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Expera IT was founded in Calgary nearly thirty years ago, and we have been helping our awesome clients navigate the ever-changing tech world ever since, ensuring they’re always able to successfully leverage the latest tools. While there have been many shifts, the most recent—and one of the biggest—is A.I. readiness.

Our CEO and Founder, Vince Fung, recently shared some insights in a virtual keynote. In this blog series, we’re breaking down the key points into further detail to give you a deeper look.

Today, in the second part of our series providing a deep dive into this topic, we’ll be covering data hygiene and why it’s a core component of A.I. readiness.


Why Data Hygiene and Cleanup Matter

To successfully implement AI, it’s crucial to emphasize the importance of a thorough data hygiene and cleanup process. Data hygiene and preparation are essential because they ensure that A.I. can accurately analyze current, relevant data while maintaining security by safeguarding sensitive information from unauthorized access or leakage.

Here are the steps that our expert team recommends taking to properly set the foundation for A.I. success.


Identify Your Most Valuable Data

As we help organizations adopt A.I. and leverage it for a competitive edge, we suggest every business leader should ask themselves: What is my most valuable data?

This question might not have an obvious answer. Today, your most valuable data is likely data that a human can understand and analyze. However, many organizations collect vast amounts of data that it is difficult for humans to comprehend. This is where A.I. shines, because it can analyze these massive data sets and make sense of them. So, it’s essential to consider what valuable insights you could gain from this data that you can’t get today.


Identify Where Your Most Valuable Data is Stored

Another important consideration is: Where is your data stored?

Identifying your most valuable data and ensuring it’s stored securely and accessibly on the right infrastructure is crucial for effective A.I. implementation. Many organizations still store data on file servers or on various platforms that are not easily accessible.

For example, with Microsoft Copilot, data needs to be on the Microsoft 365 fabric (OneDrive, SharePoint, email, Microsoft Teams) for the A.I. to leverage it.


Create A Plan for Handling Old Data

Organizations that have been around for a long time often have a lot of old data, but the A.I. needs to be accessing only the most current, up-to-date data.

This means that your team will need to remove or archive irrelevant data to prevent outdated information from interfering with A.I. analysis, so that you can make decisions based on the most up-to-date data.


Determine If Integrations Are Needed

If your business uses cloud services, you’ll need to ensure your cloud applications and vendors support secure data access and integration, so that the A.I. to utilize this information effectively.

The average small business uses at least eight or nine cloud services, while large organizations use dozens. If your most valuable data is on different cloud services, integrations become key.


Manage Permissions and Data Sensitivity


Access Control

Next, you will need to review to ensure that data is in the right places with appropriate permissions and access controls.

Data security both within and outside of your organization is a critical part of rolling out A.I. Solutions like Microsoft Copilot don’t give users more access than they already have, but they do highlight the access a person might already have. For example, if payroll information is saved in a folder that the entire team has access to, it might not be noticed prior to the implementation of A.I. However, Copilot could expose it during a search because it was incorrectly stored.


Data Loss Prevention (DLP) and Sensitivity Labels


What is DLP?

Data Loss Prevention (DLP) involves tools and processes to ensure sensitive information doesn’t leave the organization inappropriately. DLP is essential for securely rolling out AI.


Sensitivity Labels

Sensitivity labels help classify and protect information based on its sensitivity. Files with salary information, personally identifiable information (PII), or financial data need to be managed carefully.


Implementing DLP and Sensitivity Labels

If you are on Microsoft 365 Business Premium or Enterprise E5, you already have access to DLP tools and sensitivity labels. However, many organizations don’t use them due to the complexity of implementation. If something like this is a roadblock for your organization, consider enlisting a team to help you deploy these tools to protect sensitive data.


Microsoft Purview

Additionally, for those on Microsoft Enterprise E5 Security, Microsoft Purview offers advanced DLP. It automatically analyzes and tags files with sensitive labels, ensuring sensitive information doesn’t leak outside the organization.


Ongoing Data Hygiene

Data hygiene is not a one-time process; it’s an ongoing requirement. Organizations need regular data hygiene practices to ensure the accuracy of A.I. results, and that your data stays secure. As roles change, people are hired or leave, and permissions evolve, regular audits and updates are necessary to maintain data security.


Set Your Business Up for A.I. Success

Identifying your most valuable data, optimizing its storage and accessibility, and implementing strong security measures like DLP and sensitivity labels are crucial steps to take before rolling out A.I. solutions. These practices don’t just boost A.I.’s effectiveness but also safeguard your sensitive information. Ready to take the next step in securing your data and leveraging AI?

Contact our team today to schedule an A.I. risk and readiness asssessment, and explore how we can help you achieve these goals.