A Case Analysis: Target's Data-Driven Transformation: "Target: Creating a Data-Driven Product Management Organization" by Robert E. Siegel and David Kingbo

The case: https://store.hbr.org/product/target-creating-a-data-driven-product-management-organization/SM308

"Target: Creating a Data-Driven Product Management Organization" by Robert E. Siegel and David Kingbo 

Product DescriptionPublication Date: October 02, 2018

Source: Stanford Graduate School of BusinessIndustry: Retail and consumer goods

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Target Corporation: Creating a Data-Driven Organization  by Emmanuel Olimi Kasigazi

February 7, 2025

Introduction

Target, a major U.S. retailer, faced a pivotal moment in the mid-2010s. Increased competition from Walmart and Costco, coupled with the rise of e-commerce giant Amazon, presented a significant challenge. The company's traditional retail model needed modernization to compete with tech giants and meet changing consumer behaviors and futher more Target's fragmented digital infrastructure, reliance on third-party vendors like IBM and Infosys, and lack of centralized data governance hindered its competitive position. To survive and thrive, Target embarked on a strategic shift to become a data-driven organization, fundamentally changing its culture and operational approach. This case examines the challenges and successes of Target's transformation toward becoming a data-driven organization, focusing on the formation of Enterprise Data, Analytics and Business Intelligence (EDABI) in 2015, headed by Paritosh Desai, to transform Target into a data-driven organization.

Critical components involvedTarget's transformation

Target's cultural and technical transformation was driven by strategic talent acquisition, including 50+ PhDs, while bridging communication gaps between technical and retail teams through joint OKR policies. Their technical evolution centered on developing key innovations like the personalization engine, which drove $100 million in additional sales (2017), and the Cartwheel app reaching 40 million downloads. The company established a robust ethical framework by implementing clear data usage boundaries, preventing algorithmic bias, and maintaining customer trust through transparent data practices. Throughout this transformation, they balanced technical innovation with traditional retail expertise while prioritizing consumer privacy and business value.

Biggest Problem and Driving Issues: Bridging the Gap Between Data and Business

The most significant challenge Target faced was not simply the absence of data, but the inability to effectively integrate data-driven insights into its core business decision-making processes.
 The company's e-commerce platform lagged behind competitors, with digital sales representing only 5% of revenue. Citations from the Stanford study case show Target's leadership recognized "data and analytics could drive significant competitive differentiation," leading to the hiring of Paritosh Desai as Chief Data and Analytics Officer.

Several key factors contributed to this core problem::

Organizational Structure: As illustrated in Exhibit 4, EDABI initially reported to the Chief Operating Officer (COO), while Product Management resided under the CIO/CDO. This structural separation created a silo effect, hindering collaboration and leading to misaligned incentives. Product managers were primarily focused on driving key business outcomes like sales and customer engagement, while EDABI's initial focus was on developing and deploying sophisticated data-driven solutions.

Communication Barriers: EDABI's team, boasting over 50 PhDs in data science, utilized complex machine learning and deep learning algorithms. These "black box" methods were often opaque and difficult for product managers, who typically had more traditional retail backgrounds, to fully grasp. This lack of understanding bred distrust and made it difficult for product managers to fully embrace data-driven recommendations. As the case notes, "EDABI analysts oftentimes appear as nerdy or geeky, which is very different than most traditional retailers" (p. 5).

Conflicting Priorities: Data-driven recommendations, particularly from the personalization engine, sometimes conflicted with established branding guidelines or favored lower-margin product categories. For instance, promoting groceries on the homepage, while potentially data-backed, might not align with carefully designed creative campaigns or prioritize higher-margin items (p. 9). This created tension with product managers, who were directly accountable for brand consistency and profitability.

Fear of Losing the "Art" of Merchandising: Product managers expressed concern that an over-reliance on data-driven insights would diminish the intuitive, "artistic" element of merchandising that had historically been a key differentiator for Target. Desai himself acknowledged this, stating, "While AI and ML promise to increase the efficacy of many merchandising processes, we also worry that we might sacrifice the 'art' of merchandising that has allowed Target to differentiate itself" (p. 9).

Continued Analysis

Target's strategic shift was driven by the recognition that data and analytics were no longer optional but essential for competing in the rapidly evolving retail landscape. The rise of Amazon posed a particularly formidable challenge. According to a 2018 Wall Street Journal article, the company had been strengthening its dominance in e-commerce across various categories and was receiving more than 40 cents of every dollar spent online by U.S. consumers. The formation of EDABI under Paritosh Desai in 2015 was a direct response to this competitive pressure and the need to consolidate previously uncoordinated data efforts.

Thoughts on Improvement: Further Bridging the Gap and Ethical Considerations

While Target made significant progress, several areas could have been further optimized:

Proactive Cross-Functional Training: Implementing comprehensive cross-functional training prior to the full-scale launch of EDABI could have proactively mitigated the communication gap. This would have equipped product managers with a foundational understanding of data science principles and given data scientists a deeper appreciation for the nuances of retail business operations and merchandising.

Embedded Data Scientists: Strategically embedding data scientists directly within product teams, rather than maintaining a completely separate EDABI structure, might have fostered even closer collaboration, faster iteration cycles, and a more organic integration of data insights.

Iterative Algorithm Development with Early Feedback: Adopting a more iterative approach to algorithm development, actively involving product managers in the feedback loop from the very beginning, could have preempted some of the conflicts related to branding, margin considerations, and the "art" of merchandising.

Enhanced Data Governance: While the case mentions data governance, a more explicit and robust framework, clearly defining data quality standards, access protocols, and usage guidelines, could have further enhanced trust, efficiency, and data integrity across the organization.

Concluding Remarks

Target's data-driven transformation demonstrates both the potential and challenges of becoming a data-driven organization. The initial disconnect between the technically sophisticated EDABI team and the business-focused product managers highlights the crucial importance of organizational alignment, clear communication, and shared objectives. While Target successfully navigated many of these challenges, ongoing investments in cross-functional training, iterative development processes, and a robust data governance framework are paramount. The Target case provides a valuable lesson: a successful data-driven transformation is not solely about technology; it requires a fundamental shift in organizational culture, mindset, and a relentless focus on both business value and ethical responsibility.

 Reference List:

Sender, H., Stevens, L., & Serkez, Y. (2018, March 14). Amazon: The making of a giant. The Wall Street Journal. https://www.wsj.com/graphics/amazon-the-making-of-a-giant/