Data Storage Optimization – Increases Data's Value While Driving Down Costs

What is Data Storage Optimization and Why Is it So Valuable Now?

Data storage optimization maximizes the value of data by increasing efficiency, cost-effectiveness, and performance of enterprise data storage. Organizations are generating gigabytes of data every hour, while budgets remain fixed or even decreased.  This creates budget and management stress for data storage professionals, who are struggling with the cost of storage and how these data volumes are impacting performance, scalability, and compliance. 

Data storage optimization processes laser focus on understanding data’s use and value, then applying automation through policies that make sure data is stored in the right place, at the right cost, and with the right level of access. Continually optimizing where data is stored better supports analytics and regulatory requirements and is crucial for AI workloads. For IT leaders, data storage optimization strikes a tricky yet essential balance: fast access to business-critical data while controlling costs and mitigating risks. 

Key benefits include:

• Reduce storage spend
• Strengthen governance/compliance
• Improve AI readiness
• Automate data tiering and lifecycle decisions, giving time back to IT teams

Reducing storage spend, strengthening governance, and improving AI readiness all have measurable financial impact.

Try the ROI calculator to see how storage optimization could affect your costs.

How Storage Optimization is Different than Storage Management

Storage management focuses on the classic elements of maintaining, monitoring, and securing data. Storage optimization is focused on value: understanding what data you have, placing data in the optimal location based on who uses it for what purpose, and reducing costs related to stale/underused data, all through policies and automation. In an efficient and flexible enterprise, these disciplines work hand in hand. 

Storage management provides reliability and availability; storage optimization drives savings, performance, and compliance enhancements. For example, storage management might monitor usage and enforce access controls, while storage optimization intelligently tiers data to different types of storage based on its value score, and automatically moves sensitive or PII data to the most cost-effective storage based on compliance policies.

A Closer Look at Lifecycle Automation with Pentaho Data Optimization

The Five Keys to Effective Data Storage Optimization

1. Data Tiering and Lifecycle Management
   Intelligent data tiering places data in the most appropriate storage tier based on its usage and value. Frequently accessed “hot” data stays in high-performance storage like a Snowflake or Databricks, while inactive or stale data is moved to low-cost cold storage. Automation driven by policies continuously supports data movement when it makes sense, keeping storage tiers efficient and data in its right place.
2. Archiving and Retention Policies
   Regulations have a wide range of retention policies based on the data type and location. Leveraging established policies to keep only the necessary data for the required time frame reduces clutter and risk. Automating this process keeps organizations compliant and helps to simplify audits.
3. Deduplication and Compression
   Removing duplicate data and compressing files reduces storage requirements and costs, improving overall efficiency.
4. Cloud, Hybrid, and Multi-Cloud Optimization
   Enterprises operate across cloud, hybrid, and on-premises environments. Optimization helps to bridge these environments, using automation to balance cost, performance, and compliance.
5. Unstructured Data Optimization
   Unstructured data, like documents, emails, videos, and chats, make up the majority of what’s created and stored today. Modern data storage optimization offerings (such as Pentaho Data Optimizer) can automatically discover, tag, and manage unstructured data, unlocking hidden value for core analytics needs and AI workloads while also saving money.

A Framework for Deploying Modern Data Storage Optimization

Many mid-to-large organizations are overspending in the millions to store old/stale/unused/duplicate data in the wrong tiers and locations. To capture the immediate and significant cost savings data storage optimization offers, you need a robust framework.

• Discover & classify data: Automatically inventory structured and unstructured data across all your environments (cloud, on-prem, hybrid)
• Profile usage and access patterns: Analyze how data is used and by whom to inform tiering and retention decisions.
• Apply policies: Enforce cost, performance, and compliance policies with automation.
• Tier, archive, and optimize storage: Move data to the right tier or archive based on business value and regulatory requirements.
• Monitor continuously: Regular reports help to track storage usage, data movement, and cost savings.
• Optimize for AI and analytics workloads: Doing all the above sets AI workloads up for success since there will be more high-quality, governed data readily available.

How Data Storage Optimization Supports AI Goals

In addition to supporting core workloads and regulatory compliance needs, data storage optimization can be a pillar in making AI a success. AI workloads are data-intensive, requiring trusted, high-quality data that’s readily available. Keeping all the massive data sets used for AI model training and inference in high-cost storage tiers drives up costs and risks. Data storage optimization keeps only relevant, governed data in high-value storage, reducing risk and supporting compliance. And with automated lifecycle management and tiering, organizations can fuel AI initiatives with the most appropriate data while controlling costs and maintaining agility

Why Traditional Data Storage Optimization Approaches Aren’t Enough

While there are many ways data teams use to optimize storage today, many of those are based on tools that are overly specialized to one environment or require manual oversite and management that can’t keep up with the volumes and complexity of data flowing into the organization.  These include approaches like:

• Native platform tools (cloud-native / storage-specific)
• Point tools (archiving, tiering, dedupe, etc.)
• Enterprise automation platforms
• Services-led optimization programs

While these can provide some value, they only solve one part of the challenge and are limited in their ability to provide an ongoing, holistic and scalable solution.

Choosing A Data Storage Optimization Approach

When evaluating approaches, consider:

• Scalability and enterprise readiness: Can the solution handle your data volumes and complexity?
• Hybrid and cloud support: Does it optimize across all environments?
• Governance and compliance: Are policies automated and auditable?
• Automation and policy management: Is manual intervention minimized?
• AI and analytics integration: Does it support your data-driven initiatives?
• Services and support: Is expert guidance available?

Leveraging a unified, automation-driven platform like Pentaho Data Optmizer that supports intelligent data tiering, policy-based management that actually moves data, and seamless integration with analytics and AI pipelines is the best approach for modern data challenges.

If you’re reassessing how data storage impacts cost, performance, and AI initiatives, it can help to step back and evaluate where optimization opportunities actually exist. Talk to a Pentaho data expert.

Market Approaches Comparison

Enterprise Data Storage Optimization with Pentaho

Pentaho Data Optimizer is the living embodiment of modern data storage optimization. With intelligent data discovery, categorization of structured and unstructured data across any data system, and policy-driven automation that actually moves data based on your business rules and regulatory requirements, Pentaho Data Optimizer delivers efficiency, cost savings and modern processes that increase data’s value and use.

Data Storage Optimization FAQs