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InsurTalks Podcast with Alex Jimenez: Now is the Time to Reevaluate Digital Customer Experiences

7 minutes read

The COVID-19 pandemic has brought upon an unprecedented change in our daily lives and routines. Consumer behavior is changing constantly. Lockdowns and social distancing have led to huge losses for businesses across industries. The world is heading towards an economic slowdown. Under these circumstances, organizations are facing many challenges to keep their businesses going. Insurers too are facing similar issues. Some insurance lines such as motor, travel, home have suffered a business loss due to low demand.

To understand the impact of this crisis, especially in the USA, we interviewed Mr. Alex Jiminez, Strategy Officer at Extractable from California, and learned more about creating better digital customer experiences in these testing times. 

Extractable is a strategic consulting, design, and data analytics agency focused on the future of financial services. His other recent experience includes leading technology strategic planning for the office of the CIO, at Zions Bancorporation, and managing Digital Banking and Payments Strategy and Innovation at Rockland Trust. Alex has been named to several industry influencer lists in the areas of FinTech, RegTech, Blockchain, InsurTech, Innovation, and Digital Marketing. He has been featured in the Irish Tech News and the Independent Community Bankers of America’s (ICBA) Independent Banker.

Connect with Mr. Alex Jimenez – LinkedIn

The excerpt from the interview:

The impact of COVID-19 pandemic in the financial services industry

What is the impact of COVID-19 pandemic in the financial services industry, and how is the industry responding to the ongoing crisis in the US?

In the wake of the current crisis, organizations are more focused on keeping the operation going, trying to set-up work stations for remote working, dealing with customers and working with them over digital platforms. But very few are focusing on the future which is preparing for the after-effects of this pandemic on the economy. 

In-person communication is still an important mode of interaction with customers in the US banking sector. But now the issue is how to provide good services to clients? Some of our customers are going to experience digital models for the first time. 

Organizations that have well-defined Digital Strategies and Customer-First approach will be able to provide good support to their customers. Organizations that are late into this space are more likely to face problems in the future.

[Related: The Impact of Covid-19 on the Global Economy and Insurance]

Changing customer preferences

How can companies reach out to their customers in this New Normal world?

We have already started to move towards a digital-centric world which is just going to accelerate. We will see businesses who have earlier ignored their digital capabilities will now build more on them. 

The first video call was invented in the 60s and was not so appreciated as everybody thought it was expensive and complicated. Today we have FaceTime, Zoom but adoption has not happened on a larger scale. But this will soon accelerate. Customers will be comfortable dialing into a video chat with their Insurance agent. 

I don’t believe there’ll be a New Normal. For example, in the US after 9/11 people thought that life will never get back to normal but except for rigorous security screening at the airports, there hasn’t been much change in the behavior. 

In Israel, amidst all the constant disturbance, people in Tel Aviv and Jerusalem are living normal lives. There’ll certainly be some specific changes post the pandemic such as more adoption of digital technologies, more focus on customer needs but I believe there won’t be an entirely new world with a drastic change in consumer behavior.  

The need for personalization

What are some Attention hacking lessons for Insurers operating in ‘the New Normal’?

We are moving towards the personalization of products in general. Generally in Life Insurance, we insure people based on their date of birth or medical history. But what if we insure people based on their behavior? If we did that, would people change their more risky behavior to get a better rate? A non-smoker can be given a better rate as opposed to a smoker. If we get down to individuality, saying that this is your individual (your own) rate; it makes a difference. 

There is a lot of data available and AI is needed to mine that data and derive analytics. Just by building a relationship with customers, we are not doing a great job with personalization. It’s important to apply a human touch to the communication which makes customers feel like you know them. Thus, retaining their attention.

Digital customer experience in Insurance

For the insurance industry, what steps can help in delivering the right digital customer experience in terms of UX and visual design?

A lot of organizations practice Design Thinking but Financial Services don’t. They are of the opinion that they know what is needed as they themselves are customers and they have data from the surveys. But that’s a wrong approach. Design Thinking is about empathy. It is important to get into the shoes of your clients to design better solutions.

To enhance digital customer experience, Insurers need a thorough understanding of users — who are the ultimate clients, their needs, what they expect from this experience, etc. After comprehending how they engage with technology and financial services, start venturing into the solution and test the solutions with actual users.

Innovations in the financial services industry

What technology-based innovations are being explored within the financial services industry? And, do you see AI playing a role in the short term? 

AI has already affected Financial Services in a positive way and will make it better. In insurance, IoT has been very impactful and will continue to be. Some applications have already been applied in reality like sensors in cars to detect speed and ensure that you are under the speed limit. This helps in getting reduced premiums. 

However, some basic processes are still done in the old school way of shuffling papers. Straight though-out processes have not yet happened. Now RPA is being applied to this but it is more like a band-aid. What is more important is how we can build processes through true automation with AI.

[Related: 5 Insurance Front Office Operations AI Can Improve]

Adoption of AI in Insurance

Speaking about more adoption of technologies, do you think there’ll be more investment in AI now?

Absolutely! We have already seen that investment in technologies like AI, cloud computing, quantum computing has been ramping up. Businesses will invest much more in AI than before. It might be for better decision making, underwriting, understanding the behavior of clients, etc. Also, from a marketing standpoint, financial services have never focused much before but will now invest in AI for this area too.

[Related: How is AI extending customer support during COVID-19 pandemic]

In your recent article in Extractable – “Deploying third-party financial service technology to mitigate crisis” you talk about what tech vendors are doing wrong. Please expand on how to encourage resources to be innovative change agents?

There were two points that I made in the article-

First is about what companies are doing incorrectly when it comes to innovation. Risk management is consulted only after developing the product. The product release is stalled until the legal compliances are adhered to. Instead, companies should involve the risk management at the beginning of the process (while defining the problem and solution). Involving risk management at every step of the innovation process will make it much easier to push out innovation.

The second was about vendor management. Many small vendors such as tech vendors, InsurTechs want to sell solutions to financial service companies but are often surprised by the tedious vendor management process. There’s a lot of documentation. Once the first process of selling is done, vendors should package the documentation in a way that when the next prospect asks for it, the due diligence package is ready to offer. 

Read article – Deploying third-party financial service technology to mitigate crisis 

Wrapping up

Alex shared interesting insights on how Design Thinking and Visual Design can create better digital customer experience. The design vertical at Mantra Labs too believes in the same and has designed UX for various applications for its customers. Here’s an article to understand the role of Customer Experience (CX) and User Experience (UX): Creating Amazing Digital Customer Experiences


AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us 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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