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Navigating the AI Hype Train: Why Retailers Need to Invest in Data Foundations

The last year or so has seen tremendous progress and news on the cutting edge of analytics and data tools. Artificial Intelligence (AI) has been written about repeatedly in every publication you can name. Debates are ongoing in Congress around regulation, and new complications are happening daily with the ethical use of this novel technology. Venture capitalists and other fundraising avenues invested $15.2 B in the first six months of 2023 alone.  

With all this news and swirl, retailers are concerned that if they don’t start investing in these cutting-edge tools, they will get left behind. However, these trendy tools can often distract from the development of data foundation capabilities that many retailers still lack. Investing in the core setup of data can enable more significant ROI long-term and avoid the perception that data and analytics are boondoggles that often don’t pay off.  Below, we unpack why retailers need to invest in data foundations and how to get started on that journey.

What Are Data Foundations? 

Data foundations are the underlying platforms and processes supporting decision-making throughout the retail organization. These include data warehouses and lakes, ingestion pipelines, orchestration tools, and other systems that comprise the organization’s data platform. Beyond those, these foundations incorporate the organization’s processes to maintain data quality, govern their assets, and provide reliable answers to questions.  

These tools and processes have only gotten more complicated with the modern data stack and increasing volume and variety of new sources and use cases. New tools are released daily, enabling new capabilities for data teams but requiring management and integration to achieve the best results. Beyond the above, every platform retailers are using now is introducing new features, many of which are “AI-enabled” to improve their value proposition.  

Beyond the technical challenges, retailers are also at a disadvantage by the nature of their industry. The pace of customer preferences raises the need for quick insights and activations, which can limit the ability of teams to ensure high-quality pipelines. Beyond that, retail data is naturally cross-functional; high-selling product in one channel should be promoted in other channels. However, this relies on metric definitions, which may be inconsistent between the teams that manage these channels. 

This increasing complexity creates challenges and costs for retailers navigating a changing and demanding customer environment.  

How to Improve the Foundation 

Given these challenges, retailers must find ways to improve the foundation to continue to grow and manage the increasing demand for data and analytics organizations.  

  • Align your data and analytics work with your business outcomes. Asking questions like “What question is this analysis answering?” or “What organizational KPI is this effort supporting?” will help tie specific efforts to impactful results. Beyond that, close collaboration is required in data initiatives. Identifying and partnering with a key business stakeholder is crucial to meaningful outcomes. 
  • Actively manage your data assets. Not only should teams be thinking about onboarding these new data sources, but management and governance shouldn’t be ignored. Who owns certain assets, who is responsible for rectifying quality issues, and what data comes from them should be documented and tracked. Organizations should also leverage cataloging and lineage tools to understand how each source impacts topline BI metrics so issues can be proactively identified. 
  • Action on quality and security. “Garbage in, Garbage out” has been the mantra of data teams for decades, and that problem isn’t going away. Data quality should be embedded in the foundation. New data transformation tools provide automated testing capabilities, which can look for bad data and provide guardrails to avoid impacting reporting and decision-making. Retailer leaders must also be conscious of customer privacy and the increasing regulations in this space. Ensuring compliance with CCPA, GDPR, and new regulations can help protect the organization and build customer trust. 
  • Activate insights and data. Data accessibility and increasing awareness of insights via regular communication and lunch-and-learn events can greatly improve understanding of patterns within the organization. These efforts can create cross-domain data ownership and inspire new ideas and questions that can continue to drive growth within the organization as well as provide direct benefits in customer marketing and other key areas. 

Retailers are uniquely positioned to meet these challenges. Rather than relying on third parties to provide customer or sales data, retailers collect these directly from customer interactions, providing a high level of control from the collection through to the analysis. This control can help reduce quality issues and provide more robust data to support specific business use cases.  

Choosing the Right AI Use Case 

All the above isn’t to say that AI can’t also provide value to your organization. However, the critical step is selecting the right use case for these tools. There are so many new AI vendors, models, and products that it may be hard to identify what can create value vs. what has a pretty website and engaging ad copy. Beyond that, recent announcements from OpenAI create paths for retailers to recreate the functionality that these AI “wrapper” vendors offer, without requiring a third-party contract. This changing environment could lead to some of these new vendors shutting down, leaving their customers without support. 

Much like finding the best machine learning use case, organizations should start by identifying a problem that they are experiencing that causes real friction. Maybe it’s a time-consuming manual process in merchandising or a skill gap in engineering. Once identified, this provides some guardrails for the organization to explore the core question, Can AI help me solve my problem?  

Reviewing the options becomes much simpler when you can see if they help address the core problem and if the return is there for a tool. Leaders can build a business case for the cost savings or growth the tool provides and if that is worth the new tool’s cost.  

Retailers who invest in their data foundation and identify the right use cases for AI will have better outcomes than those who get caught up in the hype train. 

Reach out to us to learn more about setting up the data foundation for your retail business 

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Tags: Data Strategy, Emerging Technology, Retail Technology