The Importance of Clean Data and How This Can Help With Margins

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It All Starts with Clean Data

Introduction

Much like our daily hygiene, and the need to (at least somewhat) regularly clean our rooms and homes, data cleanup is non-negotiable when it comes to businesses. Not only is this for the general upkeep of databases, this helps companies stay on track by providing accurate trends, analyses, and keeps them functioning smoothly. A lapse in data clean-up can lead to a massive pile-up of data, both wanted and unwanted. This could, quite rapidly, cause a domino effect of the worst kind, that could ultimately result in major losses for a company. 

Why Clean Data?

  1. Need for Accuracy and Reliability: Unclean and disorganized data can lead to faulty trends and incorrect customer segmentation, which could lead to uninformed strategic decisions. Regular cleaning up of data is exactly what can help prevent this. 
  2. Effective Machine Learning: ML models are trained through companies’ existing datasets, and learn to find patterns. If, however, they are trained on data which is unreliable, and riddled with inconsistencies and errors, this training could be a complete waste of resources for companies. 
  3. Impact on Revenue and Costs: Organized and clean data would ensure accurate trend analysis, which would in turn mean better customer service, accurate pricing, and effective cost-selling, which would boost revenue for the company. 
  4. Operational Efficiency: Accurate data also helps save the different teams in a company, such as the sales team, marketing team, and more, considerable amounts of resources, purely because they will only have the correct and necessary information at hand. In a case where the data is inconsistent, or incorrect, these teams would have to waste a lot of time in figuring out how to acquire the required information, which would slow down the company’s functioning significantly. 

dataX.ai’s Role

  1. Automated De-duplication: We ensure a single source of accurate information by merging, or removing, any duplicate SKUs across various systems. 
  2. Seamless Integration: Clean data ensures a seamless connection with leading PIM/ERP platforms, which prevents any disruption in the workflow of our clients. 
  3. Attribute Matching: In cases where product details are incomplete, or missing, our model matches it with attributes from the existing SKUs in the database, and finds matches accurately. 
  4. Validation and Error Handling: Incoming data is validated against the pre-defined business rules and requirements of our clients. Moreover, we ensure accuracy even during updates or bulk imports.