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The Perfect Combination: Gen Z’s Emergence and the Dominance of Mobile Banking

GenZ, or the iGeneration or Post-Millennials, refers to the demographic cohort born between the mid-1990s and early 2010s. As digital natives, GenZ has grown up in a world dominated by technology and has unique expectations when it comes to mobile banking. 

In 2022, Business Insider estimated Gen Z’s spending power to be over $360 billion in disposable income, a sizeable amount that will only increase in the years ahead. 

This article aims to understand the attitudes, wants, and triggers that drive GenZ users and how they impact mobile banking platforms.

Brand Values

GenZ users hold high regard for social justice. They care about the world’s issues, be it environmental or social, and are willing to put their money where their hearts are. In a survey by Publicis Sapient, 67% of Gen Z consumers said they were interested in investing in sustainability organizations, and 35% were willing to invest in those organizations – even at the cost of lower returns.

Concerned about the ethical practices of brands, they are well-educated about the realities behind them and know how to access information quickly. If a brand advertises diversity but lacks diversity within its own ranks, for example, Gen Z is likely to notice and may choose to walk away from that brand. 

What does this mean for banks and the mobile apps? Well, banks offer services to a wide variety of people. They need to ensure that their messaging, policies, and practices are in line with the changing times. Be it focusing on vernacular languages to accommodate diverse cultures, voice assistance, low-data usage modes, or even ensuring their marketing banners and push notifications are sanitized with empathetic content.

User Experience

With the luxury of growing up in a technologically advanced world compared to the early years of Millenials or Boomers, GenZ is quick to understand and use new tech products. Exposed to content with high-quality visuals early on, they expect a user-friendly interface and seamlessly crafted digital journeys.

Mantra Labs recently proposed improving the mobile buyer journeys for a leading travel and hospitality firm, where we saw that modern designs supported with clarity and convenience helped boost user retention.

Hyper-personalization is another customer-centric trend that sees growing importance among young users. With the availability of customer data for targeted campaigns and product recommendations, banks need to focus on leveraging data insights to deliver more personalized offerings as and when the customer is most likely to need them. A more profound attempt to understand the customer is also expected to be appreciated by the users.

We recently helped SBI General Insurance build a first-of-its-kind personalization tool that functions as a primary risk advisor for users. Leveraging gamification, interactive mobile UI/UX designs, and advanced analytics.

Some of the pointers that banks should keep in mind while designing their mobile applications include –

  1. Creating a mobile-first approach with responsive design
  2. Biometric authentication (fingerprint or facial recognition) for quick sign-ins while maintaining security measures
  3. Ease of navigation and discovery to reduce search time
  4. Non-intrusive soft nudges and triggers to motivate user engagement

Features and Functionalities

A tech-first GenZ leverages multiple features in its mobile banking app daily. With several successful applications going the super app way, users expect mobile applications to offer various features and functionalities that help them manage their financial needs. 

Some 84% of Gen Zers and millennials surveyed by shopping website Klarna said the profusion of smartphone technology had helped them to manage their money effectively. And 63% also said that technology allows them to oversee all of their finances better. 

According to a study on Digital Banking Attitudes by Chase Bank in 2023, Gen Z users performed most of their non-banking tasks, such as goal tracking, budgeting, and checking credit scores on mobile devices. 

An interesting observation about the interest in money management was the time spent on finance news and educational videos that young users consumed during the pandemic. Global Wireless Solutions (GWS), a Dulles, Virginia-based network benchmarking and analysis firm, said all consumers increased their use of finance apps during the pandemic, but this was especially true of members of Gen Z, who doubled the time they spent checking their finances on their phones, spending 127% more time specifically looking at their investments than they did before the pandemic.

Mobile banking applications would benefit from having certain features in their applications to boost usage –

  1. Budgeting and expense tracking features
  2. Goal setting and savings tools
  3. Financial education resources and tips
  4. 24/7 customer support via chatbots

Conclusion

Understanding the wants and needs of GenZ when it comes to mobile banking is crucial for banks and financial institutions to stay relevant and attract this tech-savvy generation. From aligning brand values with what the demographic group resonates with to providing user experiences in line with the best-in-class applications and ensuring the appropriate features and functionalities are present and efficiently used within the application, we can help banks meet the expectations of GenZ and build long-lasting relationships with this demographic.

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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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