Strategic Data Analytics Deployment: Essential Steps Before Machine Learning

Robot looking at a dashboard

Many organizations are embracing advanced analytics, such as machine learning and artificial intelligence, in their quest for a competitive edge. However, deploying these advanced analytics technologies without first establishing a solid foundation of data collection, storage, and reporting can be risky and costly. In this article, we’ll explore the importance of a strategic approach to data analytics deployment and provide a step-by-step guide for success. We’ll also provide examples from the supply chain and sales industries to illustrate the benefits of this approach.

Step 1: Identify Key Initiatives and Processes

Before deploying any advanced analytics, it’s important to identify the key initiatives and processes that need support. This can be accomplished using tools like Lean Six Sigma or business analysis techniques to identify areas for improvement. Once these areas have been identified, the next step is to establish a baseline of key performance indicators (KPIs) for each initiative or process.

Example: In the supply chain industry, a company may identify the need to reduce inventory levels while maintaining customer service levels. The baseline KPIs for this initiative may include inventory turns, order lead time, and on-time delivery.

Step 2: Data Collection, Storage, and Reporting

Once the KPIs have been established, the next step is to put in place the data collection, storage, and reporting capabilities to measure the baseline performance. This includes identifying the relevant data sources, establishing data quality and integrity standards, and implementing data governance policies.

Example: In the sales industry, a company may establish baseline KPIs for customer acquisition and retention rates. Data collection may involve integrating customer data from multiple sources, such as CRM systems and website analytics tools, into a single data warehouse. Reporting capabilities may include dashboards that provide real-time insights into customer behavior and sales performance.

Step 3: Diagnosing Reports

After establishing baseline performance and implementing data collection, storage, and reporting capabilities, the next step is to diagnose the reports to identify the top 5-10 offenders that prevent the organization from reaching its targets. These reports can be used to identify root causes of performance gaps and help focus improvement efforts on the most impactful areas.

Example: In the supply chain industry, a company may use diagnosing reports to identify products with low inventory turns or long order lead times. This analysis may reveal issues with forecasting accuracy or production scheduling that can be addressed using Lean Six Sigma techniques.

Step 4: Advanced Analytics Deployment

With the top offenders identified, advanced analytics can be deployed to tackle the root causes of performance gaps. This may involve using machine learning algorithms or recommendation systems to generate suggestions for targeted subsets of customers or to optimize production schedules.

Example: In the sales industry, a company may use machine learning to generate personalized product recommendations for specific customers based on their purchase history and browsing behavior. This can help increase customer retention rates and drive revenue growth.

Conclusion

Deploying advanced analytics technologies like machine learning and artificial intelligence can provide a competitive edge for organizations. However, this deployment needs to be made strategically and in steps. The first step is to identify the key initiatives and processes that need support and establish a solid foundation of data collection, storage, and reporting capabilities. Only then can diagnosing reports be put in place to identify the top 5-10 offenders that prevent the organization from reaching its targets. Once these have been identified, advanced analytics technologies can be deployed to tackle the root causes of performance gaps. By following this strategic approach to analytics deployment, organizations can maximize the value of their investments in analytics and achieve long-term success.

*the article has been written with the assistance of ChatGPT and the image has been generated using Midjourney

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