Common Pitfalls when evaluating and decommissioning data products & How to Avoid

Even with a structured approach, organizations often encounter challenges when evaluating and decommissioning data products. Failing to anticipate these pitfalls can lead to operational disruptions, compliance risks, and resistance from stakeholders. Below are the most common mistakes organizations make and strategies to mitigate them.

1. Lack of Stakeholder Involvement

?? Pitfall: Organizations often make decommissioning decisions in isolation, driven solely by IT or data teams, without consulting business users, compliance teams, or leadership. This can result in resistance, rework, or overlooked critical use cases.

? How to Avoid It:

To ensure a smooth decommissioning process, it’s essential to Engage Stakeholders Early by forming a cross-functional evaluation team that includes business leaders, analysts, compliance officers, and IT professionals. This collaborative approach ensures that all relevant perspectives are considered. Additionally, organizations should Conduct Structured Feedback Sessions to validate whether a data product is still valuable and continues to meet the needs of the business. Once the decision is made, it’s crucial to Communicate Decisions Clearly, providing stakeholders with detailed timelines, the reasons behind the decommissioning, and any available alternatives to minimize disruption and ensure a

Example: ?A retail bank’s data team retired a customer segmentation dashboard, assuming it was outdated. However, the marketing team heavily relied on it for personalized campaigns, leading to workflow disruptions. A better approach would have involved marketing in the decision-making process.

2. Overlooking Hidden Dependencies

?? Pitfall: Many organizations decommission data products without considering their interconnections with other systems, reports, or business processes. This can break automated workflows, cause compliance issues, or lead to data inconsistencies.

? How to Avoid It:

To ensure a successful decommissioning process, organizations should Use Data Lineage Tools such as Collibra or Alation to map out dependencies before initiating the shutdown. This will help in identifying any connections or integrations with other systems. Additionally, it’s important to Conduct Impact Assessments to identify any downstream systems and reports that may rely on the data product, allowing for a comprehensive understanding of the potential consequences. Finally, to avoid disruptions, organizations should Provide Alternative Solutions for impacted users before completely shutting down a product, ensuring that necessary tools and resources remain available to maintain business continuity.

Example: ?A commercial bank removed an old risk reporting system, only to discover that regulatory filings depended on its data. This resulted in compliance violations and urgent remediation efforts. A pre-decommissioning impact study could have prevented this.

3. No Clear Decommissioning Plan

?? Pitfall: Some organizations hastily shut down data products without a structured phase-out plan, leading to user frustration, loss of critical data, or compliance risks.

? How to Avoid It:

To effectively manage the decommissioning process, organizations should Implement a Phased Decommissioning Approach, gradually reducing dependencies on the data product before its final shutdown. This ensures that the transition is smooth and manageable. Additionally, it’s essential to Archive Historical Data to comply with legal and audit requirements before any deletion occurs, ensuring that all necessary data is preserved. Finally, organizations should Offer Training on Replacement Solutions to impacted users, minimizing disruption and ensuring they are equipped to seamlessly transition to the new systems or tools in place.

Example: ?A financial institution discontinued an internal fraud monitoring system without properly transitioning data to a new solution. This resulted in gaps in fraud detection until employees were retrained. A parallel transition period would have mitigated the risk.

4. Ignoring User Adoption Metrics

?? Pitfall: Organizations often assume that a data product is obsolete based on subjective feedback rather than actual usage data. This leads to premature retirement of valuable assets.

? How to Avoid It:

To ensure effective data product management, it is crucial to Track Real Usage Metrics, such as login frequency, report downloads, and API calls. This will help determine how actively the products are being utilized. For those that are underused, organizations should Categorize Underused Products to assess if they require enhancements instead of being decommissioned. Finally, conducting Surveys with End-Users allows organizations to understand how users interact with data products, providing valuable insights that can guide decisions on product updates, improvements, or decommissioning.

Example: ?A credit risk model was flagged for decommissioning due to low reported usage. However, usage logs showed that it was frequently accessed via an API in automated workflows. The bank revised its approach, updating the model instead of retiring it.

5. Not Accounting for Regulatory & Compliance Requirements

?? Pitfall: Deleting or retiring data products without considering regulatory retention policies can expose organizations to legal risks, fines, or audit failures.

? How to Avoid It:

Before retiring any data product, it is essential to Review Compliance Requirements, such as GDPR, BCBS 239, and CCPA, to ensure that all legal obligations are met. Additionally, organizations should Archive Essential Data to maintain auditability, even if the product itself is decommissioned. Finally, it is crucial to Involve Legal and Compliance Teams in the approval process to ensure that all regulatory and compliance standards are adhered to, preventing potential legal risks.

Example: ?A bank decommissioned a customer credit scoring dataset without retaining historical records. During an audit, regulators requested past credit decisions, which were no longer available, leading to non-compliance fines. Proper data retention policies could have prevented this issue.

6. Underestimating Change Management & Resistance

?? Pitfall: Employees and business users often resist the decommissioning of familiar data products, even if they are outdated or redundant. This can slow down transition efforts and impact productivity.

? How to Avoid It:

It is important to Communicate Early about the rationale, benefits, and timeline for decommissioning to ensure transparency and prepare all stakeholders. Additionally, offering Hands-On Training and Support for alternative tools will help users transition smoothly and reduce resistance. Furthermore, organizations should Highlight Success Stories where similar transitions have improved efficiency, showcasing the positive impact and encouraging buy-in from users and teams involved in the process.

Example: ?A major bank replaced Excel-based financial reports with a modern dashboarding tool. Many finance employees resisted the change due to familiarity with Excel. By offering customized training and one-on-one support, adoption improved significantly.

7. Lack of Governance for Continuous Evaluation

?? Pitfall: Organizations often treat decommissioning as a one-time event rather than an ongoing process. Without continuous evaluation, outdated products accumulate over time, leading to data clutter.

? How to Avoid It:

It is essential to Establish a Recurring Review Cycle (e.g., quarterly or annually) to regularly assess data products, ensuring they remain relevant and effective. Additionally, organizations should Automate Monitoring using data governance tools that can flag unused or underperforming products, making it easier to identify those that require attention. Moreover, it is critical to Make Data Decommissioning Part of Governance Policies to ensure that the process is structured, consistent, and aligned with organizational objectives. This approach helps streamline data lifecycle management while maintaining compliance and operational efficiency.

Example: ?A bank’s IT team struggled with hundreds of redundant dashboards. By implementing a quarterly review process, they reduced dashboard sprawl by 40% in the first year.

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