Elements of a Data Governance Framework
September 8, 2020
5 Minute Read
Data-driven processes have expanded beyond the workplace to encompass almost every aspect of life. Data is no longer simply a means of performing better at work. Today, data-driven processes are at the heart of how we consume everything from grocery shopping to clothing, healthcare (telemedicine) to financial services.
Consequently, it’s imperative organizations supply these basic necessities with effective data governance. Data governance formalizes the roles, rules, and responsibilities required for organizations to minimize risk and maximize value from using data.
Effective governance is necessary to ensure regulatory compliance, data privacy, and customer satisfaction. Without it, data becomes a liability; with it, it becomes an asset. Gartner predicts that by 2022, nearly 100 percent of corporate strategies will regard information an enterprise asset.
A credible data governance framework consists of three main parts: policy, procedures, and people. By understanding how each of these aspects of a data governance model correlate, organizations can automate, accelerate, and perfect the essentials of dependable governance.
The foundation of any information governance program is the policies upon which it’s based. Those protocols are usually specified by data governance councils who assess the various forms of risk and opportunities IT resources present to organizations. Council members might include various data stewards, business representatives, IT specialists, and executives such as a chief data officer (CDO).
Common examples of policies include protecting personally identifiable information (PII) in outgoing communications or specifying where data assets can be stored. For instance, when building bots to automate processes, Automation Anywhere tools support bot development lifecycles to govern who can access the bots and where the resulting bot definitions and associated data are stored. Various regulations such as HIPPA and data privacy mandates such as GDPR can help form the rules upon which the data governance framework is founded. Policies effectually represent the objectives that governance procedures, and the people in charge of them, are trying to achieve.
Data governance procedures are the individual steps required to implement policy. For example, redacting PII is a good step for restricting access to sensitive information to ensure organizations comply with certain regulations. There are numerous governance steps and tools for carrying them out, most of which involve aspects of automation.
Typically, these tools involve identifying exactly where an organization’s information is, classifying it according to type, executing policies about suitably governing it, and managing protocols for retaining or eventually discarding it. Data governance staples like data quality, provenance, metadata management and others are all designed to help with these procedures. These procedures are critical to implement for cloud operators handling customer data. With cloud services, in particular, it’s important to follow and demonstrate implementation of the highest of industry standards. For example, for the Automation Anywhere Enterprise Cloud to achieve SOC2 and ISO 270001 certifications, not only did the necessary operations and procedures need to be in place to offer the cloud automation services, but they also were externally audited to follow the industry best practices.
Other approaches such as intelligent automation with software bots can streamline and accelerate implementing these rules. Organizations can use bots to tag documents, classify them, extract information from them, and position them where specified by governance policy.
Bots can perform these tasks and do more complex ones as well such as checking with humans to ensure classifications are accurate. They can help companies stay compliant with their data management needs.
A data governance framework is not complete without the people responsible for completing the various procedures necessary to fulfill governance policy. The personnel facet of this framework refers to the different roles required to get these different steps done. In addition to roles such as data governance councils, there are also CDOs, chief information officers, data stewards, IT teams, and others.
Many administrative roles in IT (such as DBAs) are responsible for ensuring governance protocols are followed by enforcing access control lists and other means of determining which employees are privy to which information assets. These different positions within this framework are essential to realizing the benefits of data governance because they formalize whose job it is to fulfill specific governance tasks. At Automation Anywhere, the Enterprise Cloud is operated by a cloud operations (CloudOps) team and secured by a separate security operations (SecOps) team. All personnel who have access to underlying cloud infrastructure have fine-grained permissions that provide least-privileged roles and are governed under the SOC 2 policies and procedures.
Mandatory for success
Data governance is a critical requisite for making data-driven processes payoff. Its advantages include producing dependable, trustworthy data that’s a solid basis for analytics, understanding and improving customer relations, and using data as a means to increase profitability.
Other benefits of data governance include reducing risk, maintaining regulatory compliance, and delivering data privacy to the valued customers without which no enterprise can function. Governance is mandatory for success for businesses in today’s data-centric world. Using the Automation Anywhere Enterprise A2019 Control Room enables governance for process automation initiatives as organizations start out on the RPA journey and as they grow and scale these efforts across departments.
Put Automation to Work for Data Governance.
About Ken Ross
Ken Ross is Senior Director, Product Management, at Automation Anywhere.Subscribe via EmailView All Posts LinkedIn