This article originally appeared on Financial Express.

Three months into 2024, and the buzz around Artificial Intelligence (AI) refuses to die down. From revolutionizing various business sectors to streamlining customer service, AI promises to transform various aspects of our lives. However, a crucial element that often gets overshadowed in these discussions is Data. AI models need clean data to deliver accurate and reliable results. 

Errors stemming from poor data can lead to various negative outcomes for businesses, including overestimating demand, misallocating resources, and misunderstanding customer behavior. 

Studies suggest that companies lose 12% on average due to bad data. In 2024, cleaning up of data will be crucial for eCommerce brands to achieve success from Artificial Intelligence (AI) and various models. 

But why is it so challenging to clean data? To start with, eCommerce businesses juggle with data flowing from a dozen different sources – ad platforms, websites, social media, marketplaces, Content Management Systems (CMS), Order Management Systems (OMP), Supply Chain Management Systems (SCM), Warehouse Management Systems (WMS)…the list goes on. 

Each platform collects, stores, and processes information differently, creating a data library that’s anything but uniform. This scattered and diverse data presents several challenges when it comes to cleaning. Firstly, data isn’t always neat and organized. It’s heterogeneous; structured tables might sit alongside messy PDF reports, each requiring different approaches to transform into usable information. 

Secondly, the sheer volume and variety of data make the cleaning process itself a hurdle. Extracting, analyzing, and correcting inconsistencies is time-consuming and labor-intensive, leaving room for human error. By the time businesses finish cleaning their data, it can already be outdated. 

This lack of real-time information or reliance on outdated data can have disastrous consequences. If we go the traditional route, eCommerce businesses must have a strong infrastructure to get their data cleaned. They need a central data warehouse to consolidate information from various sources, allowing detailed analysis and cleaning procedures. 

Equally important is the team responsible for managing this data. Data engineers design, build, and maintain the infrastructure, ensuring smooth data flow and implementing data quality checks. Data analysts then analyze the data to identify inconsistencies, recommending and implementing cleaning strategies. 

The team must have technical skills that encompass familiarity with data warehousing, data lakes, data integration tools, and programming languages like Python and SQL. 

An investment is also required in terms of resources. Like a data governance framework that establishes clear policies and procedures for data collection, storage, access, and usage, ensuring data quality and compliance. Additionally, data cleaning tools offer software solutions specifically designed for tasks like deduplication, standardization, and error correction. 

While the potential of Artificial Intelligence (AI) is undeniable, its success depends majorly on one crucial element: Clean Data. No matter how sophisticated an algorithm is, it can only process the information it’s given. The best algorithms are just tools; the true value lies in the clean data they leverage. AI models are a combination of data and algorithms, and without the former, the latter becomes ineffective. 

It is equally important for eCommerce businesses get data in real-time. Customer behavior is dynamic, so the model needs continuous data ingestion to adapt. Techniques like data cleansing and anomaly detection become essential to maintain data quality at scale and ensure the model learns from representative information. Real-time data, on the other hand, enables dynamic recommendations, adjusting to individual browsing sessions and purchase intent for a personalized experience. 

While clean data forms the foundation for success, it’s merely the first step. Clean data is raw material for something valuable — data needs to be shaped into actionable insights to truly unlock its potential. 

From a technical perspective, data analysis plays a crucial role. Techniques like data mining, statistical analysis, and machine learning are used to identify hidden trends and patterns within the data. These insights reveal valuable information, such as the understanding of what products customers are buying together, which features they favor, and their browsing behavior. This information allows eCommerce businesses to run targeted marketing campaigns and product recommendations. 

Businesses can also identify emerging trends in the market, allowing them to adapt their strategies and stay ahead of the competition. Analyzing data from various sources like logistics and customer service can reveal bottlenecks and areas for improvement, leading to optimized operations and cost savings.

These insights, from a business point of view, are invaluable for making informed decisions. If an eCommerce business is struggling with high cart abandonment rates, then by analyzing customer behavior data, they might discover a confusing checkout process or hidden fees contributing to the issue. With this insight, they can implement solutions like simplifying the checkout flow or offering free shipping to address the problem and improve conversions. 

Choosing the right tools is, therefore, critical. Businesses require data extraction tools that can efficiently gather information from various sources, ensuring it’s clean and ready for analysis. Additionally, data visualization tools play a key role in translating complex data sets into easily understandable charts and graphs, facilitating clear communication of insights to stakeholders across different departments. 

The combination of clean data, powerful analytics, and the right tools allows businesses to use their data to its truest potential. It helps businesses move beyond intuition and guesswork, basing crucial decisions on concrete evidence and insights. Clean data can show businesses the path to profitability. 

Authored By Prem Bhatia, Co-Founder and CEO, of Graas.