Data is everywhere—you’re surrounded by it, even reading it right now. But data isn’t the same for everyone. For you, it might be a few numbers or files. For businesses, it’s mountains of information coming from every direction—social media, smart devices, online shopping, and more. Retailers used to manage with small, neat datasets and trusty spreadsheets. That worked—until it didn’t. As data grew wild and messy, those old methods couldn’t keep up.

 

That’s when Big Data came by — a way to make sense of all the messy information — helping retailers see patterns, personalize deals, and make smarter choices without guessing.

 

 

Today, data comes in all shapes and sizes—structured formats like spreadsheets, and unstructured ones like social media posts, videos, and sensor readings. Collecting this data is useful, but it’s only the first step. To make it work, you need to process this overwhelming flood of information to uncover insights that guide better decisions and help prioritize efforts. To learn how businesses manage this, we spoke with two experts: Mariana Dicusari, Business System Analyst, and Iulian Ciobanu, Integrated Solutions Advisor.

 

Big or Small. It’s Data that counts, isn't it?

 

Well, let us agree to disagree. Don’t jump to conclusions just yet. To really get it, you need to see the big picture. Innovation doesn’t just show up to look cool—it’s here to solve problems. The downside? Some people use the hype of “new” to sell things based on emotion and confusion. That’s why it’s important to actually understand what data means and how it’s shaping the world around us.

 

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Iulian Ciobanu | Integrated Solution Advisor | EBS Integrator

 

To understand Big Data, you first need to know the difference between regular data and Big Data.

 

 

Regular data is simple—it’s the sales report your team reviews every Monday or the spreadsheet tracking your monthly budget. It’s small, structured, and easy to manage with basic tools. Big Data is a different beast. It’s massive, complex, and comes from everywhere—social media, online transactions, IoT devices, videos, and more.

 

Now imagine a grocery store. Regular data is the daily sales log, showing how many apples and bananas sold. Big Data includes everything from the time customers shop, their preferred payment methods, social media posts about the store, and even weather patterns influencing sales.

 

Big Data is often explained using its 3 V’s—Volume, Velocity, and Variety. These concepts capture what makes Big Data so powerful and challenging to manage. Let’s break them down:

 

• Volume: Think of social media platforms like Facebook, which processes over 500 terabytes of data every day. This includes everything from posts, photos, and videos to user interactions and ad clicks. In retail, it might mean millions of transactions, customer profiles, and website interactions. The sheer scale of this data is what gives it potential—but also what makes it tricky to handle.

 

• Velocity: It’s not just about how much data exists but how fast it’s created and processed. Big Data operates at lightning speed. For example, Netflix analyzes your viewing habits in real time to suggest shows, and Amazon adjusts prices dynamically based on demand and competitor pricing. In retail, velocity means being able to process customer behavior on the spot—like recommending a product before they leave your website.

 

• Variety: Data comes in all shapes and formats, making it versatile but also complex. Some of it is structured (like spreadsheets and databases), while much of it is unstructured (like social media posts, images, or videos). A retailer might need to combine receipts, product reviews, and Instagram photos to get a complete view of customer preferences. The diversity of data is what makes Big Data insights richer—but it also requires advanced tools to make sense of it all.

 

The 3 V’s highlight why Big Data is different from regular data. It’s not just about size—it’s about speed, diversity, and the ability to turn complexity into clarity.

 

Where does Big Data come from?

 

Now that we understand what makes Big Data unique, let’s talk about its origins.

 

Big Data isn’t some abstract concept—it comes from the everyday actions of businesses and consumers. In retail primarily originates from three key sources, each enhancing its volume, velocity, and variety: transactional data, social data, and third-party integrations.

 

Firstly Transactional Data is generated with every purchase, capturing structured information such as buyer IDs, timestamps, payment methods, and product details. This data tells a story about customer behavior and market trends.

 

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Mariana Dicusar | Business System Analysts & Data Scientist | EBS Integrator

 

Every transaction is a piece of the puzzle. By analyzing this data, retailers can forecast demand and optimize inventory, ensuring the right products are available at the right time.

 

This analysis helps reveal demand patterns, uncover spending habits, and identify regional trends, enabling targeted marketing and informed stocking decisions. For instance, tracking increased umbrella sales during unexpected rainfalls allows retailers to adjust their inventory proactively.

 

Secondly, Social Data comes from unstructured sources like Instagram, Facebook, and TikTok, where customers express opinions through likes, shares, comments, and hashtags. This data provides invaluable insights into consumer sentiment and emerging trends.

 

Social media platforms are a rich gateway into real-time consumer thoughts and preferences. By knowing this unstructured data, retailers can detect shifts in consumer behavior, anticipate emerging trends, and engage more personally with their audience.

 

A 2022 PwC Global Consumer Insights Survey found that around 50% of European consumers discover new products via social media at least monthly, a trend amplified by features like in-app checkouts, product tags, and live shopping events. Through this analysis, retailers can identify what resonates with customers, track changing interests, measure influencer impact, and refine their strategies to stay ahead in an evolving digital landscape.

