Free Resource
Free Data Warehouse Readiness Checklist
Find out if your organization is truly ready to build or migrate to a modern data warehouse — before you spend a dollar on infrastructure.
What’s inside the checklist:
- Data source inventory worksheet (systems, formats, owners)
- Data quality readiness scorecard
- Team and skills gap assessment
- Cost planning framework (cloud vs. on-prem)
- Top 7 pitfalls that derail warehouse projects
- A simple 20-point readiness scoring checklist
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Why readiness matters more than tooling
Most data warehouse projects don’t fail because teams picked the wrong platform. They fail because the organization wasn’t actually ready — the source data was messier than expected, nobody owned data quality, or the team underestimated the skills and time required to maintain the system after launch. This checklist walks you through the same readiness questions our consultants ask before recommending an architecture.
1. Data source inventory
Before any design work starts, list every system that will feed the warehouse — CRMs, ERPs, spreadsheets, IoT feeds, third-party APIs. For each, capture format, refresh frequency, and a data owner. Projects that skip this step routinely discover “surprise” sources mid-build that blow up timelines.
2. Data quality readiness
Garbage in, garbage out still applies. Check for duplicate records, inconsistent naming conventions, missing keys, and unreliable timestamps in your source systems. A quick sample audit of your top 3 data sources will tell you more than any vendor demo.
3. Team and skills readiness
Someone needs to own ETL/ELT pipelines, modeling, and ongoing governance after go-live — not just during the build. Identify who on your team will maintain the warehouse, and where you’ll need outside help (even temporarily) to fill SQL, pipeline, or BI skill gaps.
4. Cost planning
Budget for more than the platform license: storage growth, compute for transformations, connector/integration tools, and the engineering hours to build and maintain pipelines. Model costs at 12 and 24 months, not just launch day.
5. Cloud vs. on-prem
Cloud warehouses (Snowflake, BigQuery, Redshift) offer elastic scale and lower upfront cost but can surprise you on usage-based billing. On-prem or hybrid can make sense for strict data residency or compliance needs. The right choice depends on your data volumes, compliance requirements, and in-house infrastructure expertise.
6. Common pitfalls to avoid
- Starting the build before defining what business questions the warehouse must answer
- Underestimating the effort required for data cleansing and transformation
- No clear owner for data governance and quality after launch
- Choosing a platform based on hype rather than actual workload and team fit
- Skipping a small pilot before committing to a full-scale rollout
Get the full checklist
Enter your email above to download the complete 20-point Data Warehouse Readiness Checklist, including the scoring worksheet our consultants use during infrastructure assessments.