Disorganized, duplicate, and disconnected data systems are a reality for many organizations. And while the symptoms vary – missed customer communications, inaccurate reporting, inefficiencies across departments – the root cause often traces back to a lack of integration, governance, and strategy around data collection.
In a recent survey, respondents shared some of the most common and persistent challenges they face with managing data: redundancies, manual workarounds, poor system communication, and lack of visibility. The result? Lost time, rising costs, and difficulty making strategic decisions based on trustworthy information.
This blog is a recap of our recent webinar presentation, which explored key takeaways from that survey and shared practical strategies organizations can implement to clean up their data practices, eliminate redundancies, and create a more secure, scalable infrastructure.
You can watch the full webinar on-demand here.
The Problem with Redundant Systems
One of the clearest indicators of data inefficiency is redundancy. In many cases, different teams within an organization operate their own independent systems: marketing might track leads in one platform, while sales, onboarding, support, and operations all work from different sources of data. These systems may not talk to each other, or if they do, the integrations are often brittle, breaking with even small errors like a misspelled email address or missing phone number.
Redundancy can feel necessary in the short term, especially when departments operate autonomously or have specific technical needs. But long term, it becomes a liability. When customer data is duplicated across platforms, it increases the chances of inconsistent records, makes reporting unreliable, and forces internal teams to spend more time reconciling information than acting on it.
Worse, redundancy leads to a breakdown in the customer experience. For example, if a customer fills out a form on your website, and that data doesn’t flow cleanly to your CRM or onboarding tools, you risk delays in follow-up or confusion in communication. Even small inefficiencies compound over time, affecting customer satisfaction and internal productivity.
Why Data Silos Are a Security Risk
While disjointed systems create inefficiencies, they also introduce serious security and compliance risks. Many organizations operate with dozens of isolated data silos – i.e., legacy systems, spreadsheets, and cloud tools – without a clear picture of what data exists, where it’s stored, how sensitive it is, or who has access to it.
Without a centralized governance model, it becomes impossible to apply consistent data policies or understand how information flows across departments. This lack of visibility poses a major obstacle to security. For example, if a team is collecting sensitive personal data and storing it in an informal or unsupported system, that data could be exposed without anyone realizing it.
A strong data governance strategy can help break down these silos. When policies are applied consistently across a smaller number of systems, it’s easier to enforce access controls, meet compliance requirements, and reduce the risk of retaining outdated or unnecessary information. Consolidating your tech stack also brings practical benefits: lower licensing costs, less overhead to maintain, and clearer accountability for data usage.
Common Causes of Data Chaos
Several contributing factors often signal a lack of a centralized source of truth:
- Legacy platforms that lack API access or modern integration options
- Homegrown systems that are difficult to scale or maintain
- Spreadsheets and manual processes shared across teams
- Disconnected data lakes and CRMs, with no unified data model
While none of these tools are inherently problematic, relying on them without an overarching integration strategy leads to disorganization, miscommunication, and wasted effort.
One important principle to keep in mind: technology alone can’t fix a broken process. Even with powerful tools in place, the underlying workflows must be thoughtfully designed to support data quality, reduce duplication, and ensure that information moves securely and efficiently from point of entry through to system of record.
How Organizations Are Making Progress
Despite these challenges, many organizations are making important strides toward better data management. Some of the most effective improvements include:
- Prioritizing data hygiene at the start of major system projects. Organizations adopting tools like Salesforce are beginning with a thorough audit of what data is actually relevant and avoiding the temptation to migrate ten years of outdated records, instead focusing on a clean, accurate foundation.
- Collecting only what’s necessary. Teams are becoming more intentional about the data they ask for. Rather than collecting every possible field “just in case,” they align intake forms with real business needs and compliance goals, minimizing exposure to sensitive or unnecessary information.
- Implementing data minimization and expiration strategies. Rather than holding onto data indefinitely, organizations are developing policies to purge stale or outdated information. This reduces storage costs and strengthens compliance with emerging data privacy laws.
- Investing in platforms that support clean, conditional data collection. Tools that allow users to prefill, pause, resume, and securely submit information help ensure that the data collected is complete, accurate, and actionable. This also improves the user experience, which is critical when collecting data from customers, students, patients, or clients.
Considering how AI will come into play. AI is only as effective as the data it relies on, which makes data quality critically important. Without accurate, well-structured information, AI can’t generate meaningful insights. Organizations need a clear data strategy from the start to ensure their systems are capturing the right information, enabling both business success and future AI initiatives.
The Role of Secure, Centralized Data Collection
One of the most effective ways to combat redundancy, reduce silos, and increase control is to standardize the way data enters your systems in the first place. By consolidating data collection through a single, flexible platform, you create a controlled entry point – one that can validate inputs, enforce conditional logic, route data to multiple systems, and support secure compliance from the start.
This is where FormAssembly plays a pivotal role. With FormAssembly, organizations can:
- Collect data through branded, compliant web forms
- Integrate seamlessly with systems like Salesforce
- Use conditional logic to collect only the data that’s needed
- Prefill forms using verified data sources
- Enable respondents to save and resume long forms securely
- Route different parts of a form to different systems or teams
- Apply data governance and retention rules automatically
Whether you’re collecting financial data, healthcare records, admissions applications, or lead generation data, FormAssembly makes it easier to standardize and secure your intake process, which is a foundational step toward improving data quality and eliminating system sprawl.
Final Thoughts
Fragmented systems, inconsistent records, and disorganized data collection processes can slow down your business, frustrate your users, and introduce security risks. But the solution isn’t more tools – it’s smarter systems, better processes, and clear data governance from the beginning.
Organizations that prioritize clean, minimal, and secure data collection – and consolidate their systems where possible – are better positioned to reduce risk, increase efficiency, and unlock more value from the data they already have. Whether you’re building toward AI capabilities or simply trying to streamline day-to-day operations, the journey starts with high-quality, trustworthy data.
Want to learn more?
Download the Data Collection in 2025 report to explore how businesses are solving the data challenge through smarter automation.
