers?

While 90% of corporate leaders consider digitization as their top priority, 83% of them struggle to make meaningful progress on digital transformation, according to Gartner. Insurers can profit from digital dexterity in the following ways.

#1 Employee Productivity

With streamlined workflows and automating mundane tasks, insurers can improve their employee productivity to a great extent. McKinsey’s Building a Culture of Continuous Improvement in Insurance, states that “…Taking advantage of a new focus on problem-solving, the back office made a few changes to standard operating procedures that reduced the number of incomplete applications, speeding completion time by 45 percent for new customers..”

digital-dexterity-for-employee-productivity

#2 Enterprise Agility

Agility in insurance corresponds to instantly accommodating the multifaceted demands of customers & responding rapidly to opportunities and disruption. According to Accenture’s Transformation GPS study, Agile firms are twice as likely to achieve top-quartile financial performance

The agile align the entire organization to a set of lightweight, shared processes because of which, it can adapt quickly to market changes.

Also, the Gen Z are par work-life balance mindset and count on deliverables more than logging time. That’s why organizations are leveraging SaaS and cloud technology to create an agile workspace. 

#3 Cognitive Capability

Data is a greater by-product of Insurance. Processing huge amounts of Big Data can be both times consuming and complex to manage at large scale. By leveraging distinct technologies like NLP, Machine Learning (ML), and Automated Reasoning — Insurers can process huge volumes of complex data, affix intelligent insights to structured data and communicate these insights clearly to all relevant stakeholders.

With the cognitive cloud, meaningful data insights are always present irrespective of device and location. For example, IBM Watson Explorer is an ML tool for cognitive insurance with deep-analytical advice, exploration, and mining capabilities. It is still learning and maturing interactions, rules, and processing logic that can apply to policies. Currently, employees can assess claims 25% faster with the help of Watson.

What the ‘Digital-Future’ Holds for Insurers?

At this age, digital technology is a strategic priority for every insurer. Accenture envisions DARQ power, understanding customers, human+ workflow, security, and on-demand experiences to rule the insurance market. 

  • DARQ power: combining the capabilities of Distributed Ledger, AI, Extended Reality, & Quantum Computing.
  • Understanding the next generation of customers and delivering individual products
  • Human+: each worker is empowered with his skills + tech-driven capabilities.
  • Security in terms of user data and privacy in the entire insurance ecosystem.
  • On-demand experiences: customization and real-time delivery can bring a lot of competitive advantages.

We’re a new-age InsurTech, providing AI and NLP based solutions to improve digital dexterity. Feel free to drop us a line at hello@mantralabsglobal.com

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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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