10 Tips for Optimizing Data Infrastructure

A business’ stockpile of data can be a gold mine. When used right, that data can fuel analytics that help the company run more efficiently, avoid missteps, and take advantage of opportunities, including generative AI, which needs a flow of clean, well-organized data to do its work. To harness the possibilities of all that data, however, an organization must put the right strategies in place and optimize its data infrastructure.

What Is Data Infrastructure?

A data infrastructure is the ecosystem of technology, processes, and people responsible for an organization’s data—including its collection, storage, maintenance, and distribution. The technology component of the infrastructure includes on-premises hardware, such as servers and storage devices, and software, including OLTP databases and data warehouses as well as networking technologies. It typically also includes various cloud services. The people involved include application developers, database administrators, data analysts, and data scientists.

A key goal of a data infrastructure is to provide a secure storage repository as well as the computing resources for data processing and analysis. Equally important are the rules and policies that govern how data is used—and who has access to it. Ultimately, the aim is to get the most value from an organization’s data with efficient management and analysis for data-driven decision-making.

Key Takeaways

• A data infrastructure is the collection of technologies and policies that an organization uses to help ensure proper and efficient use of its data.
• The technology components of a data infrastructure include on-premises hardware, such as servers and networks; software, including operating systems, databases, and tools for data analytics; and various cloud services.
• The chief goals of any data infrastructure strategy are security, privacy, and regulatory compliance.
• One way to simplify and update a data infrastructure is to offload more workloads to modern cloud services.

Data Infrastructure Explained

Data infrastructure consists of an organization’s physical infrastructure, including hardware components such as servers and storage devices as well as the software for storing, retrieving, sharing, and analyzing data. Key components include databases, data lakes, and data warehouses that companies use to store and analyze various data types, such as graph, spatial, text, images, JSON, and vector data, among many others.

Overlayed on these technologies are security measures that protect sensitive data from unauthorized access. Beyond these are the tools and technologies that support decision-making based on the data analysis, including dashboards, and generative AI copilots.

What Does Data Infrastructure Do?

A functional data infrastructure enables efficient data handling, analysis, and decision-making while helping to address security and compliance with regulations. Organizations with effective data infrastructures can derive value by transforming what’s often a complex mix of data types into easily understandable and actionable insights.

These insights can flow from interactive dashboards that let users explore and analyze information, ideally in real time, to identify trends, patterns, and relationships that might not be apparent from the raw data. Dashboards might include charts, graphs, heat maps, and infographics that make it easy to compare the possible results of different decisions.

An effective data infrastructure will also aim to democratize data access without compromising security. When stakeholders at different levels can collaborate and contribute to strategic decision-making, the organization benefits. In addition, a data infrastructure can feed generative AI initiatives, including intelligent automations, that can make business operations more efficient.

Why Is Data Infrastructure Important?

Effective use of data has been a vital part of business decision-making for years. When a company can easily analyze its operational data, it can more clearly see what’s working and what isn’t, make split-second decisions with accuracy, or take a longer view and see trends to exploit or avoid. Now, with the emerging possibilities of generative AI, data infrastructure is more important than ever. AI runs on data, and only with the proper data infrastructure—which should now include technologies such as retrieval-augmented generation (RAG) and vector stores—can the latest generative AI models work to their full potential.

10 Tips for Optimizing Data Infrastructure

There are many angles to consider when optimizing a data infrastructure. Here are 10 ideas to help you cover all your bases.

1. Implement data governance

Alongside hardware and software investments, data governance is an essential ingredient for unlocking the power of data. Data governance is the framework for managing and using data effectively—ensuring its accuracy, consistency, availability, and security—and aligning data-related practices with the organization’s goals and objectives.

A data governance plan should define clear roles and responsibilities for individuals involved in data management to ensure accountability. A first step is defining roles and designating data owners, data stewards, and data users, each with specific rights and responsibilities. Data governance also includes rules and guidelines for IT teams that have access to data. Policies should address topics including data security, data quality, data retention, and data sharing.

Finally, solid governance calls for conducting regular data audits and monitoring data quality metrics to promptly identify and address any issues.

