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Everything you need to know about Data Integrity in eCommerce

Updated: Apr 1

Data Integrity in eCommerce

eCommerce businesses need accurate and reliable information to thrive. This information, including everything from product details to customer preferences, forms the foundation for critical decisions that can make or break your online store. 

But what happens when this data becomes compromised? 

Every year, 25-30% of data becomes inaccurate. This seemingly small percentage can have a ripple effect, leading to costly mistakes and less effective sales and marketing campaigns. 

For example, your data might be showing wrong inventory levels, which can lead to overselling a popular product. Now, because you don’t have so much supply ready, it’ll result in frustrated customers and lost sales. 

Moreover, every brand looks at data differently. Take the eCommerce equation, for example, Revenue = Traffic x Conversion Rate x AOV. 

For some brands, revenue will be the total revenue generated by sales made, while for some, it might be revenue after eliminating operating costs. Now, imagine your data integrity is low, you put one metric wrong in the equation, and the revenue projection will change entirely. You might end up making decisions that are not needed. 

Maintaining data integrity is, therefore, necessary for eCommerce businesses to ensure customers are satisfied. It also helps ensure that your channel attribution is right and you create campaigns for the right people. 

In this blog, we’ll look into the challenges associated with data integrity and see what are the solutions to safeguard your valuable information. 

What is data integrity in eCommerce?

Data integrity is essentially the trustworthiness of the data that you get from your eCommerce store, marketplaces, or your marketing channels. It ensures that all your data is accurate, complete, and consistent across all your systems. 

If you have accurate customer purchase history data, it can help you run targeted email campaigns and personalized product recommendations. 

Similarly, having reliable data on campaign performance across different channels, like which keywords and creatives are performing better or worse, helps you strategize your marketing efforts more effectively. This allows you to focus your budget on what's working and optimize your approach for channels that need improvement. 

Data integrity should be the core of your eCommerce operations as it influences everything from customer experience to the effectiveness of your marketing campaigns.

Challenges faced by eCommerce businesses for preserving data integrity

Sure, maintaining data integrity is important. However, it’s not as easy for eCommerce businesses. The volume of data generated is quite high, and the same goes for the velocity and variety in which it comes. Essentially, three out of the 5Vs of big data make the task quite difficult. 

Let’s take a look at some of the challenges that eCommerce businesses deal with while maintaining data integrity: 

1. Multiple data sources 

eCommerce is a data ecosystem rather than a data source, where valuable information flows from multiple sources. Brands collect first-party data like order history and revenue from platforms like Shopify, Amazon, or Flipkart. They gather customer behavior data from website analytics and user platforms and even advertising data from Google, Facebook, and email marketing tools. 

However, this data often resides in isolated silos on different platforms. The challenge lies in consistently collecting, storing, and processing this data across diverse systems. This fragmentation can lead to inconsistencies and inaccuracies, jeopardizing the overall integrity of your information. 

2. Manual data pulls 

While data is essential, the process of retrieving it shouldn't become a burden. Manual data pulls in eCommerce, where information is extracted from various marketplaces and marketing channels individually, can be incredibly time-consuming and prone to errors. 

A mid-sized brand with limited resources has to check every platform, like Shopify or Facebook, multiple times a day. Downloading data periodically, analyzing each dataset in spreadsheets like Excel, and then manually connecting the dots to create reports — this is a recipe for disaster. 

This approach leaves little room for efficiency and opens the door for human error. A single copy-and-paste mistake in a spreadsheet, as famously witnessed by the $2 billion loss at JP Morgan Chase in 2012, can have devastating consequences on data integrity and, hence, on your business’s bottom line. 

3. Reports are uneven 

Data comes in many shapes and sizes in eCommerce. While some platforms offer structured reports in tables, others provide information in non-uniform formats like PDF reports. This lack of homogeneity makes data integration and analysis a complex task. 

Furthermore, the process of converting data from heterogeneous to homogeneous comes with the risk of errors. It also costs a lot of time. This time-consuming process often results in stale data, obstructing your access to real-time insights. Having to make crucial business decisions based on outdated information can lead to catastrophic consequences like wrong targeting and loss of revenue. 

4. Poor procedures 

The saying "garbage in, garbage out" perfectly captures the challenge of poor data collection and preparation. Even the most sophisticated algorithms are susceptible to compromised data. 

