Is There a Competitive Advantage for Companies Using Blockchain?

Published on May 9, 2018

Updated on Oct 16, 2019

Do you mostly buy from your ‘favourite’ local store? What if all the outlets of your favourite store knew your shopping habits and preferences? Provided your data is kept secure along the transaction chain, are the promise of hyper-personalization and ultra-high convenience worth the trade-off?

Thankfully, this isn’t hypothetical. Companies using blockchain or distributed ledger technology are able to track records easily on a global scale. Not only in retail, but almost every industry is applying blockchain to simplify its processes and offer personalized solutions to its customers.

In this article, we’ll discuss what makes blockchain a compelling technology and its continuing adoption across industries.

Why are Companies Using Blockchain?

Blockchains are encrypted, growing lists of records. It records every single transaction with a time-stamp. No one, including the owner, can modify the ledgers (or records) in a blockchain.

The following features make the blockchain technology a perfect fit for transactional record-keeping in different industries.

Distributed: Blockchain is a decentralized technology, i.e. there’s no authority looking after the framework and operations. The data is accessible to all participants in the network.
Immutable: One of the key advantages of blockchain over any other technology is unchangeability. Post-transaction, no one including the creator can modify the records.
Robust: Traditional communication channels involve many indirections. For example, a bank executes transactions in its centralized database. Then the bank sends the corresponding email/SMS to the user about the transaction. Blockchain is a decentralized technology i.e. users have direct access to the transaction settlements. Companies using blockchain are more robust towards internal and client services.
Encrypted: Encryption is core to the security in blockchain technology. It means only the authorized users and participants can access the information. It also secures the identity of the participants. Ciphertexts (encrypted data, which is meaningless to external users) protects the information from intruders.
Consensus: The consensus algorithms are core to the blockchain architecture. The consensus is a decision-making process for a group of active nodes (participants). Participants agree to the decision made by the algorithm.
Tracking: It is easier to track transactions in a blockchain. The technology records every transaction with a time-stamp thus preventing corruption.

The finance industry was an early adopter of blockchain technology. In fact, the credit for the popularity of this technology goes to ‘bitcoins’, which are completely digital financial transactions.

Blockchain Adoption Across Industries

Here’s an overview of how industries using blockchain are enhancing the operations.

Use of Blockchain in Supply Chain and Logistics

E-commerce is certainly giving a boost to the supply chain and logistics industry. But, are traditional record-keeping compatible with the growing demands? Because, today, to deliver a product from point A to point B might include multiple geographies and involve multiple entities, invoices, payments, and extend over months. However, tracking shipments and business transparency is one of the key challenges that the supply chain industry struggles with. Companies using blockchain in the supply chain domain can benefit in the following ways-

Payments and fund transfers are fast and simple for stakeholders at the international level.
It’s possible to keep a track record for the product from its source of origin to end-users. For example, Walmart uses blockchain to track pork it sources from China. It records where each piece of meat came from, processed, stored, its sell-by-date, and the buyer.
Since every participant can collaborate and share records, blockchain ensures transparency in information sharing.

Blockchain in Financial Services

Statista expects that the global blockchain technology market will reach $23.3 bn by 2023. It also suggests that the financial sector will cover more than 60% of investments in this technology.

Financial services can harness blockchain for robust cross-border payments and processing, P2P payments, micropayments, and currency exchange. Investors, day traders, and market makers can also deploy blockchain for clearing and settlement in almost real-time.

Blockchain in Travel

Travel is one of the fastest-growing aspects of the global economy. Both customers and travel & tourism service providers can harness blockchain applications. Customers need not hassle with forex and can access in-depth travel-related information of the destination.

Travel businesses can bring transparency in flight and hotel bookings. For instance, for flight and hotel for a customer, a travel agency needs to share information to the customer and different firms. Blockchain can reduce manual dependencies by sharing relevant information to different stakeholders instantly.

In the list of travel companies using blockchain, Winding Tree is a leading name. It is a decentralized travel ecosystem startup that connects travellers to service providers like airlines, hotels, and tour guides directly. By eliminating the third-party fees associated, it reduces travel overheads. Blockchain’s LIF tokens, Smart Contracts, and ERC827 protocol are at the core of Winding Tree’s travel technology.

Blockchain in Insurance

The insurance industry often struggles with double-booking, counterfeiting, and premium diversions through unlicensed brokers. Distributed ledger technology in insurance can help to minimize the instances of fraudulent activities.

Smart contracts, insurance claims automation, UAVs (unmanned aerial vehicles) for underwriting, and shared databases to simplify insurance can bring transparency in the insurance industry.

Blockchain Benefits in Healthcare

The traditional healthcare record-keeping is cumbersome and the surgeon might lose important remarks, allergies, etc. while going through manual files and folders. Blockchain can track one’s medical history since birth. Also, every minute detail of diagnosis would be available to the medical professionals, even if the patient loses the prescriptions and reports.

WHO reports that developing nations produce about 10%-30% of the counterfeit drugs. Moreover, the counterfeit drug market hit $200 billion worth in 2018. Blockchain can track the drug right from sourcing the raw materials to manufacturing and distribution, reducing the instances of this critical challenge of counterfeiting.

Concluding Remarks

The International Data Corporation (IDC) predicts- investment in blockchain solutions will reach $11.7 billion in 2022 from $552 million during 2018. The blockchain trends that different industries will witness include-

Blockchain as a Service (BaaS)
Favourable regulations around the world towards blockchain
Consumer-centric digital assets
Additional security layers
Use of blockchain technology for better user experiences (UX).

Building blockchain systems are transforming the transaction value chain across industries. Talk to our experts to learn how blockchain is shaping the future of digital enterprises. Drop us a word at hello@mantralabsglobal.com

Contributing Authors: Nidhi Agrawal (Content Writer @Mantra Labs)

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:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, 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.

How blockchain is disrupting businesses?

Why are Companies Using Blockchain?

Blockchain Adoption Across Industries