In This Blog
- Aligning Your Data Foundation with 2026 Business Priorities
- TL;DR: Executive Summary
- The 2026 Data Cleanup Checklist
- If You Can Only Do Three Things, Start Here
- Where the Easiest Performance Wins Are Found
- Final Thoughts: Treat Data Like the Asset It Is
- Frequently Asked Questions
Aligning Your Data Foundation with 2026 Business Priorities
As organizations enter a new year, leadership teams are resetting priorities, refining strategies, and setting expectations for growth. One critical enabler often overlooked during this planning cycle is the health of the organization’s data foundation.
When optimized, data is a powerful strategic asset that enables better decisions, explains outcomes, and reveals performance drivers across the business. If that foundation isn’t reliable, aligned, or actively managed, even the strongest business strategies will struggle to deliver meaningful results.
We’ve created this checklist to give organizations a practical, structured way to evaluate their data foundation, identify gaps, and focus their efforts on the areas that will have the greatest impact in the year ahead.
TL;DR: Executive Summary
This is a high-level overview of how organizations should think about data cleanup at the start of 2026. It’s designed for leaders who want to understand what matters most without diving into technical detail.
If you’re short on time, here are the key takeaways:
- Data cleanup is about enabling the business, not just fixing technical issues.
- Data should be treated as an enterprise asset, not something owned by individual systems or teams.
- Governance, accountability, and usage matter as much as architecture.
- Alignment between data and business objectives is more important than tooling decisions.
- Unused data pipelines, reports, and dashboards create unnecessary cost and risk.
- A structured checklist helps teams prioritize improvements without trying to do everything at once.
The full checklist, explanations, and practical guidance are outlined in the sections below.
The 2026 Data Cleanup Checklist
Use this checklist to clean up and strengthen your data foundation.
Each section includes practical steps to help you refresh, optimize, and realign your data environment for the year ahead.
1. Reassess Backups & Recovery Plans
Make sure your data is protected and recoverable if something goes wrong. No one wants to plan for worst-case scenarios, but security incidents and failures are inevitable. The ability to respond quickly and recover data matters more than ever.
2. Clean & Standardize Data Quality
Data quality issues often stem from disconnected business processes. When data is treated as a byproduct of individual systems instead of as an enterprise asset, accuracy and completeness suffer. These gaps tend to ripple downstream and create larger issues over time.
3. Validate Data Sources & Integrations
Review where your data comes from and how it flows through the organization. Pipelines, integrations, and feeds that are no longer accurate or necessary should be deprecated. Anything running in your environment creates cost and risk.
4. Review Governance, Security & Permissions
Governance isn’t just about locking data down. It’s about ensuring the right people can access and understand data while still protecting sensitive information. Starting from an open mindset and intentionally closing what needs to be restricted often enables better insight across the business.
5. Refresh Schemas & Metadata
Businesses evolve constantly, and data platforms need to evolve with them. When schemas and definitions don’t reflect how the business actually operates, reporting gaps and confusion follow. Staying connected to the business helps prevent this drift.
6. Optimize Storage, Indexing & Performance
Backend optimization often delivers the biggest performance gains. Improving how data is processed and stored allows front-end dashboards to remain simple while still meeting business SLAs and performance expectations.
7. Evaluate Pipelines & Automation Opportunities
Manual or fragile processes tend to break over time. Identifying opportunities to automate pipelines improves reliability, reduces errors, and frees teams to focus on higher-value work.
8. Update Dashboards, KPIs & Analytics Models
Organizations often accumulate reports and dashboards without knowing whether they’re still used. Reviewing usage, identifying owners, and retiring unused assets reduces clutter and risk.
9. Align Data with 2026 Business Goals
Your business is always evolving, and your data platform should evolve with it. Mergers, process changes, new strategies, and shifting priorities can quickly create gaps if your data systems aren’t closely connected to what the business is trying to achieve.
Use this time to reestablish alignment between your data platform and your 2026 business goals. The tighter the connection, the easier it is to stay in sync and avoid drift between strategy and execution.
If You Can Only Do Three Things, Start Here
If you can’t tackle everything at once, these three areas tend to have the greatest impact.
- Align Data to Business Objectives
The first thing to look at isn’t technical architecture; it’s whether your data platform is supporting how the business actually operates. Too often, data models are built for isolated reporting needs instead of modeling end-to-end business processes. Alignment allows organizations to understand outcomes and the drivers behind them. - Reinforce Governance and Accountability
Governance is about creating mechanisms: tools, processes, and forums, to review performance and drive accountability. Dashboards without ownership don’t lead to action. Measurement only matters when it’s paired with accountability. - Reassess Backups and Continuity Planning
Security and resilience matter more than ever. It’s not a question of if something will happen, but when. Reviewing backups and recovery plans annually ensures your organization can respond quickly and contain issues when they arise.
Where the Easiest Performance Wins Are Found
Some of the most effective performance improvements come from backend data processing. By keeping dashboards and front-end tools simple and pushing complexity into well-designed backend systems, organizations can deliver faster insights and better user experiences.
This approach allows the business to self-service data without requiring deep technical expertise, while still meeting performance and reliability requirements.
Final Thoughts: Treat Data Like the Asset It Is
Organizations know their data is important, but that doesn’t always translate into behavior. Data should be treated like any other enterprise asset — something that requires care, ownership, and active management.
When data stays at the forefront of conversations, organizations gain a clearer understanding of how actions in one part of the business affect outcomes in another. That visibility enables tighter control, better decisions, and stronger results over time.
Frequently Asked Questions
Why do data platforms fall out of alignment with the business over time?
Data platforms are often treated as projects with an end date, while businesses continue to evolve. Mergers, new markets, changing processes, and shifting priorities all impact how data should be modeled and used. Without ongoing collaboration between business stakeholders and data teams, misalignment is inevitable. Regular check-ins help ensure the platform evolves alongside the business.
What data quality issues do organizations most commonly overlook?
Many issues stem from treating data as a byproduct of individual systems rather than as an enterprise asset. Upstream processes often collect only what they need, without considering downstream use cases. This leads to incomplete or inaccurate data that creates gaps in reporting, analytics, and insight. Over time, trust in the data erodes.
How can organizations identify data sources or reports that are no longer needed?
Usage data is a strong starting point. Most modern platforms provide insight into how often dashboards and reports are accessed. Working backward from usage helps identify assets that no longer deliver value. Retiring unused pipelines and reports reduces cost, complexity, and security risk.
What governance mistake do organizations make most often?
A common misstep is starting from a fully closed data environment and granting access only by request. This limits exploration and makes it difficult to understand how data connects across the business. Starting open and intentionally restricting sensitive data allows teams to uncover insights while still maintaining appropriate controls.
How often should organizations revisit their data cleanup efforts?
The start of the year is an ideal reset point, but data cleanup shouldn’t be annual-only. Organizations should revisit key elements throughout the year, especially as business priorities change. Treating data as an actively managed asset helps prevent small issues from becoming major problems.
