What is data optimization?

Data optimization is the process of improving the organization and quality of datasets to ensure efficient data storage, processing and analysis by enterprises and other entities.

Data optimization encompasses a broad range of data management techniques. It includes strategies for streamlining data cleaning, storage, transformation and processing, alongside those for optimizing queries. By successfully optimizing data, organizations can experience more informed decision-making, establish more cost-effective business operations and support scalable artificial intelligence (AI) initiatives.

As enterprises increasingly focus on optimizing their data estates, many are deploying AI-driven solutions to enhance data optimization processes. These solutions include AI-powered data cleaning tools, data governance and observability software, hybrid cloud storage solutions and data lakehouse platforms.

While access to high-quality and relevant data has always been important for reliable analytics and better decision-making, it takes on additional urgency in the modern data landscape. The reasons are threefold: data volume, complexity and AI-related competitive pressure.

Organizations today contend with data volumes that are orders of magnitude greater than what was available through most of human history: One 2024 global study of organizations of varying sizes found that nearly two-thirds managed at least one petabyte of data.¹

Much of that data is big data: massive datasets in various formats, including structured, semi-structured and unstructured data. Unstructured data, notably, doesn’t easily conform to the fixed schemas of relational databases, meaning conventional tools and methods typically can’t be used for unstructured data processing and analysis.

At the same time, enterprises are under pressure to harness AI-ready data—high-quality, accessible and trusted information that organizations can confidently use for artificial intelligence training and initiatives.

But most companies don’t have AI-ready data yet: According to a 2024 survey from the IBM Institute for Business Value, only 29% of technology leaders strongly agree that their enterprise data meets key standards for efficiently scaling generative AI.²

Deriving value from massive and complex datasets while also ensuring AI readiness requires the right tools, infrastructure and data management strategies. However, enterprises usually can’t afford infinite compute and storage resources. They must balance efforts to unlock value with measures designed to maximize efficiency and return on investment.

Data optimization helps them do it.

Through data optimization, organizations can improve both the performance and the efficiency of data workflows. Various data optimization techniques help enterprises elevate the quality and accessibility of their data—while also reducing the burden that storage and processing places on their resources and budgets.

Data optimization can help organizations address challenges in their data pipelines and budgets. The benefits of data optimization include:

Data optimization techniques help improve the usability and efficiency of data workloads at key points in the data lifecycle—such as data storage, data transformation and data usage.

Data storage optimization includes reducing the storage space required for data tables and indexes. It also encompasses strategies for using different storage options to distribute data more efficiently and cost effectively.

• Reducing storage space: A common approach for reducing storage costs and space needed is compression. This process uses algorithms to encode and decode data, which decreases the bits required for its storage.

• Using tiered storage: In tiered storage, data is grouped according to access requirements. More expensive data storage options—which typically allow for faster retrieval—are reserved for frequently accessed “hot” data. Meanwhile, “cool” or “cold” data—data that is used less often—resides in storage environments that are less expensive and require more time for data access.

• Choosing data storage architecture: In addition to using storage tiers, organization can also choose one or more storage methods to optimize speed, cost savings and other goals. The three main types of storage systems are object storage, file storage and block storage, each with different strengths and drawbacks.

Significant data quality improvement occurs during successfully executed data transformation and data cleaning processes.

Data transformation is the conversion of raw data into a unified format and structure. The first step of data transformation is data cleaning. Also called data cleansing or data scrubbing, this is the identification and correction of errors and inconsistencies in datasets.

Key data cleaning techniques include:

• Standardization: When data is represented in different structures and formats within the same dataset, resulting inconsistencies can make it harder to use. Standardizing data structures and formats can help ensure uniformity and compatibility for accurate analysis.

• Data deduplication: Duplicate or redundant data can distort analysis. Data deduplication eliminates duplicate records (such as those created by data integration problems, manual entry errors or system glitches). In addition to improving data quality, data deduplication can also lower costs and resource utilization as less compute and storage is expended on duplicate records.

• Addressing missing values: Missing values can also distort data analysis. Tactics deployed by data professionals to address such gaps include replacing missing values with estimated data or removing incomplete entries.

• Data validation: Data validation is the process of verifying that data is clean, accurate and ready for use. It entails the establishment and enforcement of business rules and data validation checks, including checks on consistency, data type, format, range and uniqueness.

To address poor data quality in AI model training, researchers often turn to additional measures for improving the quality of training datasets, including data augmentation and synthetic data generation.

Metadata management is the organization and use of metadata to improve the accessibility and quality of data.

