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5 CX trends that will define the Indian Banking Experience in 2023

3 minutes read

Banks initially began as trusted entities that would act as custodians of their customers’ wealth and channel them toward growth opportunities that would aid in growth and prosperity for all. Cut to 2023, that promise alone does not suffice. The modern account holder holds banks to a higher standard and has little to no tolerance for banks with poor customer experience (CX). 

Over the years, CX has gone from being a frill to an essential component that can have a colossal impact on the bottom line for banks. For 2023, incorporating these trends into workflows will help banks pull ahead of their competitors by a significant margin. 

Applied Analytics

Blake Morgan, a notable customer experience futurist noted that 2023 will be a year of reckoning for brands as rising inflation and thrifty spending will up the ante on brands to satisfy their customers. With a trough of data at their disposal, banks are uniquely positioned to apply all of these insights into architecting a bespoke CX that will signal their commitment to their account holders in these trying times.

Automated Onboarding

One of the greatest challenges that India currently faces is the huge gap between the banking experience in urban and rural areas. With close to 70% of the Indian population being concentrated in these areas, it is vital for banks to design a frictionless and delightful customer onboarding experience that will bring these people into the fold. Such a solution would decrease staffing costs in the long run and translate to more customers in urban areas in light of the fact that over a quarter of banking customers would rather not step into a branch at all.

Hyper-Personalization

An eternal feature that has been topping CX trend lists for years, hyper-personalization will continue to pay off in 2023 as few banks have even attempted to get this right. Boston Consulting Group’s (BCG)’s finds on personalization revealed that when done right, it can result in a 10% increase in annual revenue uplifts till the time competitors catch up. 

As open-banking models start to take off and Indian banks become comfortable with experimentation, hyper-personalized services could serve as a key differentiator for both incumbents and upstarts alike. ABN Amro, one of Europe’s leading banks partnered with Subaio, a Danish fintech firm to offer personalized loans. 

Going Phygital

Much has been written about India’s demographic dividend and the implications it has on global economic prospects. With the world’s youngest cohort of millennials and GenZ alike, Indian banks are at the cusp of a massive demographic change that could be the opportunity of a lifetime. By revamping the bank branches to work with smartphones, banks can ‘humanize’ their systems to provide a wholesome, truly immersive banking experience that gives account holders the best of both worlds. JP Morgan Chase’s decision to open a new digital banking unit in the U.K. is an example of combining the trust of an established bank with the conveniences offered by a neobank.

AI goes deeper

As chatbots start to become a staple in customer service workflows, the next step would be to integrate AI across all customer touchpoints to enable better self-service, quicker resolution, and lower servicing costs. For instance, ICICI bank’s virtual assistant is primed to analyze voice queries to deliver instant solutions, point to appropriate areas, or escalate to a support executive if the need arises.

A Year of Reckoning

2023 will be all about resilience, innovation, and focus as central banks all over the world begin to raise interest rates and tighten expenditures in a bid to curb inflation. For banks, the only way to grow through this crisis is to correct technological debt, digitize relentlessly, and build a razor-sharp focus on their customer experience.

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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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