Interview with Chongwei Chen, President & CEO, DataNumen

This interview is with Chongwei Chen, President & CEO, DataNumen.

1. Can you introduce yourself and your role at DataNumen? What inspired you to specialize in data recovery software?

I'm the President and CEO of DataNumen, where I lead our mission to help individuals and businesses recover their critical data. My journey into data recovery began during my master's studies at Zhejiang University. I was inspired by media coverage of shareware developers succeeding in the software market, which motivated me to create my own solution.

The defining moment came when I spent hours downloading a large Zip file, only to find it corrupted and unable to open. Using a hex editor, I discovered that most of the data was intact—just minor damage at the end. Rather than re-downloading, I realized I could repair it myself. This frustration sparked the creation of Advanced Zip Repair, my first data recovery tool.

What started as a solution to my own problem revealed a widespread need: people everywhere were losing access to valuable data due to file corruption. After the success of Advanced Zip Repair, I expanded our product line to address corruption across:

Office files
Emails
Databases
Images
Documents
Backups
Archives

This evolution grew DataNumen into the comprehensive data recovery company it is today. What drives me is knowing that behind every corrupted file is someone's important work, memories, or business-critical information—and we have the expertise to help them recover it.

2. How did your journey in tech evolve from your early struggles with software products at Zhejiang University to founding a successful data recovery company?

My journey was far from smooth. When I first launched Advanced Zip Repair, the reality hit hard—in six months, I sold only three copies. I seriously considered abandoning the whole venture, thinking I'd starve trying to make a living from software development.

But before walking away completely, I made a small adjustment to the business model. That seemingly minor change produced unexpected results: sales jumped to over $600 that very month. It was a turning point that taught me an invaluable lesson—sometimes success isn't about the product itself, but how you position and deliver it to your market.

By the time I graduated from Zhejiang University, I had built a stable monthly income exceeding $2,000. That gave me the confidence and financial foundation to officially establish DataNumen and commit fully to professional data recovery software development.

Those early struggles were crucial. They taught me resilience, the importance of business model experimentation, and that failure is often just one pivot away from success. That experience shaped how I approach challenges at DataNumen today—we're always willing to adapt and innovate, both in our technology and our approach to serving customers.

3. You mentioned using AI models like ChatGPT and Claude to stay updated on industry trends. Can you share a specific instance where this approach led to a significant innovation or improvement in your data recovery solutions?

I'll be honest—I was initially skeptical about AI's capabilities in software development. I assumed AI could only handle trivial tasks like writing simple games such as Snake, and that any code it produced would be inferior to human work, riddled with bugs. That assumption was completely wrong.

After experimenting with AI models, I discovered they are capable of developing commercial-grade, large-scale software with quality that matches or even exceeds human development—and at dramatically faster speeds. This was a game-changer for DataNumen.

Based on this realization, we now leverage AI for developing our newer products. Specific examples include:

DataNumen STL Repair
DataNumen FIT Repair

These tools were built with significant AI assistance, allowing us to expand our product portfolio much more rapidly than traditional development would allow.

The key insight wasn't just that AI could code—it's that AI enabled us to enter new file format recovery markets quickly and efficiently. Instead of spending months developing each new recovery tool from scratch, we can now prototype, develop, and refine solutions in a fraction of the time. This allows us to respond faster to emerging customer needs and support more file formats than ever before.

4. Your company developed an AI-powered tool to automate HARO query filtering. What were the biggest challenges in creating this tool, and how did you overcome them?

The biggest challenge was converting different HARO queries into a unified, structured data format that could be processed automatically by our system. HARO queries come in many different styles, formats, and levels of detail, making standardization a significant technical hurdle.

Fortunately, the Featured platform provided excellent support that directly addressed this challenge. Their API delivers HARO query data in JSON format, which gave us a consistent, machine-readable structure to work with. This was crucial—instead of having to parse and normalize unstructured text from various sources, we could immediately begin building our AI filtering logic on top of clean, structured data.

With this foundation in place, we focused our development efforts on the AI algorithms themselves—training the system to understand query relevance, match topics to our expertise, and identify high-potential opportunities. The structured JSON format allowed our AI to quickly analyze key fields like industry categories, journalist requirements, and deadlines without getting bogged down in data extraction and normalization.

This partnership between Featured's robust API infrastructure and our AI capabilities created a seamless automation workflow. What once required manual review of hundreds of queries can now be filtered intelligently and instantly, allowing us to focus our time on crafting quality responses to the most relevant opportunities.

5. You achieved a 10x improvement in server performance through AI-driven log analysis. Can you walk us through the process of implementing this system and any unexpected insights you gained?

We achieved this breakthrough through a systematic, iterative approach rather than a single dramatic change.

The Process:

Every week, we fed our server logs into AI models and asked them to identify performance bottlenecks and provide actionable improvement recommendations.
We then implemented these suggestions on our servers and monitored the results.
The following week, we would input the new logs back into the AI and ask it to evaluate whether the previous week's changes actually improved performance.
If an optimization proved effective, we kept it. If it didn't deliver results, we abandoned it and moved on.

This continuous feedback loop was key—we weren't just blindly implementing AI suggestions; we were creating a data-driven validation cycle that separated genuinely effective optimizations from theoretical improvements that didn't work in practice.

Unexpected Insights:

The most surprising discovery was how many "obvious" optimizations either had minimal impact or sometimes even degraded performance in our specific environment.
The AI helped us identify non-intuitive bottlenecks we would have overlooked—things that didn't match conventional wisdom but made huge differences in our actual usage patterns.
Another insight was the compounding effect: small, validated improvements built on each other. No single change gave us 10x performance, but the accumulated effect of dozens of weekly optimizations, each validated and refined, eventually multiplied our server capacity dramatically.

