Mitigating the Risks of Data Democratization

Democratizing data use within an organization empowers employees, fosters greater collaboration, and enables them to make more informed, data-driven decisions. By giving data and analytics tools and solutions to the many, companies can take accurate and timely decision making out of the hands of the few, resulting in improved productivity, increased efficiency, and enhanced business outcomes.

With access to data—and the tools needed to analyze or integrate it into their own applications—employees no longer need to wait for the overburdened and understaffed IT departments to set things up for them. This alone can speed up the process of identifying trends, predicting outcomes, and making more impactful decisions. When done correctly, data democratization can lead to improved business outcomes, increased revenue, and a competitive advantage in the marketplace.

The fast and effective utilization of data to improve decision making across an entire organization, by all employees, will be what sets companies apart from one another in the future.

Potential Pitfalls

That said, while there are numerous benefits of data democratization, the process is not as easy as it sounds. There are several potential pitfalls that need to be managed to maintain accurate and consistent data that can be used by all, for the good of the company. Without governance and guidelines in place, any organization-wide data initiative can quickly unravel.

Some potential issues to be aware of include:

  • Security - One of the most significant risks of data democratization is compliance and security. As more employees gain access to data, the risk of data breaches increases. Because of greater data availability and use, an attacker might have additional opportunities to steal data, or an employee could accidentally gain access to, or share, sensitive information. To mitigate this risk, companies must establish and enforce strict governance and security protocols, such as access controls, user authentication, and more. Proper training of employees on how to use data correctly within the guidelines is key as well. Protecting data is just as important as enabling its use.
  • Privacy - Another important consideration is privacy and complying with relevant regulations around data use. Employees must be trained in the proper handling and protection of personally identifiable information (PII), and the company must ensure that data privacy regulations, such as those connected to GDPR and HIPAA, are being followed. Potential risks, such as the unauthorized disclosure of personal information, the use of data for unethical purposes, or the potential for data to be used to discriminate against individuals, need to be protected against. Ways to solve this can include anonymizing data, encrypting it, or restricting access to certain types of data, such as medical or financial records. There is very little business benefit for this type of data to ever be accessed outside of HR or payroll—so be sure to lock it down. Improper access and use of any sensitive data could have wide ramifications for a company—both financially and reputationally.
  • Ethics - Ensuring that the programs used to analyze data are making ethical decisions is another important consideration. The use of artificial intelligence (AI) and machine learning (ML) algorithms can help to automate analysis and decision-making, but as these tools become more mainstream, there needs to be a greater consideration about whether they’re introducing any type of bias into the process. As we’ve already seen with the increased use of free AI tools, programs may make decisions that are not in line with the company's values or ethical standards. Companies should put in place clear guidelines for data use - and ensure that employees understand and adhere to them.

Mitigating the Risks

As more employees gain access to data, the risk of inconsistencies and errors increases. Companies must ensure that data is accurate and reliable, and that it has been properly vetted before it is used. This can include performing data quality checks and establishing clear guidelines for data validation and verification.

One of the goals of granting greater access to data and analysis is to improve data use and improve decision-making across the board. But if data becomes accessed and modified into separate line-of-business owned silos, then the initiative will have failed.

To mitigate this risk, companies must establish a clear process for updating data and ensuring that all employees have access to the latest information. Doing so also requires that there are rules and governance put in place to ensure that a common data source does not become corrupted, or that updates do not create a silo situation. This can include using a centralized data repository or semantic data layer that provides a single source of truth for all data within the organization. A semantic data layer provides a unified view of data, allowing employees to access and analyze data in a consistent manner. This can help to ensure that data is accurate and reliable, while also simplifying the process of data democratization.

The Importance of Democratization

Data is a powerful tool for empowering employees and improving business outcomes—but the process is not without its challenges and risks. Organizations must enforce strict security, ensure compliance with privacy regulations, establish ethical guidelines, and create a process for data access and updates that prevent data sources from becoming corrupted, inconsistent, or siloed. The use of data and analytics is too important to a company’s competitive advantage to let a few potential pitfalls scare an organization away from embracing data democratization.


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