Data Governance Is Ineffective Without Automation

Data governance is one of the most important undertakings for businesses today. Regulations like the GDPR and CCPA require organizations to have thorough insight and control over their data, and the costs of poor-quality information keep climbing. An effective governance strategy addresses both, but creating and implementing such a program is often easier said than done.

More than 90% of enterprise leaders plan data initiatives for the coming year, yet more than half report struggling to pull any business value from their information. Many of these companies realize the importance of data governance in achieving their goals, but their strategies frequently fall short. For many organizations, that's because they rely too heavily on manual processes.

Manually Data Audits Are Impractical

Governance often begins with a data audit, as you must understand what information you have, how you use it, and what risks it carries to implement effective changes. However, many companies approach this manually. For example, just 36% of IT professionals use automated data cataloging.

Manual audits become increasingly impractical as data volumes grow. Sifting through terabytes of information to label and provide context for each data point takes considerable time. Spending hours on repetitive and information-intensive work also makes it easy to lose focus and make errors.

Automation is the ideal solution. Automated data cataloging and mapping tools can audit your organization's information faster than an employee could. In addition, computers can't get tired or distracted, so automating this process also reduces the risk of errors, ensuring all your labels and data maps are accurate.

Automation Streamlines Data Cleaning

Data quality is another common barrier to effective governance strategies. This should happen twice during the collection process, once when you've collected 60% of the information and again once you have it all. This will ensure you can make the most informed governance decisions, but reviewing and fixing data manually twice per audit is too time-consuming to be practical.

Despite these challenges, 46% of organizations today still clean their information manually. Unfortunately, that's a significant reason so many data and governance operations fail to meet expectations.

Automated cleaning tools can review data, fill incomplete fields, fix inconsistencies and standardize formats in minimal time. You'll then have an easier time understanding your information and storage and security needs. In addition, automated tools can flag fields that require a human touch, and the time you've saved by not manually cleaning your data will give you room to address the issue.

Automation Can Enrich Critical Data

Similarly, automation can enrich your data to make it more valuable to your business. As automated tools analyze and clean your information, they can scan the web or databases for critical context. They can use this to provide more insight into your data, making determining how best to act on the information is easier.

Security and stewardship are the most prevalent parts of data governance, but enrichment can also play an important role. This allows you to see the information for its full value, helping you more accurately determine what's the most mission-critical and guiding more effective security measures.

This enrichment also helps you identify what information constitutes personal details that may fall under privacy regulations. In addition, automated enrichment can give you a single access point for all users' PII. That consolidation makes it easier to comply with the right to erasure many laws like the GDPR provide users.

Continuous Monitoring Maximizes Security

Security monitoring is another important application of automation in data governance. Thorough governance requires a constant understanding of where your information is and what's accessing it. Gaining that insight requires continuous monitoring, and that requires automation.

The ongoing cybersecurity skills gap means many organizations don't have enough security staff to monitor their networks manually. Even if you do, human employees may be unable to spot abnormalities quickly enough, especially given the repetitive nature of this work. Automated monitoring tools offer a solution to both sides of the issue.

Machine learning models can establish a baseline for normal network behavior to spot potential breaches in real-time. They can then contain the breach so you can investigate it further and respond appropriately. As data security regulations rise, these quick responses will become increasingly crucial in governance.

Automation Improves Data Availability

Similar tools can prevent disruptions that may hinder your data governance. For example, unplanned IT outages are common, with 80% of data centers experiencing at least one in the past three years. These disruptions can take security tools offline, render mission-critical data inaccessible and expose customers' PII, so preventing them is critical for effective governance.

Network monitoring tools can detect changes like increasing requests and use automated load-balancing systems to adjust to these shifting needs. These timely, automatic adjustments prevent overloading servers and similar disruptions. As a result, you can always maintain insight and control over your data.

Like with security monitoring, enabling this level of responsiveness is nearly impossible with manual processes. Automation provides the most effective solution and, in many cases, the only feasible one.

Data Governance Needs Automation

Effective data governance requires a considerable amount of repetitive work, analysis, and continuous insight. Achieving that is challenging without automation.

Automated tools provide the responsiveness, accuracy, and efficiency you need to make the most of your data governance operations. You can then reduce the strain on your workforce while meeting increasingly stringent regulations, security concerns, and data quality needs.