In the fiercely competitive retail landscape, the ability to make rapid, data-backed decisions is no longer a luxury but a fundamental requirement for survival and growth, yet many expanding companies find themselves shackled by outdated, manual processes that create more questions than answers. For a rapidly growing kitchenware company like Made By Gather, home to beloved brands such as Bella and Drew Barrymore’s Beautiful, the challenge was stark: its fragmented data foundation was becoming a significant barrier to efficiency. The daily grind of manually pulling information from disparate retailer portals and compiling it in spreadsheets created a slow, cumbersome system that struggled to keep pace with the business. This scenario illustrates a common pain point where the very data meant to illuminate the path forward instead casts a shadow of inconsistency and delay, making proactive strategy nearly impossible and forcing teams into a constant state of reactive problem-solving.
The Pitfalls of Manual Data Consolidation
The operational reality for many businesses involves a time-intensive and error-prone approach to data management that directly impacts the bottom line. Before its transformation, Made By Gather’s team dedicated countless hours to manually accessing individual retailer portals, from Target to Amazon, to extract sales and inventory information. This raw data was then painstakingly compiled in Excel, a method that, while familiar, is notoriously slow and difficult to scale. The process required constant maintenance and verification, consuming valuable employee time that could have been allocated to strategic analysis. This reliance on manual aggregation created a significant lag between data collection and actionable insight, meaning that by the time a report was ready, the market conditions it described may have already changed. This inherent delay made it exceedingly difficult to respond to sales trends, manage inventory effectively, and maintain a competitive edge in a fast-moving consumer goods environment.
Compounding the issue of manual labor was the severe lack of data consistency, a problem that plagues many multi-channel retail operations. Each retail partner provided information in a different format, with unique data structures and naming conventions for products, stores, and performance metrics. This fragmentation resulted in a cascade of problems, including outdated analytics, significant reporting discrepancies, and costly operational errors. Without a single source of truth, different departments could inadvertently work from conflicting datasets, leading to misaligned strategies and inefficient resource allocation. The effort required to normalize this varied data was immense, often demanding dedicated internal resources just to maintain a semblance of order. For a growing company, this inability to achieve a clear, unified view of its performance across all retail channels made it nearly impossible to scale operations, innovate with confidence, or build reliable forecasts without risking significant financial and reputational damage.
Forging a Path to Automated Insights
To escape the cycle of manual data wrangling and unlock its true potential, the company made a pivotal decision to overhaul its entire data infrastructure by embracing automation. The solution involved partnering with the technology platform Crisp to create a system that could automatically consolidate data from all its retail partners and integrate it directly into its existing Snowflake data cloud. This strategic move eliminated the need for manual downloads and spreadsheet management, creating a seamless pipeline of clean, standardized information. The implementation was not merely a technical upgrade but a cultural one; the new system was rapidly adopted across the organization, with over 35 team members from various departments now using the analytics platform. This widespread adoption embedded clean data practices into the company’s core operations, transforming data from a cumbersome liability into an accessible, reliable asset that empowers employees at every level to make more informed decisions.
The operational impact of this data automation was both immediate and profound, yielding significant results that have been compounding over the past two years. With access to near-real-time information, Made By Gather dramatically improved its forecasting accuracy and replenishment processes, which was critical for maintaining a 95% in-stock rate at a major online retailer like Amazon. The ability to proactively identify risks and opportunities at the individual SKU and store levels gave the company a powerful competitive advantage. Instead of reacting to stockouts after the fact, the team could anticipate demand fluctuations and adjust inventory accordingly, maximizing sales and strengthening retailer relationships. This newfound agility in its supply chain operations allowed the company to consistently meet consumer demand, enhance its on-shelf availability, and build a reputation for reliability and operational excellence in a crowded marketplace.
The Tangible Returns of a Data-Driven Culture
The benefits of a robust, automated data foundation extended far beyond operational efficiency, directly fueling business growth and accelerating product innovation. With reliable, up-to-the-minute insights, the company could launch new products with a much higher degree of confidence. A prime example is its Bella Fits-Anywhere line at Target, which leveraged precise market data to achieve a staggering 1,450% year-over-year growth. This success was not an anomaly but a direct result of the ability to understand consumer behavior, track performance metrics, and pivot strategies in real time. Furthermore, the enhanced data capabilities made the company a more attractive partner for major retailers. The ability to provide clear, accurate, and timely performance data facilitated expanded distribution with partners like Costco and Best Buy, opening up new revenue streams and solidifying the company’s market position as a forward-thinking industry leader.
Ultimately, the most significant return on this investment was the strategic reallocation of human capital. By automating the tedious, low-value tasks of data collection and cleaning, the company freed up its key personnel, such as the data operations manager, to focus on what truly matters: strategic analysis and forward-looking planning. This shift transformed the role of the data team from reactive data janitors to proactive strategic partners for the entire business. When clean, standardized data is readily available across all departments—from supply chain and logistics to sales and marketing—it breaks down information silos and fosters a culture of collaboration. This holistic view of the business empowers the entire organization to become more agile, competitive, and informed, ensuring that every decision, from a marketing campaign to a supply chain adjustment, is backed by a unified and accurate understanding of the market.
Lessons in Scalable Foundations
The journey undertaken by Made By Gather provided a clear and compelling blueprint for modern retail success. The central lesson was that investing early in a clean, scalable, and automated data foundation is not an expense but a critical investment that yields exponential dividends during periods of growth. By transitioning from fragmented, manual processes to a consolidated, automated system, the company not only solved its immediate operational headaches but also built the infrastructure needed for sustained expansion. The experience demonstrated that readily available, trustworthy data empowers every function of the business, from optimizing supply chains to crafting resonant marketing campaigns. It proved that in today’s retail environment, the ability to see clearly and act decisively is what separates market leaders from the rest of the pack.
