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  • Writer's pictureBrad Hoyt

Overcoming the Data Paradox in Highly Regulated Industries

Data has quickly become one of an organization’s greatest assets. The ability to make predictions and decisions at scale based on unprecedented amounts of unbiased information has exacerbated the competitiveness of the corporate landscape. But with the innumerable benefits of quantified information come novel challenges.

Organizations are faced with consequential decisions about how to collect, organize, store, and analyze data to get the most value for their company. As technology continues to evolve and our capabilities expand, there is an ever present pressure to evaluate the ongoing merit of those choices and either adapt within the frameworks that have already been established, or undergo enormous digital transformations.

Many companies opt for the former; making adjustments within the legacy models that they had committed to in a past technological environment. While that choice saves money upfront, it contributes to growing technical debt and cumulative long term costs while failing to address the underlying limitations of legacy systems.

Temporary workarounds that negate the inherent limitations of legacy technology are especially common among enterprises operating within highly regulated industries.

Highly regulated industries, like health insurance, financial services, and public utilities, have an exceptional responsibility to manage data in ethical ways that prioritize privacy. In order to prevent personal information from leaking, there is extensive legislation mandating the minimum security measures each organization must adhere to. The Health Insurance Portability and Accountability Act (HIPAA), for example, was created to protect confidential data as new insurance practices and systems of transfer were being implemented.¹ As technological capabilities and practices continue to evolve, new industry-specific regulations will continue to be enforced in order to protect sensitive information, driving organizations to continue locking data away in secure legacy systems.

At the same time, the ability to extract, analyze, and utilize data is what makes it worthwhile. Value is derived when systems are capable of real time data integration from heterogeneous systems.² In other words, liberating data is the only way to gain a holistic understanding of an organization’s collective information in order to make informed evidence-based decisions.

The workarounds enterprises often employ in an attempt to overcome the paradox of data utility versus security present several issues.

Traditional ETL tools can take months to make data available and draw insights, at which point the data may be stale and any resulting insights are rendered virtually irrelevant, while legacy technology like ERP systems forces companies to use the ERP system’s tools to reuse data, locking them into costly and often limited proprietary solutions and preventing them from truly optimizing data.

The practice that is especially evident in highly regulated industries - making copies of data - simply creates more technical debt without solving the underlying issue. The process is opaque, with no way of knowing if all the relevant data has been transferred and no way of identifying discrepancies until it’s too late to resolve them.

Consideration must be given to how data streams are created, how they’re processed, analyzed, and aggregated, and to how insights are delivered and visualized.¹ What’s needed is the ability to combine streams of data from multiple sources and show results in near real time, without compromising security or privacy, and ensuring lineage.

Data liberation, as we call it, is imperative. That’s why we built Quest. The Quest framework facilitates data movement from legacy systems into the cloud, ensures its integrity, and allows the application of machine learning models to present the results in near real time, all without sacrificing security. The framework can facilitate updating the data back into the legacy source or making it available to other systems, circumventing a significant amount of redundant data processing.

Quest facilitates a birds eye view of data streaming, allowing organizations to watch as independent data streams are combined into related groupings, and monitor their processing. If anomalies arise, they are not only visible, but trigger an alert to the enterprise’s dashboard to be assessed before it’s too late.

When alerts are triggered and changes need to be made, only those who are authorized can do so directly from the dashboard. Quest monitors who (or what, in the cases where algorithms make modifications) has adjusted the data for full transparency and accountability. These features are especially impactful for organizations operating in highly regulated industries who need to be able to tailor the framework to maintain integrity given the particular considerations and regulations of their industry.

The team at Stratizant has a plethora of experience navigating highly regulated industries. We developed Quest after years of watching companies fail to utilize data that has been locked away and siloed in legacy systems.

Privacy regulations make the extraction and utilization of data uniquely challenging. But it is nonetheless imperative to liberate data in a secure way, in order to derive value from its insights.


  1. Jones, Ben. 2018. Managing Data in Highly Regulated Industries. IT Support Guys. Web.

  2. Zaino, Jennifer. 2018. Streamlining Real-Time Data Integration and Analytics: The Struggle is Over. Dataveristy. Web.


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