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The organization Jared works for is navigating a significant transformation. Largely transitioning from static SSRS reports to dynamic Power BI dashboards. This shift was facilitated by implementing Snowflake as the central data warehouse, enabling better data integration across various systems. By working with a third-party consulting group, the company has streamlined data management processes, allowing them to focus on core tasks like replenishment and inventory control.
Despite these advancements, the retail environment remains a blend of sophisticated technology and manual operations—a reality Jared likens to "the Jetsons meeting the Flintstones." While central teams manage 90% of revenue-related tasks, store-level employees perform essential duties like stocking shelves and engaging with customers. This duality underscores the need for systems that can seamlessly bridge high-level data insights and ground-level execution.
One of the most intriguing challenges Jared highlighted is managing rolling changes in SKUs. With a typical store location having 20,000 SKUs and 2,000 changing annually, these transitions significantly impact sales forecasting, pricing, and customer service. The manual effort required to maintain assortment integrity and ensure accurate forecasting occupies a significant portion of category managers' time.
The lack of universal databases for product equivalency further complicates this task. Industry Product Information Management (PIM) systems provide some support, but they fall short of offering comprehensive solutions. As Jared noted, the risk of inaccuracies in forecasting due to these rolling changes can have profound business impacts, from lost sales to diminished customer satisfaction.
AI offers a promising avenue to address these challenges. The organization is considering AI-driven solutions, with possibilities such as:
The next frontier involves integrating AI to tackle the labor-intensive process of managing rolling changes. By identifying patterns and automating manual tasks, AI could significantly reduce the time and effort required while improving accuracy.
The implementation of Snowflake was a pivotal step, facilitated by a carefully selected third-party team. This vendor partnership—secured through a technological headhunter—ensures ongoing support and collaboration, allowing the internal team to focus on leveraging data rather than managing infrastructure. The success of this partnership highlights the importance of choosing vendors with compatible pricing models and robust project portfolios.
Looking ahead, Jared is excited about the potential of using AI to generalize solutions for SKU management and forecasting. For instance, they aim to integrate weather data with sales trends to add causality to predictions, a project that promises both high rewards and technical challenges.
Another area of focus is exception-based reporting, which currently generates excessive noise. AI’s ability to filter and prioritize actionable insights could transform this process, enabling teams to address critical issues without being overwhelmed by data.
Jared’s insights reveal a dynamic landscape where technology and manual effort coexist. The journey from SSRS to Snowflake and Power BI underscores the value of modern tools in centralizing and simplifying data management. However, the real opportunity lies in AI’s potential to tackle persistent challenges like rolling changes and exception-based reporting.
It was very enlightening to take a deep look at the data landscape in a retail with Jared. We expect that the challenges that we discussed, like managing rolling changes, bridging high-level insights with on-the-ground execution, and maintaining data integrity across vast assortments are hurdles that most retailers face, not necessarily unique to Aubuchon. On the other hand there seems to be a lack of standardized industry solutions. This gap presents an opportunity for committed organizations like Aubuchon to differentiate themselves using new approaches and technologies leveraging data. With significant progress already made, Aubuchon is well-positioned to continue tackling these complexities and setting a benchmark for innovation and operational excellence.
Stay tuned for the next post in our series, where we uncover more stories of innovation and excellence from the world of data leadership. If these challenges resonate with your organization, let’s start a conversation about how data-driven solutions can transform your operations.
We’re excited to start our new blog series dedicated to conversations with data leaders. Each post will explore their challenges, innovations, and lessons learned in navigating the ever-evolving data landscape. We will be highlighting challenges they have overcome, pain points and the achievements they take the most pride in.
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