It’s a familiar scenario within fashion companies: Once merchandise hits retail, information about its performance, inventory levels, customer feedback and more all flows back toward different teams and systems. Without the ability to see the bigger picture in real time, retailers could be left with stock misalignment, leading to missed sales opportunities due to stockouts or eroded margins due to overstocks, both of which impact the bottom line.

“Data becomes a big issue there within an organization, and end of the day, it really causes issues on the financial side of the organization. All of this ends up hitting your P&L,” Ken Weygand, solutions architect at software firm Aptean, told Sarah Jones, senior editor, strategic content at Sourcing Journal, during a recent fireside chat.

To overcome these visibility issues, the industry needs more centralized, communicative data management. And users must be able to trust that the data in these systems is accurate, complete and current. “You build trust through transparency, so making sure that people can see where the data came from, when it was last updated and really how it was calculated,” said Mike Lisson, North America apparel sales director at Aptean.

For fashion sourcing teams, some of the most critical data points include information on suppliers, lead times and style masters—the main record for a particular style and its iterations. To build a stronger data foundation, Lisson suggested to focus first on which operational outcomes are desired—whether it’s reducing overstocks or making forecasts more accurate—and then building systems that ensure the data needed for those decisions reaches the right hands without becoming inaccurate. Spreadsheets do not suffice, and often lead to outdated figures rather than real-time insights. There can also be inefficiencies if data must be manually reconciled and copied from one platform to another, such as between an enterprise resource planning (ERP) solution and an e-commerce site.

Data is plentiful; having usable, actionable data is more commonly the missing piece. “It’s not just getting the data; it’s the decisions that you make with that data, and actions that you can take because of that data,” Lisson said.

To help companies turn data into action, Aptean has layered artificially intelligent agents on top of its software. Users can ask these agents questions in natural language to glean insights, tap them to automate routine tasks and look to them for predictions. For instance, an AI agent could flag that a particular style has seen strong interest and suggest a reorder.

“AI is going to help not just analyze historically what has happened and point out trends but also help with recommendations,” said Weygand.

Watch the video for more insights on establishing better data management and how AI can factor into decision-making.

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