AITech365 Interview with Doug Kimball, Chief Marketing Officer at Ontotext

“By working in conjunction with NLP, businesses can uncover and analyze complex relationships between customers, products, and sentiments.”

Doug, can you tell us about your professional background and your current role at Ontotext.

I have had the good fortune of working in several roles across a variety of companies and verticals markets, including spending 7 years working in Education as a counselor.  My journey has taken me from 10 years at Nielsen dealing with consumer data, focusing on retailers and manufacturers, to bar code standardization at UCCnet (1WorldSync), to several supply chain providers, and even  a few years at a master data management company. Now, as the marketing leader for Ontotext, I have the opportunity to leverage all of that collective experience to help customers extract valuable knowledge from vast quantities of data while enabling our sales teams to deliver revenue opportunities. Supported by a strong, global team, my role includes delivering thought leadership, enhancing our relationships with analysts, strategic event planning, persistent demand generation planning, brand development and growth. Plus, I regularly ask ‘so what, why, and who cares? to help us develop an approach that is not about us, but about our prospects and customers.

How does Ontotext differentiate itself from other companies in the same space?

 We are leading the industry in delivering proven scalability, and consistent profitability, and for our unwavering focus on delivering technology that enables actionable results. We are well funded, strategically focused and – something we hear very often from clients –  our post sale support is a key factor in their ability to leverage solutions. Another thing that makes us different, and is something I am extremely proud of, is our culture. Our headquarters are located in Bulgaria so we tend to say what we are going to do and do what we say. It’s a very straightforward culture, which I believe streamlines program development, creates more clear communication and information sharing and more; all of which lead us to deliver more excellence to our clients.

Given your specialized expertise in marketing and communications, specifically within the realm of enterprise SaaS, could you elaborate on the strategies that you regard as exceptionally effective for engaging and captivating global audiences?

 This is going to sound contradictory, but you have to make noise while cutting through the noise. We are an established company and we are always building our global brand, so part of my focus is getting our name out in front of more people in more places, at the right time. I have found that activities like effective event strategies and execution have a big role in this endeavor, especially when you can garner speaking opportunities as a platform to further espouse the value we bring to real world challenges. Leveraging speaking platforms that have a bigger voice than you do is important. Leveraging influencers in the right space is also important for deriving awareness. For example, it’s important to embark on a regular cadence of engagement by briefing analysts so they know about Ontotext, our success stories, capabilities and outcomes. With influencers, building champions who have large networks will level up exposure by standing on the shoulders of others.

However, all of those efforts are not effective without great positioning and messaging and ensuring that the product story is being told in the best, targeted way possible. In our case, this is an evolution as we move past what’s primarily been a technical conversation and expand into the bigger picture – the enterprise world of speaking to business value and outcomes. Talking about data for the sake of data is not beneficial, but explaining why a company should care, how their business is impacted by data integration, and sharing and knowledge management are the pillars  of good story telling. I don’t want to leave out brand equity, as how customers/prospects see you can be just as critical as what you say and how you say it.  We’ve made some shifts in our brand and messaging since I’ve come on board, and it is resonating well.

Can you explain how GraphDB, your flagship product, differs from traditional relational databases and what advantages it offers for organizations looking to manage and query large- scale knowledge graphs?

The power of GraphDB lies in its ability to determine relationships between data points, and by using semantics, adds context to help machines (and people) understand, interpret, and use all their enterprise data most effectively. Using the Resource Description Framework (RDF), GraphDB adds to the depth of knowledge and insights of an organizations’ data as it develops inferences and connections. Relational databases don’t have the ability to develop and maintain intricate relationships across multiple systems, can’t integrate disparate, unstructured data, and are limited in many ways, including scalability, speed, and true information management. Using AI, Ontotext’s semantic knowledge graph identifies and provides reasoning about relationships between data points that aren’t implicit and can uncover connections that may not have been identified before. At our core, lies a strong text analytics solution, but Ontotext provides customers with much more. It’s just easy to forget that it’s not really data our clients are after, it’s information and, more ideally, wisdom from information that is  often delivered in text. So, being able to connect the dots of your data and find insights from the text in that information is key to delivering value.

Knowledge graph development is a significant focus for Ontotext. Can you share some success stories or use cases where your technology has been instrumental in creating and leveraging knowledge graphs?

