The DSMS Playbook

Table of Contents

1. Cloud Costs Don’t Start at Storage — They Start at the Data Layer

Posts #1 and #2 in this series showed why cloud storage costs climb:

• Storage is overprovisioned
• Snapshots accumulate
• Cold data lives in hot tiers
• Retention and tagging policies drift
• No one has visibility into what data actually exists

Those are storage symptoms.

The root cause sits one layer up:

Cloud storage costs are ultimately the result of unmanaged, unclassified, ungoverned data.

If teams don’t understand:

• what the data is,
• who owns it,
• how valuable it is,
• or whether it should be deleted…

…then no amount of tiering or rightsizing solves the underlying cost problem. This is why Data Storage Management Services (DSMS) exist.

2. What DSMS Actually Is (and Why Cost Leaders Rely On It)

Data Storage Management Services (DSMS) is a category defined by Gartner to give organizations a unified, metadata-rich view of all enterprise data across:

• Multicloud
• Hybrid
• On-prem
• SaaS apps
• Unstructured repositories

If cloud storage optimization focuses on volumes, buckets, and shares…

DSMS focuses on the data inside them.

DSMS answers questions cost teams can’t otherwise answer:

• What is this data?
• How old is it?
• Does it have business value?
• Is it duplicated?
• Is it sensitive?
• Should it be archived, tiered, or deleted?

This directly influences cloud cost because:

Cost = storing the wrong data, in the wrong place, for the wrong amount of time. DSMS fixes that.

3. The DSMS Lifecycle: How It Eliminates ROT and Waste

DSMS platforms follow a predictable lifecycle that mirrors how cost leaks occur.

1. Discover

Scan all structured and unstructured data across storage platforms.

2. Classify

Enrich data with:

• Metadata (author, department, timestamps)
• Content analysis (keywords, entities, file types)
• Sensitivity labels
• Usage patterns

3. Govern

Apply and enforce:

• Retention rules
• Deletion policies
• Legal holds
• Residency requirements

4. Optimize

Execute automated lifecycle actions:

• Archive cold data
• Delete ROT (redundant, obsolete, trivial) data
• Migrate data to cheaper tiers
• Consolidate redundant datasets

5. Report

Provide dashboards showing:

• ROT volume
• Storage cost impact
• Compliance alignment
• Ownership gaps

This is the lifecycle that prevents cloud costs from spiraling.

4. DSMS vs. Traditional Storage Tools: Why Cost Outcomes Diverge

Traditional storage tools answer:

• How much capacity do we have?
• How fast is storage performing?
• Is the system healthy?

Useful, but not cost-directed.

DSMS answers:

• What percentage of our storage is junk?
• Which data can we delete safely?
• Which departments generate the most ROT?
• Where are the retention violations?
• How much data should be archived automatically?

The distinction is simple:

Storage tools optimize storage.

DSMS optimizes the data that drives storage costs.

This is why organizations adopting DSMS often see double-digit percentage savings in their first 90–120 days.

5. The Three Cost Problems DSMS Solves

1. ROT Data (Redundant, Obsolete, Trivial)

ROT is usually 30–60% of all unstructured data in an enterprise.

ROT causes:

• Unnecessary storage consumption
• Expanded backup windows
• Higher replication costs
• Legal exposure due to over-retention

DSMS identifies and eliminates ROT at scale.

2. Misplaced Data

Cold or inactive data sitting in premium storage is one of the largest cloud waste drivers.

DSMS automates:

• Archival
• Tier migration
• Temperature-based lifecycle moves

This turns tiering from “best effort” into policy-driven enforcement.

3. Over-Retention & Compliance Drift

Most companies store personal or sensitive data far longer than required.

This creates:

• Legal risk
• Audit complications
• Excessive storage consumption

DSMS enforces retention policies consistently across environments.

6. How DSMS Supports AI & Analytics Readiness

Cost outcomes are only half the story.

DSMS also prepares enterprises for analytics and GenAI by:

• Building structured metadata
• Auto-tagging data for search and retrieval
• Providing lineage and context
• Removing noise (ROT) from training datasets

DSMS is becoming part of the AI infrastructure foundation, because models require:

• governed data
• clean data
• discoverable data
• high-signal, low-noise corpuses

This is a natural evolution of cost management into strategic data enablement.

7. What DSMS Looks Like in Practice

Data Visibility

A unified catalog of all enterprise data — across file, object, SaaS, and cloud providers.

Policy Automation

Rules that govern:

• retention
• deletion
• migration
• archival

Automatically, not manually.

ROT Elimination at Scale

Bulk remediation for:

• duplicate files
• stale datasets
• outdated snapshots
• abandoned project folders

Cost Forecasting

Predictive models showing:

• how much storage will grow
• what ROT will cost
• which departments or workloads drive expansion

Governed Deletion

Defensible, auditable removal of data no longer needed — reducing storage costs and legal exposure simultaneously.