The world of business today is undergoing rapid digital transformation for zero trust, with a focus on reducing the number of applications and introducing common interfaces across large scale systems like product lifecycle management, enterprise resource planning and human resources. This is driven by the need to eliminate technical debt and simplify processes. However, there are challenges that need to be addressed in order to achieve these goals. One such challenge is resistance from system integrators and analysts to embedding compliance data science into these initiatives.

Compliance requirements are an essential aspect of business operations, but they can also lead to process debt. This is because applications that do not meet these requirements often require manual processes to satisfy them, leading to multiple ways of solving the same problem. This results in increased variance and risk in not having a standardized solution for data tagging and compliance.

To address this conundrum, it is important to understand the value of compliance data science and its role in reducing process debt. By identifying process debt during application assessments, agencies can score and analyze these processes, allowing them to adopt best practices and best-of-breed solutions. This can help the agency to pivot towards common approaches until the application is refined and automated, reducing variance and risk in the process.

However, system integrators who have been with the agency for many years may be hesitant to embrace these changes, often citing the user community as the reason for resistance to change. In reality, it may be these integrators who are contributing to the errant culture, as they have never promoted a single integrated solution for data tagging and compliance due to a lack of understanding of the importance of compliance data science and its role in common interfaces, databases and attribution standards.

To overcome this resistance, it is important to educate system integrators and analysts about the value of compliance data science and its role in reducing process debt. This can be achieved through the use of process debt as a cost figure in a return on investment, which will allow applications to gain the funds required to embed compliance data science. By using process debt as a measure of cost, businesses can calculate the potential savings that can be achieved through short-term process alignment and eventual application automation, reducing process debt and improving the overall efficiency of the agency through an application assessment process driven by one or several compliance requirements.

The ultimate goal of this approach is to change the culture of the agency and encourage the adoption of a more integrated solution for data tagging and compliance. This can be achieved by simplifying processes and reducing the manual burden on users, allowing the system to do the work for them. By changing the culture in this way, agencies can leverage the full potential of zero trust initiatives and achieve the goal of reducing technical debt while introducing common interfaces across large scale systems with adaptive zero trust policy architecture.

Understanding the value of compliance data science and using process debt as a cost figure, agencies will overcome resistance to change and improve the efficiency of their operations. This will lead to the adoption of best practices, best-of-breed solutions, and ultimately, a more integrated and automated approach to data tagging and compliance, reducing variance and risk while improving the overall efficiency of the agency.

Dave Harris is cofounder and partner at TCEngine.

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