 

Thirdly, third-party integrations involve External Platforms and Data Vendors offering insights beyond a retailer’s internal data. This includes information on competitor pricing, market trends, and customer demographics.

 

Relying solely on internal data risks creating a narrow view. Integrating third-party data helps retailers understand market dynamics, allowing them to keep up with changing industries and adapt their strategies accordingly.

 

Incorporating this data enables retailers to perform competitor analysis, anticipate emerging consumer demands, and enhance customer profiles for more targeted marketing efforts. For instance, discovering a growing interest in sustainable products allows retailers to adjust their inventory and marketing campaigns accordingly.

 

In essence, Big Data transforms everyday information into actionable insights, driving innovation and giving retailers a competitive edge in today’s rapidly evolving market.

 

So... How Retailers use Big Data in practice?

 

So, now that we know where Big Data comes from, next question that appears is how retailers actually use it? They’re not collecting all that information just for fun—it’s their way of making shopping easier, smarter, and more personal. Let’s take a closer look at how it all works in your everyday life.

 

Take Zalando, one of Europe’s biggest online fashion stores. They don’t just recommend random clothes—they look at what you’ve browsed, what you’ve bought, and even your size to show you items that fit your style perfectly. It’s like having your own fashion assistant.

 

Let’s talk about pricing. Ever noticed how prices on eMAG change throughout the day? That’s Big Data in action. By analyzing demand, stock levels, and competitor pricing in real time, eMAG adjusts its prices to stay competitive while giving you the best deals. In Romania’s booming e-commerce market—worth about EUR 7 billion locally and another EUR 2.5 billion from cross-border sales—this flexibility is key. It ensures eMAG can meet customer expectations while keeping up with a fast-paced and highly competitive industry.

 

When we partnered with Lensa, a prominent Romanian optical retailer, we developed their mobile app from the ground up as a key component of their omnichannel strategy. This integration bridged their online platform with physical stores, enabling customers to transition effortlessly between channels.

 

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Lensa app uses data from how customers shop online and in-store to suggest products they’ll love, simplify checkout, and match promotions across all platforms—making shopping easier and boosting sales and satisfaction.

 

By analyzing app usage, in-store visits, and purchase histories, we identified patterns such as customers browsing online before purchasing in-store. Leveraging these insights, we personalized product recommendations, streamlined the app’s checkout process, and synchronized promotions across all platforms. These enhancements led to increased sales and elevated customer satisfaction, underscoring the impact of cohesive, data-driven retail experiences.

 

In retail, Big Data is like the backstage crew making everything run smoothly. It connects your preferences, shopping habits, and even the weather to create an experience that feels effortless, personal, and just right.

 

Our Experience with Big Data in Retail

 

Retail is ironic at its core—it’s all about selling, yet it’s not really about selling. It’s less about the push and more about the pull: understanding what customers need and offering it in a way that feels effortless. In this sense, retail is more about service than sales, where Big Data steps in as the ultimate matchmaker. By turning raw information into insights, we’ve helped businesses decode behavior, optimize experiences, and make smarter decisions that sell without really “selling.”
 

For instance a challenge we faced was adapting to African markets without direct presence in the region. We needed precision and Big Data provided the clarity we needed. Insights from Tanzania revealed that mobile devices accounted for over half of web traffic, emphasizing a strong preference for mobile platforms. Additionally, data from user interactions on Kupatana’s legacy platforms further highlighted the demand for mobile-first solutions. These combined insights formed the foundation of our strategy, ensuring a customer-centric approach tailored to the region’s unique needs.

 

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Kupatana app features a clean, mobile-first interface designed for seamless navigation, improved speed, and offline functionality to accommodate varying connectivity levels.

 

Using app analytics and segmentation tools, we identified high-engagement features and areas where users dropped off. This led to a complete redesign of their app, prioritizing speed, usability, and offline capabilities which led to higher engagement and more conversions.

 

Sometimes, the challenge in retail isn’t just collecting data but making sense of it—especially when it’s messy or unstructured. For Global Database, a B2B data provider, the challenge was to clean and organize data from multiple sources.

 

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Global Database dashboard showcasing organized insights for audience segmentation.

 

Using natural language processing (NLP), we standardized details like locations and company profiles, creating a centralized data lake. This allowed for precise audience segmentation, better pricing strategies, and uncovering opportunities for new offerings. Retailers face similar hurdles when combining data from social media, transactions, and external sources, but structured insights make the difference in tailoring campaigns or optimizing inventory.

 

The process behind these successes is the same: collect, clean, analyze, and act. Whether it’s structuring messy data, monitoring customer sentiment, or personalizing offers, Big Data turns complexity into clarity. For retailers, this means actionable insights that drive smarter decisions, better customer experiences, and, ultimately, growth.

 

Conclusion

 

Big Data is essential for both retail and eCommerce success. Every transaction and customer interaction—whether online or in-store—creates valuable data. By combining this with third-party insights, businesses can uncover smarter ways to connect with their customers and grow.

Best exemplified by changing post-pandemic consumer behaviour — innovation is more important than ever in selling. And thanks to big data, we can help you make your decisions accurate, predict trends and create personalized marketing strategies that work.