Bad data isn’t just a governance headache. It inflates storage, slows reports, and makes cloud bills swell. Thomas C. Redman’s well-known estimate pegged the annual cost of poor data quality in the United States at around three trillion dollars, a figure that still frames the business risk today.
Data Quality and Data Volume Move Together
Data Volume Management (DVM) focuses on keeping systems lean, but volume and quality grow together. Duplicates, inconsistent codes, and stale records inflate storage, slow queries, and complicate retention. SAP’s Information Lifecycle Management (ILM) frames a disciplined way to set residence times, block records under hold, and delete what no longer serves a legal or business purpose. That lifecycle thinking belongs at the center of every quality program.
The Storage Angle: Keep Hot Data Small
The storage model adds another layer. SAP HANA offers multi-temperature options so frequently accessed data stays hot while less active history moves to warm or cold tiers. Native Storage Extension (NSE) supports this pattern by holding rarely used rows in a buffer cache instead of expensive in-memory space. Teams that validate and de-duplicate data before tiering see the biggest savings because they keep “junk” from landing in any tier.
Analytics Choices: Virtualize First, Replicate Only When Needed
Modern analytics choices can either help or hurt. SAP Datasphere encourages virtual access to source data through remote tables, which reduces replication and keeps footprints predictable. If you copy everything “just in case,” you multiply errors and pay for them twice. Start with virtualization, then replicate only when a real performance need appears.
Quality Problems That Grow Footprints
- Duplicates. Replicated objects across analytics platforms create parallel copies that never quite match. SAP Datasphere and BTP documentation advise virtual access first to avoid unnecessary duplication.
- Stale history without purpose. Records outliving their retention add weight with no business value. Information Lifecycle Management provides rule-based retention, blocking, and deletion to keep history lawful yet lean.
- Over-replication. Continuous feeds copied into multiple stores magnify errors and cost. Even SAP’s docs outline when to switch from real-time replication to snapshots.
A Short Playbook That Links Quality to Volume
- Define purpose before retention. Map a table to its business or legal purpose, then apply ILM residence times and deletion rules. This trims historic weight without losing defensibility during audits.
- Virtualize first. Use remote tables to provide access without duplication; replicate only when measurements show a need.
- Gate tiering with data checks. Allow only complete and deduplicated records into warm or cold tiers so you don’t carry defects forward. HANA’s tiering choices make the policy enforceable.
- Measure waste in business terms. Track cycle time, write-off rates, and rework alongside storage growth. The financial case becomes obvious when quality fixes cut both operational delay and infrastructure spend.
Make It a Program, Not a Cleanup
For ongoing control, treat footprint work as a program rather than a one-off cleanup. Teams that follow data volume management in SAP guidance typically align quality rules with archiving, aging, and tiering so issues don’t reappear the next quarter. When analytics expands, keep virtualization as the default to avoid accidental bloat and protect performance at the same time.
Where to Start and How to Sustain It
As your operations mature, fold quality checkpoints into release pipelines and shared dashboards. This brings business owners, architects, and Basis teams to the same table and keeps cost signals visible. If you need a structured starting point, tap services that specialize in sap data volume management and make them part of your operating rhythm.
Close the loop with a lightweight standards page that defines naming, code lists, and retention by domain; it often prevents most of the growth in the first place. Keep independent references handy alongside sap data volume management as you tune systems for both accuracy and scale.