We’ve been talking a lot lately about Keatext and how it is successfully carving out a space in the text analytics industry. Just as important are the people behind the scenes who are turning these ideas into reality.
In this interview, Keatext’s very own CEO, Narjès Boufaden, shares her insights on life, business lessons and thoughts on the future of text analysis.
Q: Tell us a bit about yourself.
A: For as long as I can remember I have always been interested in the field of Artificial Intelligence. I used to watch movies and TV shows that centered around the relationship between humans, language, and machines — like Star Trek, without a doubt my favorite TV series growing up. Captain Picard was my favourite character. I liked his leadership skills but most importantly I liked the way he leveraged the team’s individual competencies to solve problems.
I guess it is no surprise that my academic path led me to completing a Masters & PhD in Computational Linguistics! This is a pretty broad field, but my specific interests were in the comprehension and interpretation of human speech. In other words, how do we manage to understand each other through fragmented conversations and the absence of proper grammar?
Q: Who are your role models?
First and foremost, my father. I remember him as a passionate man who had that great big entrepreneurial spirit. He was an out-of-the-box thinker, always drumming up new ideas and innovative ways to solve complex problems. I definitely caught the entrepreneurial bug from him! Secondly, I really admire Richard Branson and the way he thinks about the world. He’s a risk-taker and I like that.
Q: Where did the idea to launch Keatext come from?
A: In 2010, I made the transition from the world of academia to business. This gave me a whole new perspective on text mining and I saw how it was used by different organizations and verticals. Companies were looking for a way to analyze text-rich threads of customer feedback more efficiently but a lot of the text mining and text analytics tools involved tons of customization, and lengthy implementations. It became very clear to me that there was a definite gap in the industry for a text analytics solution that could more simply and effectively address their needs. That’s when the light bulb in my head went off and the concept for Keatext was born.
Q: What has been your greatest challenge in your career so far?
The road to building a successful startup is never easy. I think as a woman there’s an added layer of difficulty there, too. I had to learn to balance raising a family with running a business. It was really hard at times. Startup life is filled with uncertainties and you have to learn to navigate the ebbs and flows. My biggest piece of advice for entrepreneurs out there? Seek the guidance of a mentor who can provide you with not only the knowledge and experience but the support and encouragement needed to keep going.
Q: What makes Keatext different?
When we look at the market today, there’s two types of text analytics solutions: API’s and Enterprise-level applications. API’s involve a ton of implementation and integration and the other side of the spectrum you have full suites of costly and complex enterprise solutions. With Keatext, we want to provide companies with an app that they can easily connect to their existing CRM platforms. There’s no need for API integrations, and Keatext eliminates the need to purchase an enterprise license that might cost hundred of thousand of dollars.
Q: What do you believe are the biggest trends impacting the text analytics space?
There’s a big focus on technology solutions that improve customer engagement. More specifically, there’s a lot of talk around the role that Artificial intelligence will play in the coming years. I don’t really believe that machines will completely replace human ability, it would simply be too hard for an artificial system to acknowledge all those human characteristics and provide the appropriate actionable feedback to help a company truly increase their customer engagement.
Instead, I am confident that in the future, we will see more initiatives that emphasize the relationship between machines and humans — in my field for example, how natural language processing (NLP) technology can help them address specific text analytics challenges.
I also think we will continue to see an increase in predictive text analytics solutions that are user-friendly and ergonomic. The idea here is that text analysis will “run in the background”; the analyst feeds the system information, the system learns how the analyst works and as a result can not only predict outcomes, but is also sophisticated enough to suggest actions the analyst or customer support staff should take to prevent customer churn, for instance.
This is exactly where we’re heading with Keatext.