2. Use automation and cloud technologies

The IT pros involved in building and maintaining a data infrastructure are good at automating tasks, often by writing scripts to automate the steps involved in provisioning, monitoring, and updating software. More recently, cloud providers have been using powerful AI and machine learning (ML) tools to help organizations automate a wider range of tasks—including provisioning, data loading, query execution, and failure handling—and achieve high query performance at scale.

On the business side, this level of performance can drive predictive analytics, which can help improve the accuracy and velocity of decision-making in areas such as finance, data security, logistics, and many others.

3. Organize data into logical groupings

It’s important for any data infrastructure to organize data into logical groupings for efficient management and transfer. There are two parts to this effort: data categorization and data classification. Categorization groups data into categories based on shared attributes, such as source or sensitivity, while classification assigns data to predefined classes based on rules or algorithms.

A product R&D document, for example, could potentially fit into multiple categories, such as “technical data” and “market research,” but will be only one classification within a specific hierarchy, such as “public,” “confidential-internal,” or “secret.”

4. Store metadata to track the origin of data

Metadata is information that describes a data asset. When you take a picture, the metadata says where and when the picture was taken, among many other possible attributes. A metadata store in a data infrastructure organizes and retains metadata about data assets, processes, and schematas within the system. Metadata stores can improve both data discoverability and data governance across hybrid environments, such as data lakehouses. Metadata stores may also help in regulatory compliance by providing information about data lineage, access control, encryption, and audit logging, which all contribute to data privacy and protection. Increasingly, generative AI systems take advantage of metadata to bring transparency and explainability to their outputs.

5. Use security protocols to protect your infrastructure

The right data infrastructure can help protect your organization’s digital assets, which in turn earns the trust of customers and stakeholders and helps comply with industry regulations.

In data security, there are several angles to consider, some technical, some social. Start by encrypting data at rest and in transit in case it’s intercepted or accessed by unauthorized personnel. Then, protect against those threats by implementing controls to restrict who can see sensitive data. That can be achieved through user authentication and role-based access control. Because threats to data security constantly evolve, regularly monitor and update protection measures, and, of course, stay up-to-date with the latest security patches and software updates. Cloud providers often will proactively patch and update software as soon as vulnerabilities are discovered.

Another line of defense is employee education. Make sure employees understand data security as part of their workday. Establish training to raise awareness about strong passwords, phishing scams, and social engineering attacks—and provide a reporting structure for suspicious activities. In the end, data breaches happen, but you can minimize their impact with protocols for steps to be taken, including containment and recovery as well as communication procedures for helping maintain the trust of your customers and stakeholders.

6. Monitor system performance regularly

It’s crucial to monitor your data infrastructure to identify potential issues before they hurt productivity. To monitor a range of infrastructure components, data engineers use software agents to collect performance data on operating systems, CPU utilization, memory usage, network traffic, and many other components. When an issue is detected that could affect users, the monitoring system can help diagnose and even fix the problem. With real-time monitoring across data centers and cloud providers, technology can even predict outages or slowdowns so they can be addressed before users ever detect them.

7. Plan for future growth scenarios

Your organization is likely generating and collecting large amounts of data. It’s prudent to plan for the pace to accelerate. How can you help ensure your data infrastructure can handle growth and adapt to changing demands?

Work to understand how your current hardware, software, and cloud services will adapt to increasing data volumes and computational demand. Know where disruptions and bottlenecks are likely to occur, and begin to design around them. This will require that you stay up-to-date on emerging technologies and their potential impact on your data management strategies. With generative AI’s growing influence, for example, you’ll want to understand how to benefit from new data types, such as vectors, and RAG.

8. Focus on scalability and flexibility

An organization’s compute needs change throughout the day, week, month, and year. Online retailers, for example, need to plan for heavy usage during the holidays, and universities need to scale up quickly during those short bursts when potentially tens of thousands of students register for classes. Using a data infrastructure with automated scale up and scale down capabilities can lower overall IT costs, especially when paying for instances in a cloud service.

