Artificial Intelligence [AI] is reshaping the e-commerce sector by eliminating many manual processes in the area of promotion, supply chain, and assortments. AI has been a helping hand by making decisions smarter, accurate and real time. The agile, data-driven technology advanced industry could get a boost with the adoption of AI into the system. Identifying patterns, sourcing data from different channels, facial recognition, shopping patterns, media trends and automation of mundane jobs would make e-commerce industry more dynamic, and customer focussed.

Industry expert Gartner predicts – by 2020, 85% of the customer relationship, would be managed without human interaction.

So what are key areas where AI has been hitting hard and providing positive outcomes, let’s explore :-

Offering customer focussed Solution

The critical requirement of any business is “serving the customer needs.” However, with digitization its hard to know them, study their body language or communicate with them. That’s where Artificial Intelligence [AI] comes into the picture. With the help of machine learning and natural language processing, it gets easy to read the movements of the customer, analyze and show them relevant items. One of the best examples is of Pinterest that offers a browser extension and shows similar images with image recognition software. What it helps is narrowing down the search for the user with ease and convenience.

Realigning Sales Strategy

Thanks to software industry we have best in class Sales and marketing Management system and
CRM, but studies have shown that almost 30% of leads are not followed up by the sales team. The system is not equipped to predict or read market sentiments and most importantly realign sales messages in real time. For all of this embedding natural language learning, predictive marketing and analyzing deeply onto the leads would help in realigning sales strategy, thereby empowering speedy resolutions and positive growth for the company.

Bots in touch always

Do you recollect Air India Maharaja Logo? How much warmth it had? Wouldn’t you like to offer your client base the same feeling? A similar experience when they enter your digital store they are greeted with a smile quickly navigated to the section of their choice and offered exactly what they are looking for – be it a product, query resolution, returns or offers. Thanks to chatbots – the computer program that could communicate with human users digitally. Integration of chatbot with social media or shopping cart could be beneficial for the e-commerce industry. A customer could get quick answers to the items added to the shopping cart, be it about warranty details or return policies. Also coupling it with social media like Facebook could help the customer in a situation like payment has been processed but a confirmation email/message was not seen. Chatbots are empowering the e-commerce business with a possibility of real-time interaction and enhancing the customer experience.

Expand Services with IoTs integration

For all the digital stores it’s like a dream come true when conditions like these turn into reality. The smart refrigerator issues a command that eggs are out of stock and need to be replenished places an order with a weekly grocery list to your favorite online grocery store, or the Bose speaker that automatically renews the subscription of amazon prime music. With seamless integration with smart devices, AI could help in building a strong and loyal customer base.

AI is reshaping the online retailers and e-commerce marketing. With brands like Walmart, Amazon and eBay have been at fore frontiers in leveraging the latest technology; the start-ups are now investing in enhancing the productivity of the firm. So experience the AI and take your e-commerce business to the next level.

Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

By : Sampada

Published on Dec 19, 2024

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.

Loading More Posts