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4 Data Governance Challenges Holding Back Your Business Goals and Data Assets

by iSphere on October 28, 2024 in Blog, Data Governance

Data is an asset that can pull organizations towards their goals. That said, the difference between those with data governance and those without is the difference between trained huskies carrying a dog sled to the finish line and distractable golden retrievers pulling you into a snowy ravine.

By implementing data governance, organizations can increase the availability, accuracy, usability, security, and interoperability of their data, propelling their goals forward rather than holding them back.

However, coordinating the necessary standards, policies, processes, systems, and people for governing data requires intention and can easily go awry—if you’re not prepared to overcome common data governance challenges.

Here’s what you need to know if you’re going to maximize the value of your data through proper governance.

1.) Breaking Down Data Silos

If you’re not the person consistently reviewing data assets, you’d be floored by how chaotic and scattered data can become without oversight. Different departments and functions will often adopt new platforms and data storage solutions that are independent from others in the organization. When they do, they’re also unlikely to give key stakeholders updates.

This creates data silos, which are isolated data repositories removed from the rest of your organization. More than just being detached from your larger system, data silos can cause incompatibility, redundancies, and blind spots in your data that reduce visibility and hinder decision making.

Organizations can only eliminate data silos by first changing their organizational mentality. Leaders at the top need to be invested in data governance. Their dedication to codifying data standards and integrating disparate data into a single source of truth can break down barriers. Plus, their engagement in communicating data governance best practices down the line can increase the chances end users take this new initiative seriously.

Preventing the formation of new data silos is an ongoing battle. Having dedicated data owners to oversee data elements and processes for their function and communicate back to a data policy committee can prevent silos from reforming.

2.) Improving Data Quality

Do your users trust the quality of your data? High data quality is a major cornerstone of data governance. The accuracy, completeness, reliability, and timeliness of your collected data can influence the ability of your leaders, employees, customers, and users to use insights to make impactful decisions.

Poor data can come in the form of incorrect phone numbers, incomplete addresses, mismatched SSNs, false sensor readings, miscategorized sensitive information, and other unreliable information. Even real-time data can create quality issues, delaying processes because of human error, malfunctioning equipment, or even AI hallucinations.

How do you fix poor data quality? Start by getting a lay of the land. Conduct a diagnostic to determine if your existing data sets meet your standards of quality. These diagnostics can help to quickly identify if there are problem users, dysfunctional functions, or even gaps in your data input process.

Then, promote data standardization for data formats, naming conventions, and other data elements. Communicate these expectations to the team and whenever possible, update rules for automated data collection, and monitor data quality to address new issues as they arise.

3.) Reducing Privilege Creep

Just because good data governance requires accessibility does not mean that everyone should have access. In fact, the more people who have accumulated rights to view, manipulate, or analyze data that does not pertain to their role, the more your organization risks poor data quality or data falling into the wrong hands.

To resolve this problem, organizations should follow the principle of least privilege (PoLP) that only grants users access to the essential data, applications, and authority they need to complete their jobs. Additionally, leaders need to prioritize data access governance, so that people who need data can use it and those who don’t stay restricted.

4.) Processing Data in Real-Time

The abundance of data (especially from real-time sources) has the potential to reduce quality and compliance. Before the era of streaming real-time data, you could get away with batch processing because your data was growing at the pace of employee user or customer input. Now, data proliferates at an unprecedented speed, presenting a challenge to organizations looking to future-proof their data governance.

Real-time data governance strategies can help with this challenge. Organizations can start by creating rules that trigger alerts to automatically tag data with particular categories and functions in mind. For PII, PCI, and other sensitive data, you can automatically mask data at the column or field level, protecting privacy and maintaining regulatory compliance.

Plus, the right artificial intelligence tools can automate data classification, detect quality issues in real time, and validate regulatory compliance in the moment. Using AI agents to oversee certain elements of the data governance process can provide your organization with an adaptable AI solution that continuously learns to better define, organize, and monitor incoming data.

Stay Aware of Data Governance Challenges

This isn’t a comprehensive list of the data governance challenges you’ll face. As your organization makes the shift to greater accuracy, reliability, and security, you’ll find plenty of hurdles along the way.

Yet when you choose to work with the right data management services company, you can simplify your roadmap, identifying drifts and obstacles along the trail so your organization can sprint towards the finish line.