Creating data governance policies that protect an organization against fraud, waste, and cyber risks while complying with regulations has become an increasingly complex challenge. In this landscape, manual governance and reporting processes can introduce many data compliance risks. For example, organizations that provide their staff with training sessions on data use rules could be severely fined or sanctioned if one staff member violates their sector’s data usage regulations. Similarly, noncompliant financial reporting could cause an organization to lose funding or be fined. Automating governance by doing things like limiting data access, labeling data with usage requirements, or automating the process of financial reporting via guardrails and reporting workflows enables compliance across the organization.
Automated governance procedures and processes don’t just help organizations avoid missteps, they also empower them to realize cost efficiencies and productivity gains by reducing the time it takes to navigate regulatory complexity, decreasing their risks, and increasing compliance. But effectively automating data governance procedures is a comprehensive process that begins by conducting a landscape assessment, envisioning the future state, and creating a roadmap to maturity, before building automation frameworks and supporting them through a change management program.
The first step to automating governance in any organization is doing a landscape assessment to gain a deep understanding of the organization and its regulatory environment from both business and technology perspectives. A comprehensive landscape assessment includes quantifying existing and emerging regulatory requirements and the organization’s current procedures, vulnerabilities, and processes for meeting them. The assessment process can consist of evaluating the organization’s internal policies or technology, conducting a data impact assessment, creating a privacy assessment, or interviewing teams involved in ensuring compliance.
For example, if a financial institution was looking to automate its fraud detection and reporting processes, people conducting a landscape assessment would look at general fraud reporting guidelines, fraud guidelines specific to financial institutions, and internal policies. They would then analyze the people, processes, and technology currently involved in detecting and reporting fraud at that organization.
The next step in the process involves envisioning the desired future state of data governance. This step might include things like looking at the current and emerging regulations the organization is expected to adhere to, the areas where it has been falling short, the risks of noncompliance, and the current time and cost to enable compliance. Developing the future state entails imagining what new governance policies, procedures, technologies, and change management interventions would automate and simplify compliance. For some organizations, envisioning the future state might involve creating or updating the organization’s governance framework to be in line with emerging regulations or internal policy changes. In this phase, organizations should clearly lay out the requirements and capabilities needed to achieve the ideal automated state from the perspectives of people, processes, and technologies. A gap analysis can then be developed to highlight what’s needed to bridge the difference between the current situation and the ideal automated state.
For instance, a data governance automation strategy would involve envisioning future policies and standards to ensure that, at all stages of the data life cycle, data is stored safely, handled properly, remains trustworthy, and doesn’t get misused. The strategy could include leveraging technology to write rules-based code in enterprise data apps to prevent data misuse, creating access and identity management program guidelines to limit data access, or automating data destruction for data that the organization is only entitled to keep for a limited time.
Governance transformation is often best completed in stages due to the complexity of implementing new technologies, the challenges of transitioning from old technology and processes, and the reality of budgetary, time, and capacity limitations. It’s critical to create a data maturity model for staged implementation of automated governance processes to bridge past and future stages and help guide the organization through each step. Depending on the complexity of the transformation and scope of the automation project, the maturity model could showcase an iterative implementation process that takes as little as a few months to many years to complete.
For example, an organization might stage the rollout of their governance automation transformation by focusing first on creating new policies, followed by implementing new technologies, then developing new procedures, and finally, implementing change management programs.
Building a data automation framework doesn’t just involve implementing the automation itself but also coming up with a strategy that maximizes the efficiency and effectiveness of the transformation. The focus should be on finding the right technology mix to streamline and automate governance that leverages easy-to-use tools to simplify the process. This technology can be deployed in concert with existing tools the organization uses or via newly adopted ones.
Guidehouse works with a number of different tools to come up with the right mix of technologies for each client to help speed implementation and reduce costs. What’s right or recommended for one client isn’t necessarily ideal for another.
No matter what process you’re looking to automate, people are at the center of it. As new policies, procedures, and technologies are implemented, it’s critical to support existing staff to build their capabilities around those advances. While automation strategies that make new processes as easy to adopt as possible are important, compliance can still break down if staff members are not properly trained.
For example, organizations, such as utilities, that are liable for large fines for not removing access to sensitive information or critical controls to employees as soon as they leave the organization, can automate an identity and access management process to remove access when employment ends. But the system is still vulnerable if a human resource employee forgets to immediately record a person who’s terminated in the organization’s automated workforce management system. Effective change management can address such risks.
Leveraging intelligent automation in governance policies provides a wrap-around service that contains guardrails and gates to enforce compliance in a way that minimizes friction and complexity. If a task is easy, it’s more likely to get done. Those that adopt automation technologies to implement governance solutions will enjoy significant benefits, including greater standardization, compliance, efficiencies, productivity, and risk reduction.
Guidehouse is uniquely positioned to help both public and corporate organizations leverage these benefits. With deep capabilities around automation, industry-specific risk management and compliance, and common areas of compliance concerns like grant reporting, data governance, privacy, cybersecurity, regulatory compliance, fraud, and waste, Guidehouse can provide holistic, automated governance solutions customized to the specific needs and concerns of each organization.
This article was co-written by Bassel Haidar, Parth Kapor, and Ranyah Salous.
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