This taught us that AI's real power isn't replacing human judgment—it's accelerating the experimental cycle and helping us test hypotheses faster than traditional methods ever could.

6. In your experience, what's the most common misconception about data recovery that you encounter among clients or the general public? How do you address it?

The biggest misconception is taking data recovery as a kind of magic that can restore any and all lost data, no matter the circumstances. This belief often leads to unrealistic expectations and disappointment.

The Reality: Data recovery can only restore data that hasn't been permanently lost. Once data is truly gone—particularly when it's been overwritten by other data—it becomes unrecoverable. It's not a limitation of our tools or expertise; it's a fundamental reality of how digital storage works.

How We Address It: We take a transparent, educational approach. When clients send us their corrupted files, we manually analyze them before making any promises. If we discover that portions or all of the file have been overwritten with zeros or other data, we clearly explain the situation: those overwritten sections are permanently lost and cannot be recovered.

For example, we might tell a client: "We can see that 70% of your file's original data is still intact and recoverable, but the remaining 30% has been overwritten and is gone permanently. We can recover what remains and reconstruct as much as possible, but we want you to understand the limitations upfront."

This honest assessment serves two purposes: it manages expectations realistically and helps clients understand the importance of prevention—regular backups, proper shutdown procedures, and prompt action when corruption occurs. We would rather disappoint someone with the truth early than promise miracles we can't deliver.

7. You've emphasized the importance of understanding the market deeply. Can you share a specific example of how customer feedback or market research directly influenced a product decision at DataNumen?

We had a real case with DataNumen SQL Recovery that perfectly illustrates the value of listening to customers.

The Problem: Our software originally displayed "1000 records recovered" every time it recovered another thousand records. When processing databases with millions of records, users would see a full list of this same message for extended periods—sometimes hours. The log appeared static, with no visible changes, which made customers think our software was deadlocked rather than actively working.

Our Solution: We redesigned the logging system to display "Totally ### records recovered" with a continuously updating cumulative count. Now, instead of seeing repetitive identical messages, users see: "Totally 1000 records recovered," then "Totally 2000 records recovered," "Totally 3000 records recovered," and so on.

The Impact: This simple change had significant results. Users could now clearly see progress in real-time. The continuously incrementing number gave them confidence that the software was working properly and making steady progress through their database. This increased trust translated directly into improved sales—when people believe your software is actively working, they're more likely to complete the purchase and recommend it to others.

It's a perfect example of how a small UX detail, identified through customer feedback, can dramatically impact both user experience and business outcomes.

8. As AI continues to evolve, how do you see it changing the landscape of data recovery and protection in the next 5-10 years? What potential risks or challenges should businesses be prepared for?

The Evolution: Over the next 5-10 years, the comprehensive integration of AI into data recovery will be a major industry shift. Currently, most raw-level data recovery relies on file carving techniques combined with file system metadata—essentially rule-based, mechanical approaches to reconstructing data. These traditional methods struggle with complex scenarios. For example, when a hard drive has been repeatedly formatted with different file systems installed over time, rule-based recovery becomes increasingly ineffective. The layers of metadata conflicts and fragmented data patterns overwhelm conventional algorithms.

AI changes this fundamentally. By comprehensively analyzing thousands of real-world data disaster cases, AI can recognize patterns, predict data locations, and make intelligent decisions that simple rules cannot. Instead of mechanically following predefined logic, AI-driven recovery can adapt to unique situations, handle ambiguity, and achieve significantly better recovery rates in complex scenarios.

The Risks and Challenges: The biggest challenge businesses face isn't the technology itself—it's successful integration. This breaks down into two critical areas:

Overcoming Human Bias: Many professionals are skeptical of AI or overestimate its limitations based on early experiences. Others swing too far the other way, treating AI as infallible. Both extremes hinder effective adoption.
Accurate Understanding: Businesses must develop a precise grasp of AI's actual strengths and weaknesses. AI isn't magic—it has specific capabilities and specific limitations. Success requires knowing when to apply AI, when to rely on traditional methods, and how to validate AI-generated results.

Companies that master this balance will lead the industry. Those that don't risk being left behind.

9. Based on your entrepreneurial journey, what advice would you give to tech founders who are struggling to find the right balance between technical excellence and market demands?

My advice is straightforward: Let market demand be the driving force for technical excellence. Without market demand, even the most perfect technology loses its meaning. I learned this lesson the hard way. When Advanced Zip Repair first launched, I had built what I thought was a solid technical solution. But in six months, I sold only three copies. The technology worked—but I hadn't aligned it with what the market actually needed or how customers wanted to access it.

The Key Insight:

Technical excellence isn't measured by the elegance of code or the sophistication of algorithms—it's measured by whether it solves real problems for real people willing to pay for solutions.
A technically "imperfect" product that addresses genuine market pain will always outperform a technically "perfect" product that nobody wants.

Practical Application:

Start with the market problem, not the technical solution.
Talk to potential customers.
Understand their frustrations.
Then build the minimum technical solution that addresses those needs effectively.

You can always refine and improve the technology later—but you can't retrofit market demand into a product that was built in isolation.

Technical founders often fall in love with the technology itself. I understand that impulse—I'm a technical person too. But our job isn't to create beautiful code; it's to create value for customers. When you frame technical excellence as "what best serves the market," suddenly the path forward becomes much clearer.

The market will tell you where your technical efforts should focus. Listen to it.