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What is Data Optimization? What are the benefits of data optimization? What are the key techniques of data optimization? How can organizations implement data optimization? How can AWS support your data optimization efforts?

What is Data Optimization?

Data optimization is the process of improving data quality to maximize its usefulness for its intended purpose. Modern organizations collect data from thousands of sources for AI, analytics, and data-driven decision-making. Data optimization involves sorting and cleaning data to remove redundancies, inconsistencies, and other errors. It ensures the information is relevant, meaningful, and comprehensive for high-quality analytics.

What are the benefits of data optimization?

Data optimization is important for analytics accuracy, resource efficiency, and cost reductions.

Efficient resource usage

Working with data requires storage, compute, and memory resources. When data optimization techniques are applied, the underlying data requires less storage space and fewer resources for data processing. Costs are effectively reduced for added efficiency.

Advanced analytics capabilities

Advanced analytics, which includes machine learning (ML) and artificial intelligence (AI), requires data insights beyond traditional business intelligence (BI). The high-quality, highly accessible, and organized data produced when optimizing data makes it possible to implement more advanced data analysis technologies.

Maximizes data utilization

Data optimization maximizes utilization by improving data accessibility, usability, and efficiencies within the data. Removing redundancies, inconsistencies, and errors contributes to improvements in data utilization, extending the data’s internal and external use cases.

Supports cost-effective scalability

Cost-effective data scalability involves increasing the data volume without meaningfully impacting the resources required to accommodate that larger data volume. By implementing data optimization, not only are storage and processing costs minimized, but storage and compute resources are also minimized as big data volume grows. Cloud-based technologies such as on-demand compute and right-sized instances can further minimize costs in big data processing.

What are the key techniques of data optimization?

Data optimization refers to many different optimization strategies, each of which can be combined for greater efficiency.

Storage optimization

Storage optimization is a crucial technique for data optimization, as it can significantly impact efficiency, cost, and performance. Techniques used in optimizing data storage include indexing, block storage, and tiered storage.

Indexing

Indexing uses metadata to enable faster data retrieval, thereby reducing data query times.

Block storage

Block storage divides raw data into blocks of equal size, which can be stored across media for maximum retrieval efficiency.

Tiered storage

Tiered storage distributes data across multiple storage types based on specific rules and processes to optimize big data efficiency. For example, frequently accessed data may be stored on high-performance solid state drives (SSD), while less frequently used data can be stored on slower, less expensive storage such as hard drives (HDD).

Quality optimization

Quality optimization involves verifying data consistency, identifying errors, and ensuring it is up-to-date. Numerous sophisticated data quality tools are available to assist in the optimization process. Techniques for optimizing data quality include standardization, deduplication, and validation.

Maximizing data quality involves:

  • Consolidating data from various sources and formats into a standardized form, utilizing data transformation.
  • Ensuring there are no duplicates in the dataset.
  • Ensuring the data is complete and in the correct format, either by deleting incomplete data or infilling it to achieve completeness.

For example, a phone number should consist of 10 digits and no other characters.

Processing optimization

Processing optimization techniques include parallel processing, efficiency optimization algorithms, and caching strategies.

Parallel processing distributes data processing tasks across multiple CPUs, dramatically reducing compute time. Rather than using all-purpose algorithms, algorithms tailored to specific data tasks can be employed to reduce CPU load and accelerate data processing.

The caching technique stores frequently used data in the fastest available memory to minimize retrieval times.

Query optimization

Query optimization leverages several database techniques to improve speed, efficiency, and resource utilization during data retrieval. Query optimization techniques include the following, depending on the type of database:

  • Indexing uses metadata to enable faster retrieval
  • Selective filtering fetches only the necessary rows from the database
  • Column projection fetches only the necessary columns from the database
  • Query caching stores frequently used queries in fast memory
  • Parallel querying distributes query tasks among multiple CPUs
  • Partitioning divides large database tables into smaller, query-specific tables

Governance optimization

Data governance optimization ensures data meets all security and regulatory requirements in an efficient manner. This type of data optimization begins with establishing scalable and secure policies, processes, and frameworks for compliance.

Data governance optimization can involve the following tools and techniques:

  • Automated compliance tools to enforce compliance with regulations
  • Data lifecycle management automation to automate data creation, retention, archiving, and deletion
  • Data quality frameworks to conduct automatic data quality checks
  • Role-based access control (RBAC) to limit access to authorized users
  • Centralized governance platforms for data management policies and processes
  • Training and awareness programs to educate stakeholders on policies and best practices

How can organizations implement data optimization?

The data optimization process requires strategic planning, adherence to internal policies, and ongoing refinement.

Before implementing data optimization techniques, organizations should assess their current data, processes, and technologies. From here, you determine new goals and key performance indicators (KPIs) to identify which data optimization techniques are appropriate and how to prove measurable results.

Establish data governance

Developing a data governance framework is the first step in data optimization. Data governance encompasses the processes and policies that ensure data is in a suitable condition to support business initiatives and operations. Data governance determines roles, responsibilities, and standards for data usage.

By optimizing data with a data governance framework, organizations can benefit from increased usability, scalability, risk mitigation, stakeholder alignment, and compliance.

Implement data-as-a-product approach

A data-as-a-product (DaaP) approach treats internal data with the same management techniques as a business product. DaaP includes a clear product owner, defined responsibilities, approved data vendors, established standards, established design patterns, comprehensive documentation, well-defined data sets and digital records, and robust governance structures across the data lifecycle.

This systematic data management approach delivers data optimization via high-quality, easily accessible DaaP for both internal and external users.

Set up data cataloging

A data catalog inventories all the data an organization collects and processes, storing it in one place: the data catalog. Setting up a data catalog can assist in data optimization by making data more easily accessible and discoverable.

Setting up data cataloging reduces data redundancy, facilitates collaboration, enhances scalability, and enables automation. When metadata standards are enforced, the data catalog will also improve data quality.

Integrate your data

One of the key tenets of data optimization is accessibility. The fastest, easiest, and most cost-effective way to make any data accessible is by moving it to a centralized location in the cloud. Migrating to a cloud-based data lakehouse combines the flexibility of a data lake with the data analysis capabilities of a data warehouse.

The benefits of data lakehouses include unified data access, scalability, enhanced collaboration, interoperability, and efficient resource usage.

Set up automation

To assist in implementing data optimization techniques, almost every aspect of the optimization process can be automated with the right data optimization tool. Selecting the right data optimization tool depends on your specific use case and the optimization technique you need.

Automation tools are available for data optimization across integration and ETL, data quality and cleaning, governance and cataloging, storage and compression, data processing, workflow automation and orchestration, and database and query optimization.

Ensure scalability

Using scalable resources ensures organizations gain the resourcing efficiency benefits of many data optimization techniques. Cloud-based data storage, processing, and analytics can help enforce scalability in data optimization, with right-sized instances and on-demand processing.

How can AWS support your data optimization efforts?

Analytics on AWS offers a comprehensive set of capabilities for every data optimization requirement. From optimizing data processing and SQL data analysis to streaming, search, and business intelligence, AWS delivers unmatched price performance and scalability with governance built in. Choose purpose-built services optimized for specific workloads or streamline, manage, and optimize your data and AI workflows with Amazon SageMaker.

For example, you can use:

Get started with data optimization on AWS by creating a free account today.