Key Steps to Drive Effective Data-Driven Business Transformation

1. Ensure Excellence in Salesforce and NetSuite Implementation
   Start by conducting a health check of your existing systems to identify and resolve issues related to configuration, process inefficiencies, or integration gaps. Optimizing current systems, addressing backlog requirements, and eliminating technical debt will help establish a solid foundation. We recommend leveraging Salesforce Einstein and NetSuite NSAW, which are purpose built and can be both quick and cost-effective, leading to a higher success rate. Regardless of the approach, building this robust foundation is essential before implementing any data-driven strategy.
2. Define and Prioritize Business Problems
   Clearly define the business challenges you want to address and prioritize them. Whether it’s demand forecasting, inventory optimization, dynamic pricing, or other areas, a focused approach ensures that your transformation efforts are aligned with your most critical business needs. Consider post-production elements such as compliance requirements, process changes, and the impact on customers and employees, including training needs. At Lirik, we work closely with business teams to help them understand capabilities and limitations of AI/ML, ensuring everyone is aware of potential gains and pitfalls. This level of awareness empowers business teams to be a great collaborator and partner.
3. Validate Data Sources and Prepare for AI/ML Deployment
   After defining your business problems, identify the relevant data sources and checking for their availability, completeness, and accuracy. This process is straightforward if most of your data resides in Salesforce or NetSuite. If your needs extend beyond the native AI tools in Einstein and NSAW, or your data is complex and siloed, consider leveraging leading data platforms like Snowflake, Databricks, and AWS. Once confident in your data’s quality, move to production while adhering to AI/ML best practices—from model explainability to MLOps for monitoring model efficacy. Over time, look for opportunities to improve in other areas using a similar approach.