Aside from choosing the right cloud provider, you can help ensure scalability with an architecture and tools designed for integration, modeling, orchestration, monitoring, and visualization. Technologies such as load balancers can distribute traffic across servers. In addition, the right database solution, either on-premises or as a database as a service offering, will employ techniques to maximize scalability, such as indexing, caching, and query optimization.

9. Prioritize speed and storage capacity

Fast data processing and ample storage capacity are the cornerstones of an efficient data architecture. The simplest, and often the least expensive, way to get there is to offload some workloads to the cloud. These can include database services and software-defined storage as a service, using a collection of virtual machines on a single cloud server to improve resource utilization.

For workloads that stay in your data center, invest in modern, high performance hardware to replace outdated equipment and improve throughput. Modern network hardware and software are important for moving data around in your data center or between your location and cloud data centers. As you upgrade, look to avoid the requirement to move data between databases for machine learning and analytics; using one cloud database service that does it all improves speed and lowers complexity.

10. Invest in training staff on new technologies

There are many moving parts in an efficient data infrastructure, including physical infrastructure, which includes storage hardware, processing hardware, and networks; information infrastructure, including business applications and data repositories; and business infrastructure, such as business intelligence systems and analytics tools. Keeping each of these elements working and secure requires skill sets that must remain up-to-date. For example, modern data systems need to consider generative AI, which can require proficiency in new data types, software tools, compute architectures, and organizational structures. Encourage staff to seek training from upskilling firms, user groups, and tech events so they can stay on top of modern data systems, learn about databases in full-stack development processes, explore data mesh architectures, and grasp the principles involved in analyzing data and presenting findings.

Tech professionals also can access training offered by cloud providers or by the community around a certain technology.

Optimize Your Data Infrastructure with Oracle HeatWave

MySQL is the world’s most popular open source database, but until now data analytics had to happen on a separate database. Now, HeatWave MySQL provides a fully managed database cloud service that combines transactions and real-time analytics, eliminating the complexity, latency, costs, and risks of ETL duplication. Further simplify your data infrastructure by using other built-in HeatWave capabilities that eliminate the need to move data to separate cloud services:

• HeatWave Lakehouse lets you query data in object storage, MySQL databases, or a combination of both with record speed.
• HeatWave GenAI provides integrated, automated, and secure generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language.
• HeatWave AutoML includes everything users need to build, train, and explain machine learning models within HeatWave at no additional cost.

HeatWave is available in Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), and Microsoft Azure.

Optimizing Data Infrastructure FAQs

What happens if your data infrastructure system is faulty?

A faulty data infrastructure can lead to several outcomes, none of them good. It can lead to slower response times for websites, applications, analytical tools, and AI systems that depend on efficient, clean data. Worse yet, faulty infrastructure systems can open vulnerabilities, putting data at risk of loss because of human error or a system crash, or data could be compromised if bad actors gain access to the faulty data infrastructure.

How do you manage data infrastructure?

You manage data infrastructure with a set of technologies and policies that help ensure data stays safe and gets to the people it’s designed to serve. Focus areas include data storage hardware, database software, and networking software and equipment that are designed to ensure data flows efficiently between internal systems and cloud service providers. Managing data infrastructure is a highly sought-after skill, especially as generative AI grows more commonplace, given that it requires a steady flow of clean data to operate.

How do I know which technologies to invest in for my data infrastructure?

Prioritize technologies that add value without adding complexity. For example, you might invest in a database that can handle transaction processing and machine learning, which can save you from time-consuming ETL processes. You might also look for a database that works natively with many different data types, such as text, spatial, graphs, JSON, and vectors. That will also help you simplify your data infrastructure.

How often should I review my data infrastructure?

Data infrastructures are often complex to assemble and maintain. It’s best to review your data infrastructure for upgrades when you want to adopt a new technology, such as machine learning or AI, or when you require new data security measures. Organizational growth or change, such as a merger or acquisition, should also trigger a review. For ongoing maintenance, ensure the data infrastructure collects logs on how well various components work, and review them regularly. Those logs will alert data experts to problems that are occurring or are on the horizon.