How to improve data accessibility for software and AI development

In fast-paced software and AI environments, accessible data is a foundational requirement. Whether you're building robust enterprise software or training complex AI models, your team’s success depends on quick, compliant, and frictionless access to high-quality data.

Unfortunately, many organizations still struggle with bottlenecks caused by siloed systems, limited access workflows, or security restrictions. The Tonic.ai product suite helps solve these challenges by offering secure, realistic data synthesis and provisioning solutions with native integrations to streamline access across environments.

What is data accessibility?

Data accessibility refers to the ability of authorized users to easily locate, retrieve, and work with the data they need. It encompasses not just the availability of data, but how usable, secure, and timely it is for the use cases at hand.

Accessible data is:

• Discoverable: Developers and analysts can easily find the data relevant to their tasks.
• Usable: It’s well-structured, current, and ready to work with.
• Secure: Access is governed by clear rules and privacy controls.
• Timely: Data is provisioned when needed—not after deadlines have passed.

Why data accessibility matters for software and AI development

Data accessibility directly impacts product quality, team velocity, and deployment success. When data is hard to access, dev cycles slow down. Testing becomes patchy. AI models are trained on stale or incomplete information.

On the flip side, when high-quality data is accessible, teams move faster and build better. As outlined in our guide to high-quality data, accessible data helps teams collaborate more effectively, avoid production issues, and ensure software development workflows meet both functional and compliance standards.

For software developers, it means fewer delays and higher confidence in releases. For AI teams, it enables faster iteration, better model performance, and more ethical outcomes.

The current state of data accessibility

In a recent benchmark study, 90.5% of participants reported that investments in data and AI would be a top organizational priority in 2025. And yet despite significant advancements in data infrastructure, many organizations continue to grapple with challenges that impede data accessibility. 

Organizational and technical barriers

A prevalent issue is the fragmentation of data across disparate systems and departments. This siloed approach not only hampers collaboration but also leads to inconsistent data formats and standards, making it difficult for teams to derive meaningful insights. Moreover, the lack of user-friendly tools means that non-technical stakeholders often rely heavily on data specialists, creating bottlenecks and slowing down decision-making processes.

The need for a cultural shift

There's a pressing need for a cultural shift within organizations. Data accessibility should be viewed not merely as a technical issue but as a strategic imperative. Empowering all employees with the tools and knowledge to access and interpret data fosters a data-driven culture, leading to more informed decision-making and a competitive edge in the market.

Benefits of more accessible data

Enhancing data accessibility yields significant advantages across various facets of an organization. By ensuring that data is readily available and usable, businesses can unlock efficiencies, drive innovation, and gain a competitive edge. Here are some immediate benefits:

Improved operational efficiency

Organizations that prioritize data accessibility experience a notable reduction in decision-making time. By streamlining data retrieval processes, companies can decrease the time to market for decisions by up to 40%, enabling more agile responses to market changes and opportunities. 

Cost reduction

Implementing centralized data platforms and improving accessibility can lead to substantial cost savings. Companies have reported up to a 50% reduction in expenses related to data retrieval and consolidation, freeing up resources for other strategic initiatives.

Accelerated development

Accessible test data allows developers to prototype and test faster. With fewer dependencies on external teams or approvals, test environments can be spun up faster, QA cycles shrink, and iteration becomes smoother. 

Unblocked AI innovation

When data scientists can easily access representative, compliant datasets, they can train and evaluate models more accurately. This reduces model bias, improves accuracy, and speeds up deployment.

Faster time-to-market

With fewer delays and dependencies, teams can release features, ship updates, and respond to market demands quickly and confidently. Accessible data shortens the feedback loop and helps teams stay agile.

Compliance and risk management

Improved access doesn’t mean increased exposure. Processes like data masking can ensure that sensitive data stays protected.

Challenges and barriers to access

Despite its importance, improving data accessibility isn’t simple. For organizations trying to democratize data access, challenges include regulatory constraints, technical complexities, and outdated organizational practices.

Data privacy regulations

According to Statista, 98% of countries in Europe had data privacy legislation in place by the end of 2024. In the United States, 20 states have adopted comprehensive data privacy laws, each with its own set of requirements and compliance standards. Stringent requirements for data handling result in cautious data sharing practices. And fear of non-compliance leads to overly restrictive access controls, limiting the availability of data for legitimate business needs.

Complex data ecosystems and siloed data sources

Data silos emerge when information is isolated within specific departments or systems, making it challenging to obtain a unified view of organizational data. This fragmentation hampers cross-functional collaboration and decision-making, as teams struggle to access and integrate data from disparate sources.

