The idea behind the implementation of technology in the Insurance sector is to make the Insurance processes much more efficient, comfortable and provide the customers with a simplified interface. In recent years when talks about Insurtech was ripe then it was all about blockchain, IoT, wearables, innovations labs and AI. But, as the things started to roll out, it doesn’t seem to be an easy road with expected results will not be visible anytime soon. The digitalization of the Insurance industry has begun with a boom but the challenges surrounding this whole new era are unlimited, and Insurers need to strike a balance between expectation and the practicalities.

The challenges of the Insurtech industry and Insurance as a service:

1. Data and more data

It is a matter of the fact that the available data for the insurers is unlimited which help them to underwrite policies, detect fraud, price the products that were otherwise not possible traditionally. Insurers are constantly gathering, incorporating data received from automobile sensors, home sensors, Amazon web services, social media channels into their business models. It is a great way to be efficient enough and provide relevant content to the insurants.

Reality: There is a widening gap between the available data and the ability of the insurers to process this data contextually and derive insights into it. The data is something that keeps changing continuously, and it needs to be processed and used quickly for the expected results. But, the truth is that insurers do not have any actionable information around this data as they lack the proper infrastructure for fast processing the datasets.

2. Automated customer service and the chatbots

The impact of AI and machine learning on InsurTech is profound, and it is most visible in the customer service department. The automated chatbots are programmed to provide an instant solution to customer queries without any delays.

Reality: Even though chatbots are being adopted by big insurance companies, but accuracy is still an issue. The more complex the chatbot is, the more problematic it becomes. No matter how intelligent a chatbot is, it can never replace a human. Insurers need to ensure that their bots offer a high level of data protection and are compliant with regulatory measures. There are still customers who want to talk to the customer representative, not an automated agent. So, chatbot can never replace the human representatives it can just be another option of communication.

3. AI and cognitive automation

Data analytics and AI are a boon for the insurance industry. The power of AI backed systems help insurers to accurately price risk, manage claims value and do a lot more than only providing insurance. For example, in health insurance, the insurance product is more like a health assistant and for auto insurance using car sensors for usage-based policies. All this sounds like an insurance-perfect technology which is ready to revolutionize the insurance industry.

Reality: The technical hurdles sprout at every stage of AI implementation. AI helps insurers, but it may prohibit them to consider some factors or introduce some new precise elements. The immense intrusion of AI into the systems poses a roadblock that is the more sophisticated and accurate AI becomes the capability of humans to interpret and understand it keeps growing bleak. It is a challenge for the state actuaries and the rate reviewers who are responsible for evaluating the vast number of risk-classifications and seeing how it influences other in the process. Rate determination for tomorrow requires a perfect balance between the insurers and the AI-driven risk pricing tools.

From the above, it can be concluded that the insurance industry is rapidly evolving introducing a new wave of innovation. But, the challenges are still persistent and to be successful insurance companies need to employ quality people with competent management and supporting technical infrastructure.

Silent Drains: How Poor Data Observability Costs Enterprises Millions

By : Sampada S M

Published on Jan 30, 2025

Updated on Feb 4, 2025

Data Observability Data Pipeline

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success.

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

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