A knowledge graph is essentially a network of linked data that represents domain knowledge. When you overlay knowledge into a graph database through an ontology the ability to access and use data more effectively becomes very evident. For example, Ontotext has helped drug manufacturers bring medicines to market up to 10X faster, by providing them with the ability to more quickly search, analyze, and connect thousands of research materials. One of our publishing clients saved 30% on their data management costs by integrating multiple databases, text files, audio, and visual assets and delivered a more user intuitive access to their information. A major pharmaceutical organization also uses Ontotext GraphDB as the de facto system for their data integration and sharing efforts to enable a data fabric approach – connecting over 10,000 different systems.

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Developing a professional network is essential. How do you approach networking to ensure meaningful connections that can mutually benefit both parties in the long run?

I keep it real, meaning it is non-invasive and collaborative. If I reach out to someone on LinkedIn, I let them know why, and if I am asking something of them, I let them know. Most often, I generally want to connect so that I am on their radar and vice versa. I am part of a data leaders Slack group, and as I see people join that I find interesting, I send a connect message.  One of these has led to a couple of video meetings to gain vertical insights related to our business, and interest in a demo of our product. If someone connects with me, and the next message they send me is ‘can I interest you in buying XYZ?’ – it’s generally a short connection, although I always provide  a review and a polite response. We’re all selling in some fashion or another, and I prefer sincere conversation over the typical ambush approach.

What role does natural language processing play in sentiment analysis of social media data for understanding customer opinions about products or brands?

The power and flexibility of using NLP to automatically analyze and categorize the vast amount of unstructured text data from social media is pretty amazing. I mentioned text analytics earlier, as this is where semantic graph technology  supported by  NLP, can be a key contribution to sentiment analysis of social media data. With NLP, and all that unstructured data connected and enhanced with a knowledge graph, companies can improve customer satisfaction by looking at patterns in conversations, understand consumers’ needs better and make changes to their products or services accordingly, not to mention more quickly.

With these valuable insights, companies can gain a competitive edge, whether it’s responding in real time to customer feedback, trends or disruptions, or improve marketing efforts, supply chain strategies and much more. This is where the power of knowledge graphs really shines. By working in conjunction with NLP, businesses can uncover and analyze complex relationships between customers, products, and sentiments. Being able to really get into the nuanced relationships within social media data, enhanced with semantic context, companies can get a detailed understanding of customer sentiment and enable data-driven decisions that lead to enhanced product and brand strategies.

What advice would you give to other leaders who helped you personally?

Like others who have helped me, and there have been some great ones, I would say ‘take the time, take the chance, and take the responsibility.’  Being a good leader is being a mentor, and there is a need to be selective. It also means there is weight on your shoulders when you not just lead, but guide someone to reach the potential that is seen. This is something I try to really focus on with the teams I have led, helping them to be where they want to be, but sometimes that means leaving them to excel right where they are.

What is the biggest problem you or your team is solving this year?

For my team, dealing with a CMO who has a lot of ideas, enthusiasm, strategic vision, which can be a lot to process at times. For me, setting clearer priorities, ensuring I balance strategy, ideation, and tactical execution with realistic expectations. I’ve got a great team doing excellent work. But if we struggle in a specific area, I would say it’s the process of converting browsers to engagers….which is not uncommon among strategic marketers. This involves everything from delivering the right content at the right time for the right persona, to standing out in a crowded space with a lot of noise. The buyers journey for B2B is so much about education, teaching, and supporting prospects to have them be as informed as possible. We have a long sales cycle, so the more we can effectively feed our prospective audience, the better. Beyond that, I would say it’s adjusting our messaging to also reach the enterprise business users with the message that good data needs storytelling to be effective and usable.

Is there anything that you’re currently reading, or any favorite books, that you’d Recommend?

I’ve nearly finished a book called The AI Powered Enterprise by Seth Earley, and it’s quite good. Despite being well written and focusing on ontologies, data challenges it includes positive summaries throughout to make it a good resource for many levels of readers. I’ve also enjoyed A Knowledge Graph Cookbook from Andreas Blumauer, that tells some good stories about knowledge graphs, including the “why” and “where” of their  valuable.

Thanks Doug!