GUIDE
Data quality isn’t an abstract problem that should sit somewhere down the line on your business roadmap. It is an immediate problem for your business and it is costing you daily.
According to an MIT study, 95% of generative AI implementations in enterprises fail to show measurable impact on profit and loss (P&L) due to poor source data.
Poor data quality costs organizations an average of $12.9 million per year according to Gartner.
A ¼” error in case height can result in about 1,000 fewer cases per truckload or about 6 more trucks than necessary per shipment.
39% of organizations have minimal to no data governance, resulting in fragmented and inaccurate data.
When your data quality drops, you expose yourself to a host of problems resulting in poor insights and bad decisions, which then lead to subpar commercial performance.
Flawed Decision-Making: Inaccurate or incomplete data can lead to misguided business strategies and poor decision-making, as executives rely on data-driven insights to understand market trends, customer behavior, and operational performance.
Operational Inefficiencies and Increased Costs: Poor data quality can cause redundant processes, transaction errors, and delayed decision-making, ultimately hindering productivity and driving up costs.
Regulatory Risks and Compliance Challenges: In industries like finance, healthcare, and manufacturing, data accuracy is crucial for regulatory compliance. Inaccurate data can result in costly violations, financial losses, and reputational damage.
Eroding Customer Trust: Inaccurate or outdated customer data can damage relationships and erode trust. For instance, sending targeted promotions to incorrect addresses or contacting unsubscribed customers can lead to customer alienation.
Data hygiene shares a lot of principles with regular hygiene. Do you let your food and dishes be handled by anyone without supervision? Would you let someone from the outside walk into your house without cleaning their feet? Do you not clean your house regularly? Similarly, you need to ensure the hygiene of your data with some regular maintenance and good policies.
Many elements determine data quality, and each can be prioritized differently by different organizations based on their goals.