How Data Quality Impacts Artificial Intelligence Efforts
Artificial Intelligence (AI) is all the hype right now in both personal and business situations. Manufacturers are excited about the promise and hope that insights from advanced analytics and AI can help revolutionize their decision making. AI can increase efficiency, offer predictive insights, and cost savings across the entire supply chain. However, one potential road bock can impact a business from reaping these benefits, the issue of data quality.
To really maximize the benefits of advanced analytics and AI, your business needs high-quality, accurate and well-organized data. You can only get out of something what you put in. If your business has a lack of trust in data quality, they are going to have a lack of trust in the decision-making process linked to that data. Essentially thwarting all efforts of using modern technology. Inaccurate or incomplete data can lead to costly missteps.
Addressing data quality is unfortunately not a one-time effort. It requires establishing processes and procedures that ensure your data quality is healthy and sustainable. An organization needs to identify critical data elements and the quality issues around their data.
Here are some key steps an organization can take to help improve their data quality:
- Establish a data governance policy that includes data ownership, quality standards, and validation processes
- Create processes that treat data quality as an ongoing process, not a one-time event
- Eliminate data silos
- Regularly monitor data quality metrics and analytics
Transforming the supply chain landscape through the integration of advanced analytics and AI is possible and exciting to consider. However, this transformation hinges on the quality of the data. Are you ready to fully leverage advanced analytics and AI with your current supply chain data management processes? If so, let the team at 2W Tech help in your journey.