SAP Data Volume Management (DVM) is a continuous discipline for understanding why data grows, controlling what is created, and applying the right reduction measure at the right point in the information lifecycle. Archiving is one important measure, but DVM also includes prevention, avoidance, summarization, deletion, compression and ongoing measurement.

DVM is broader than an archiving project

SAP describes DVM as a framework of best practices, tools and services spanning discovery through continuous improvement. It balances business access to information with the operational need to control growth and use storage efficiently, subject to legal requirements and corporate policies.

This article explains how to run that lifecycle. For enterprise policy, governance and investment choices, see SAP Data Archiving: The Complete Guide. For the mechanics of SAP archiving objects and ADK, see SAP Data Archiving. Detailed run performance and financial forecasting belong in the separate performance monitoring and capacity and cost management guides.

The DVM lifecycle

1. Discover where volume is accumulating

Establish a repeatable landscape baseline: database size, growth rate, largest tables, fast-growing tables, system copies, interfaces and business processes that generate the records. Look across systems rather than treating each database as an isolated problem. Record the observation date, measurement method and system scope so later comparisons remain credible.

2. Profile the data and its business context

Table size alone does not explain what action is safe. Map tables to applications, document types, archiving objects, retention requirements, related documents and access patterns. Analyze time-based distribution, usage and dependencies. SAP DVM functions include data allocation statistics, table analysis, table fact sheets and age-based reduction analysis; availability depends on the SAP product and release.

3. Prevent unnecessary creation

The strongest control acts before data exists. Review excessive logging, trace retention, duplicate interfaces, obsolete variants, unnecessary document detail, avoidable attachments and custom processes that generate redundant records. Any configuration change needs process-owner approval, regression testing and an audit assessment. “Do not create” is not permission to omit information that the business or law requires.

4. Avoid or summarize future growth

Some SAP applications offer data-avoidance or summarization options. These can reduce future line-item growth by storing less granular information, but they may change reporting detail and may not affect records already created. SAP’s CO documentation, for example, notes that line-item summarization affects future postings and can remove fields from line-item reporting. Treat each option as application-specific, not as a universal DVM switch.

5. Select the appropriate reduction measure

  • Archive: remove eligible business-complete data from the online database while retaining controlled access for the required period.
  • Delete: remove data through an approved application or technical procedure when it has no continuing retention or business requirement. Do not substitute direct table deletion for supported lifecycle processing.
  • Summarize: retain an approved aggregate where detail is no longer required or avoid creating unnecessary detail in future.
  • Compress or reorganize: improve physical storage utilization where supported. These database measures can release or defer capacity, but they do not decide business retention and do not replace archiving.
  • Age: where supported, move less frequently accessed data between SAP HANA temperature tiers. Data aging changes placement and resource use; it is distinct from archiving and destruction.

6. Verify and improve continuously

Compare the result with the baseline, reconcile object counts and investigate exceptions. Then refresh the profile because business activity, retention rules, releases and custom development change. SAP’s DVM improvement projects support KPI-based tracking and trends over time. The practical objective is a stable operating rhythm, not a one-time database reduction.

Age is evidence, not eligibility

An age distribution reveals opportunity: a table may contain millions of records from prior years. It does not prove that those records can be archived or deleted. Eligibility may depend on business completion, residence time, linked objects, open items, legal holds, retention rules, application checks and the behavior of a specific archiving or destruction object.

Keep the two concepts separate in every forecast. Label age-based estimates as potential until supported application checks confirm executable scope. SAP’s DVM age-based reduction analysis provides a starting point for stakeholder discussion, including delivered best-practice residence times, but customer policy and application validation govern the decision.

Prioritize work with a transparent score

A ranked list of the largest tables is useful but incomplete. Score candidates using multiple factors:

  • current size and recent growth rate;
  • estimated eligible volume, not merely old volume;
  • business, compliance and historical-access risk;
  • availability and maturity of a supported reduction method;
  • dependencies, implementation effort and operational window;
  • expected effect on memory, storage, backup, migration or retirement scope.

SAP’s Prioritize Objects application supports weighted key figures for a ranked DVM list. Preserve the assumptions behind any score, and require business and technical owners to approve the final sequence. A smaller, low-risk object with high confirmed eligibility may be a better first wave than the largest table in the system.

Define roles before execution

  • DVM lead: owns the backlog, measurement method, roadmap and improvement cadence.
  • Application owner: confirms process completion, functional dependencies and supported measures.
  • Business data owner: approves access needs, residence assumptions and acceptable loss of detail.
  • Records, legal and privacy teams: define retention, holds, destruction and evidence requirements.
  • Basis and database teams: plan jobs, capacity, reorganization, backup and technical validation.
  • Security and audit: verify authorization, logging, segregation and control evidence.
  • Reporting owner: validates that required historical questions remain answerable.

Use KPIs that show control, not activity

A useful DVM scorecard combines leading and outcome measures:

  • database size and monthly growth by system, application and major object;
  • share of growth prevented or avoided through approved design changes;
  • old volume versus confirmed eligible volume;
  • archiving and deletion backlog by object and age band;
  • objects written, verified and deleted, plus exceptions and reruns;
  • compression or reorganization outcome where applicable;
  • historical-access success and unresolved business-access issues;
  • forecast versus measured online-volume reduction;
  • improvement actions completed, overdue and awaiting ownership.

Review execution indicators after each cycle, trends monthly or quarterly, and the prioritized portfolio after material business or landscape change. Use the performance guide for run-level measures and the capacity guide for translating technical outcomes into defensible forecasts.

Turn analysis into an operating backlog

For every candidate, record the data source, owner, age profile, confirmed eligibility method, proposed treatment, dependencies, test evidence, expected reduction and next review date. Pilot supported measures on representative data, reconcile results, validate access and then schedule recurring operation. Close an item only when the intended outcome has been measured—not when a recommendation slide is approved.

How ArchiveHub contributes

ArchiveHub helps teams connect SAP volume analysis with archiving feasibility, historical reporting and application-retirement requirements. It can support the assessment of candidate data, required relationships and access scenarios while governance and execution decisions remain with the customer’s authorized owners.

Begin with the complete guide, explore SAP archiving fundamentals, or request an assessment to establish an evidence-based DVM backlog.

Official SAP references

Scope note: DVM capabilities, applications and valid reduction methods vary by SAP solution, release, database and business object. Confirm current product documentation, retention policy and application-specific prerequisites before changing or removing data.