Examples of metadata include:

• Descriptive metadata: Includes basic information, such as titles and keywords. This type of metadata helps organizations improve the searchability and discoverability of their data in catalogs, social media platforms and search engines.

• Administrative metadata: Encompasses ownership, permissions and retention policies. This type of metadata helps organizations comply with legal, regulatory and internal policies.

• Preservation metadata: Ensures the long-term usability and accessibility of data. This type of metadata helps organizations meet extended data-retention requirements, especially in industries where records must remain accessible for compliance.

Query optimization speeds the execution of queries (the retrieval and manipulation of data) in SQL and NoSQL databases while minimizing the use of resources such as memory and CPU. While query optimization techniques vary depending on the type of database, common ones include:

• Filtering: Ensure the system is not scanning data irrelevant to queries.

• Adding an index: Indexes can pre-sort information to power more intelligent searches.

• Caching: Caching results of repetitive queries reduces the need for new computation each time the query recurs.

• Partitioning: During database design, databases can be broken into smaller segments for faster, more targeted queries.

Choosing the right, fit-for-purpose query engine can also be a key component of query optimization—that’s because different engines may be better suited to different data workloads. For example, Presto C++ can be used for high-performance, low-latency queries on large datasets, while Spark works well for complex, distributed tasks.

Other techniques deployed for data optimization include parallel processing (breaking down data processing tasks into smaller parts to be performed simultaneously on multiple processors); rules-based access control, or RBAC (limiting access to sensitive data, which helps prevent accidental data loss and intentional data breaches); and data visualization (the graphical representation of data to aid in data analysis).

Data optimization can be considered a component of data management, or it can be viewed as a complementary practice. Ultimately, what matters is that data optimization enables more effective data management by improving the quality and accessibility of the data being managed.

Data governance is a data management discipline that helps ensure data integrity and data security by defining and implementing policies, quality standards and procedures for data collection, ownership, storage, processing and use. As such, it can support various data optimization techniques.

For example, an organization’s data governance program may establish data quality metrics to measure progress toward improving data quality and set data retention policies that help optimize data storage.

Tools for data optimization range from targeted solutions to comprehensive platforms, typically featuring AI-powered components that reduce manual processes and support operational efficiency.

AI-powered data cleaning tools can automatically identify patterns, anomalies and inconsistencies in source data. Rule-based or learned AI models can also consolidate or eliminate duplicates by deciding which record should “survive” based on accuracy, recency or reliability. AI models can automate the creation and enforcement of data cleaning rules by learning from historical corrections and user feedback.

Data observability tools enable automated monitoring, triage alerting, root cause analysis, data lineage and service level agreement (SLA) tracking, which helps practitioners understand end-to-end data quality. Such tools allow teams to detect issues such as missing values, duplicate records or inconsistent formats early on before they affect downstream dependencies, leading to faster troubleshooting and issue resolution.

Data governance tools help enterprises enforce the policies set through data governance programs, including policies supporting data optimization. Common capabilities of data governance solutions include the automatic discovery and classification of data, the enforcement of data protection rules and role-based access controls, and features to support data privacy and compliance requirements.

Hybrid cloud solutions offer a “mix-and-match” approach to data storage, with public cloud platforms, private cloud environments and on-premises infrastructure available to help organizations store data in a flexible, scalable and cost-optimized manner.

Organizations can choose the best, most cost-effective storage option to meet their business needs and transfer data workloads as necessary. Hybrid multicloud approaches offer additional flexibility, as enterprises can use services from more than one cloud provider.

A data lakehouse is a data platform that combines the flexible data storage of data lakes with the high-performance analytics capabilities of data warehouses. Data lakehouses use cloud object storage for fast, low-cost storage across a broad range of data types.

Additionally, their hybrid architecture eliminates the need to maintain multiple data storage systems, making them less expensive to operate. Features of leading solutions include multiple query engines for efficient query execution and integrated capabilities for data governance, data cleaning and observability.

Data optimization strategies and tools can improve efficiency and performance in a range of fields and industries.

• Internet of Things (IoT) networks: Compressing enormous amounts of data collected by sensors in IoT networks can enable more efficient cloud storage.⁵

• Customer relationship management (CRM): Data cleaning and deduplication in CRM systems can help improve lead management, sales forecasting and managing customer communications.

• Autonomous vehicles: Filtering images collected for autonomous vehicle model training can ensure training data includes the most valuable images while also accelerating the speed of training.⁶

• Sporting events: Data visualization can help inform and accelerate data-driven storytelling and content creation during live sporting events.⁷