When processes are primarily manual, the onus is on business owners to identify the correct data sources for specific metrics. For example, order data should be retrieved directly from your store platform, not from an external marketing tool. 

Furthermore, data is inherently dynamic. Different ad platforms, for example, use different attribution windows, ranging from seven to thirty days, impacting how marketing performance is measured. 

Failing to account for these nuances during data collection and preparation can lead to inaccurate insights and skewed conclusions. These poor procedures set the perfect stage for inaccurate data to compromise integrity and give you false insights. 

5. Deriving actionable 

Getting actionable insights from data requires more than just numbers. However, individual interpretation can introduce bias and hinder the integrity of data. 

When crucial decisions are completely based on subjective interpretations, a disconnect arises from the lack of comparison with established industry benchmarks. This lack of objectivity can lead to skewed conclusions and hinder your ability to make data-driven decisions. 

How to maintain data integrity for accurate and reliable insights? 

Ensuring data integrity is crucial for eCommerce businesses that want to make informed decisions based on reliable insights. 

However, maintaining integrity comes with unique challenges, as we discussed, particularly when dealing with the vast amount of data generated from diverse sources. 

Here are two key approaches to maintaining data integrity: 

1. Building in-house data teams 

For larger eCommerce brands with the resources, building their own data management system can offer a high degree of control and customization. However, this path requires a significant investment of time, money, and expertise. 

The process typically involves the following crucial steps: 

1. Data extraction: Extracting data from various sources like e-commerce platforms, marketing channels, and customer databases. The team has to establish secure connections and write scripts to pull the data efficiently. 

2. Data preprocessing: Once extracted, the data needs to be cleaned and prepared for analysis. This includes tasks like removing duplicates, correcting errors, and standardizing formats to ensure data consistency and usability. 

3. Data analysis: After preprocessing, data scientists and analysts use various tools and techniques to analyze the prepared data. They ask specific questions, run statistical models, and identify patterns and trends to extract valuable insights. 

4. Data visualization and reporting: Finally, the extracted insights are transformed into visually appealing and easily digestible reports. 

Building a robust in-house data management system requires substantial investment in assembling a dedicated team of specialists with different skill sets. They are people like data engineers with expertise in cloud infrastructure, scaling, security, and data governance, data scientists and analysts for data preparation, model building, and analysis, and an execution team to translate insights into actionable strategies. 

Then investments are also required in robust cloud infrastructure, data management tools, and analytics platforms to support the efficient processing, storage, and analysis of large data volumes. 

And while a well-built in-house system offers advantages, it also comes with its own set of challenges. Like code-level errors or operational problems that can disrupt system functionality. Scaling infrastructure and resources to handle surges in data volume, such as during peak sales periods, can be complex and costly to manage. 

2. Invest in AI-based data solutions for eCommerce 

While building in-house data teams offers a path to data management, it comes with significant time and resource commitments. 

AI-based data solutions like Graas, on the other hand, are a good alternative, addressing the limitations of manual processes and offering more advantages, particularly in maintaining data integrity and simplifying the task of gaining valuable insights. 

Graas prioritizes data integrity at its core. Unlike manual data collection, where human error can easily compromise accuracy, Graas automates the entire process. It fetches data from various sources, including marketplaces like Flipkart, Lazada, and Amazon, your own store, and marketing channels like Facebook and Google Ads. 

Grass fetches these data via API integrations and a custom browser extension for direct data extraction from the source. This does two things—first, it ensures that the data is reliable, and second, you don’t have to manually log in and data transfers across multiple platforms. 

Furthermore, Graas handles data transformation automatically, ensuring consistency and eliminating the potential for errors introduced during manual conversion from heterogeneous to homogeneous. This significantly reduces the risk of inaccuracies creeping into your data, which is crucial for reliable and actionable insights. 

Beyond maintaining data integrity, Graas simplifies the process of gaining insights. You don’t have to log in to multiple platforms 20 times a day and spend hours piecing together information. Graas's Marketing Deep Dive feature offers a comprehensive and user-friendly interface to analyze your sales performance across various channels with unmatched precision. 

Graas consolidates insights from all your channels into a single, intuitive dashboard. It allows you to measure all the efforts from marketing to sales to inventory. It is real-time, so your data won’t be stale leading to more accurate insights. 

Preserve the integrity of your eCommerce data. Sign up for free today! 


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