Poor quality data due to inadequate tooling

Without proper validation and transformation mechanisms, data may contain inaccuracies, inconsistencies, or redundancies. Such data quality issues not only undermine trust in data-driven workflows but also complicate data integration efforts, as teams must expend additional resources to fix data problems before meaningful work can proceed.

Inability to scale existing data

As organizations grow, their data needs evolve, requiring scalable solutions to manage increasing data volumes and complexity. However, legacy systems and processes often lack the flexibility to accommodate this growth, leading to bottlenecks in data access and processing. This restricts the organization's ability to leverage data effectively, particularly in agile development environments and when looking to leverage or develop AI solutions..

Slow data provisioning

Many organizations still rely on manual provisioning processes that can take days or weeks. This delays testing, frustrates dev teams, and increases the likelihood of using unsafe or out-of-date datasets as a workaround. Without an efficient data provisioning strategy capable of getting isolated datasets to developers on demand, organizations risk approval bottlenecks, product development slowdowns, and missed revenue opportunities. 

Best practices for improving data accessibility

Improving data accessibility means building a secure, scalable foundation that gives the right people the right data, at the right time. Here are five proven strategies to level up data accessibility across your development and AI teams:

Implement an enterprise test data strategy

The first step to accessible data is eliminating chaos. Instead of struggling with a fragmented approach, organizations should adopt a centralized test data strategy that enforces standards while giving teams flexibility.

Tonic Structural is a test data management solution with granular access controls, automated audit logging, and data generation capabilities that scale across environments. Whether you’re a small dev team or a global engineering org, Tonic Structural ensures everyone is working from a consistent, governed data foundation.

Implement solutions that work across data sources

Most enterprises operate across multiple databases, clouds, file systems, and legacy platforms. With native integrations for popular data stores like Snowflake, MongoDB, BigQuery, and more, Tonic.ai’s suite of products makes it easy to access safe, usable test data no matter your tech stack.

Safely leverage production data to generate high-quality test data

Using raw production data is risky, but you can mimic its structure and relationships safely with Tonic Structural. With advanced data masking and data synthesis techniques, you can realistically mimic edge cases, preserve referential integrity, and enable rich testing scenarios that would otherwise require real data.

Fill the gaps of inexistent data with synthetic data generated from scratch

If a lack of data is at the root of your data accessibility issues, either due to the early stage of your product’s development or due to heightened privacy concerns fully restricting access to production data, synthetic data generated from scratch can fill the void. Especially useful in greenfield product or feature development, synthetic data from scratch, like that generated by Tonic Fabricate, can be tailored to your data’s specific needs and scaled to meet your testing demands.

Implement developer data provisioning solutions

By embedding a streamlined data provisioning solution directly into your developer workflows, you ensure that data accessibility scales alongside your velocity. Tonic Ephemeral spins up isolated databases on demand to get each developer the data environment they need to avoid collisions in testing. When paired with Tonic Structural, you get the added value of fully populating your isolated databases with secure synthetic data, as well as referentially intact database subsets.

Integrate solutions into your tech stack and CI/CD pipelines

The most accessible data is data that’s already there when you need it. With CI/CD integrations and infrastructure-as-code compatibility, Tonic.ai’s products bring test data into your delivery pipeline. Secure, high-quality data can be automatically generated and deployed as part of every build.

Real-world use cases of data accessibility

Tonic.ai is helping high-growth companies like Kin Insurance remove friction from development by giving teams faster, safer access to the data they need.

Kin’s engineering team needed test data that matched the complexity of their production environment without exposing sensitive customer information. But according to co-founder Stephen Wooten, “Accessing data took so long that developers didn’t want to do it.”

Using Tonic Structural, the Kin team was able to generate realistic, de-identified datasets that preserved the business logic and edge cases critical to performance testing. As a result, developers gained immediate access to useful test data, security concerns were mitigated, and the company accelerated product velocity while maintaining compliance.

Software and AI development success relies on data accessibility

Accessible data enables innovation. Whether you’re building a next-gen AI model or shipping an enterprise feature, success depends on your ability to access usable, secure, and timely data.

With Tonic.ai’s industry-leading data synthesis and provisioning solutions, you don’t have to choose between speed and safety. We help engineering and data teams unlock access to the data they need, without risking the data they can’t.

Ready to accelerate innovation with secure, high-quality, accessible data? Connect with our team today.