We’ve all been there—scrolling through our gallery only to find three copies of the same sunset photo, saved under different filenames: “IMG_2023”, “Sunset_final1”, and “Sunset_reallyfinal.jpg”. Multiply that by a few hundred, and suddenly your storage is bloated, albums are a mess, and finding the real best shot takes forever.
Now imagine that happening with products in your business.
Duplicate SKUs cause the same kind of chaos—but with real consequences: miscounts, wrong orders, unhappy customers, and confused systems. An inaccurate inventory count, a major result of SKU duplications, usually leads to either understocking or overstocking of a product. These could become a cause for major loss in business. The aforementioned problems usually lead to poor demand forecasting– it becomes painstakingly hard to comprehend which SKUs are performing well and which are not. This further leads to an ill-informed procurement of products in the future. In fact, even supply chains can be affected due to such discrepancies. Inconsistent SKU references can result in issues with purchase orders, shipment tracking, and supplier coordination. Not to mention, such duplication would trigger inaccurate analytics and a breakdown of integration within various systems.
What businesses then suffer are huge losses, not only because of the losses in the market, but also because cleaning up such piled up and unorganized data incurs high costs. To aid this, and combat the various difficulties that come with it, dataX.ai stresses the importance of Data De-duplication.
As the name suggests, this tool allows for any duplication of data to be eliminated, and thus allows for clean, organized, and accurate data. Duplication of data could be detrimental to businesses, especially B2B eCommerce, because it affects revenue-making as well. All the issues highlighted above have a domino effect, and lead to many more complications, including buyers becoming frustrated. So what can the de-duplication tool achieve?
dataX.ai’s de-duplication model can be applied to both small and large scale data sets, which allows a variety of businesses to clean up anomalies in hitches with their data sets and successfully enter the market. This, in turn, allows these businesses to make faster and more informed decisions regarding their products.