Common Mistakes in Big Data Management… Avoid Them Immediately!

Common Mistakes in Big Data Management… Avoid Them Immediately!

Despite increasing investments in digital infrastructure, many companies still make fundamental mistakes that diminish the value of data rather than maximizing it.

🚨 These mistakes can cost organizations millions of riyals annually and hinder their ability to make effective strategic decisions.

Here are the most critical mistakes to avoid:

📁 1. Storing Data Without a Clear Strategy

Keeping data just because “it might be useful someday” leads to unnecessary complexity and load.

✅ Solution: Adopt a clear classification and governance policy that defines what should be kept, why, and for how long.

🔐 2. Ignoring Cybersecurity in Data Collection and Analysis Phases

Cyberattacks are increasingly targeting large databases, especially in the financial and healthcare sectors.

✅ Solution: Implement data encryption, access permission management, and regular security protocol audits.

📊 3. Relying on Tools Without Training or Analytical Understanding

Tools like Power BI or BigQuery offer tremendous capabilities but are ineffective if the team is not trained to use them or link them to strategic goals.

✅ Solution: Invest in training, not just software.

🧱 4. Operating in Isolated Data Silos

Some departments isolate their data from others, preventing a comprehensive view.

✅ Solution: Adopt an integrative approach that links databases and unifies the analytical framework.

🧪 5. Ignoring Data Quality and Validation

Inaccurate data leads to inaccurate decisions. It makes no sense to use “bad data” to make “good decisions.”

✅ Solution: Establish regular data validation processes.

🇸🇦 What About Saudi Arabia?

Local reports indicate that a significant portion of companies in the Kingdom still suffer from these challenges despite digital progress.

Programs like “SDAIA Capacity Building” and the “Center of Excellence in Data” provide support to overcome these gaps.

💡 Big data management is not about having more data, but about having accurate, organized data tied to a clear institutional vision.

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