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6G And The Future Customer Experience

3 minutes 24 seconds read

The world is rapidly approaching a future where the distinction between the real and virtual worlds is becoming increasingly blurred. We’re heading toward a more connected world that has meaningful interactions between humans and machines. And 6G will accelerate this experience for the customers and businesses.

When it comes to technological innovations, advanced countries like U.S.-China have always been one step ahead. While majority of the globe is yet to experience 5G, US and China are racing to develop and patent the 6G network. Chinese organization, Huawei Technologies Co. predicts that 6G technology will enter the market by 2030.

Image Source: Nokia

3G enabled human mobile connectivity, 4G enabled social interaction, and 5G enabled IoT devices to sense and communicate. What about 6G?

In one of the recent videos posted by Nokia, Peter Vetter, president of Bell Research Labs Core Research Labs said, “6G will enable holographic communication as if people would be able to teleport to each other’s environment and work on the same thing as if they’re in the same room.”

6G network is anticipated to offer cost savings, 10 times lower latency, 100 times higher connection density, geo-location accuracy, etc. It will have space-air-ground-sea integrated networks to meet the global coverage demand. Satellite communication, UAV communication, and maritime communication will significantly expand the coverage range of wireless communication networks.

Image Source: Science China Information Sciences

Impact of 6G on Future Customer Experience:

6G will be 100 times faster than 5G and will enable data transmission within seconds. Let’s look at the impacts of 6G on the future customer experience.

  1. Seamless Phygital experience: With the 6G technology, embedded sensors will allow digital representations to construct digital twins of people, actual objects, and their surroundings, converging the physical and digital world. 6G will also boost the metaverse and mixed reality experiences for the customers by offering the required peed and efficiency required.
  1. Faster Speed: 6G will enable us to stream videos faster and download 100 hours of video within seconds. This will surely have numerous use cases for businesses to increase their efficiency and improve customer experience. For example, this can be highly useful for the insurers in making the claims process faster and more accurate.
  1. Intelligent and connected Devices: Experts predict that 6G will be able to support 10 times the number of devices and will be 100 times more reliable. The technology will build a more connected ecosystem where all the devices will be in sync with each other. For example, maybe the future self driving cars maybe able to know where other vehicles are. Instead of tracking high traffic locations, vehicles might be able to communicate and coordinate with each other and save travel time for riders. All this would be possible if the network connectivity is strong and fast.
  1. Real-time experience: We’ve been talking about meetings and storefronts in the metaverse. The world is moving into a space where there is no boundary between the two realities. Maybe a customer would want to visit the store in the comfort of his home, converse with the salesperson face-to-face and have a real-time experience.  

Conclusion

Japan’s NTT DOCOMO has partnered with Nokia to start indoor 6G trials this fiscal year, and outdoor trials next year. 

While there is still time for commercialization of 6G, it’s usage might turn science fiction into reality. Real-time holograms, flying taxis, and the internet-connected human brains might be the future. To facilitate the next-gen of technology, we need a next-gen network that could define the future of communication.

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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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