Data Lake Implementation Project Manager Simulation — Target

The scenario

Target Corporation — the eighth-largest retailer in the United States with $107B in annual revenue — has approved an enterprise data lake implementation to replace its aging Teradata data warehouse infrastructure. The current environment is fragmented: Merchandising runs its own Teradata instance with 15 years of proprietary analytics models, Marketing operates a separate CDP built on Adobe Experience Platform, Supply Chain uses a mix of SAP BW extracts and Excel, and Digital has built a real-time event pipeline on Kafka that feeds into its own Redshift cluster. The business case is straightforward: Target cannot execute its 'unified guest experience' strategy when guest data lives in four disconnected systems. The Target Circle loyalty program — with 100M+ members — generates data that should power personalization across every channel, but today it takes 6-8 weeks to get a cross-functional dataset built. The board approved $6.8M to consolidate onto Snowflake on AWS, with Slalom Consulting as the implementation partner. The project is technically achievable. The politics are the problem. Every business unit has built analytics capabilities independently for years, and consolidation means giving up control. You have been brought in as the IT Project Manager to deliver the platform — but your real job is getting four powerful VPs to agree on shared data standards, migration priority, and governance rules when each of them believes their data domain should come first.

What you'll do as the project manager

• →Consolidate four disparate data environments (Teradata, Adobe CDP, SAP BW, Redshift) into a single Snowflake-based enterprise data lake on AWS
• →Deliver a unified guest profile integrating Target Circle loyalty data, POS transactions, digital behavior, and marketing engagement — accessible within 24 hours of event capture
• →Enable self-service analytics for 200+ business analysts across Merchandising, Marketing, Supply Chain, and Digital within 90 days of go-live
• →Establish an enterprise data governance framework with shared data standards, access policies, and data ownership model approved by all four business units
• →Reduce cross-functional dataset delivery time from 6-8 weeks to under 48 hours

Project management skills you'll build

The challenges you'll navigate

• •Stakeholder alignment: Four business units with competing priorities and no history of shared data governance — consensus on migration order and data standards may be difficult to achieve
• •Merchandising resistance: Merchandising has operated independently for 15 years and views consolidation as a loss of analytical advantage. Their VP has publicly questioned whether a shared platform can meet their performance requirements
• •Data quality variance: Each source system has different data quality standards, naming conventions, and update frequencies. Harmonization effort is likely underestimated
• •Q4 freeze deadline: If the platform is not operational by October 15, the project stalls for 3 months and the Teradata renewal becomes unavoidable
• •Budget allocation model: The 40/20/20/10/10 split creates perverse incentives — business units paying less may demand more, and Merchandising (paying 20%) may argue they deserve priority

Technology & stakeholders

You'll manage 7 stakeholders, including Brett Craig (EVP & Chief Information Officer), Nithya Rajan (VP Enterprise Data & Analytics), Mark Schindele (EVP & Chief Stores Officer), and more.

What you'll walk away with

A verified, shareable record of a completed enterprise project — plus the PMO deliverables you produced along the way (charter, project plan, SteerCo deck, closure document). It's real, demonstrable project management experience you can put on your resume and speak to in interviews.

Frequently asked questions

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