#231: Revolutionising Property Technology with Modular Analytics, with General Manager, Innovation & Advanced Analytics of Investa Property Group

This week we welcome to the podcast, Joanna Marsh, the General Manager of Innovation and Advanced Analytics for Investa Property Group. She’s also the CEO and Co-Founder of a “side hustle” at Exomnia, a startup that provides real estate companies with a modular approach to analytics.

Exomnia has only been in operation for four months, but it is already turning heads. It has recently completed a pre-seed funding round for an impressive $1.5 million. On the podcast, Joanna shares some deep insights into the opportunity and challenges of building a data startup. 

Data startups need to meet cyber security expectations before they can begin interacting with enterprises around data. The enterprises have strict regulatory requirements in this area. This creates a challenge for the startup, as they need to invest in gaining certifications before they can even build the MVP that most pre-launch startups focus on.

However, the gap in the market is significant, and as Joanna says, Exomnia is already resonating with foundation clients. With advanced analytics available at the click of the button, Exomnia is poised to make some real waves in the property technology space. 

Tune in to this podcast for some fascinating insights on building a data company at its earliest stages!

Thank you to our sponsor Talent Insights Group!

Connect with Joanna: https://www.linkedin.com/in/joannamaemarsh/

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“We had to give enough confidence that we were going to deliver on what we were promising without locking the startup into something that we didn't know how to handle.”

—  Joanna Marsh, General Manager, Innovation & Advanced Analytics of Investa Property Group

WHAT WE DISCUSSED

9:59: Felipe introduces Joanna, and then asks to overview her career to date.

15:11: How long did Joanna have the idea for Exomnia before pulling the trigger?

24:22: Joanna explains the challenges that she faces in protecting her IP when starting up a data company.

26:34: How was Joanna able to navigate challenging discussions with her first investors?

32:52: How has Joanna avoided conflicts of interest in the first investors and foundational customers being the same?

35:21: One of the biggest challenges for startups when working with corporates is managing all the requirements and processes around insurance, security and privacy that they need to meet. Joanna overviews how her company went about this.

41:49: Joanna explains the value of using open source so other startups can “plug in” to Exomnia’s data and platform.

44:29: Joanna and Felipe compare the challenges of managing different kinds of data, based on how sensitive the sector is towards data.

47:24: What’s next, as Exomnia continues to build up as a startup?

EPISODE HIGHLIGHTS

• “I'm 42 and I have two kids, five and six. For women in rising corporate careers in really good companies who are doing well, we don't step out and start startups. It's too risky. If you look at just the pure statistics, even just women who run venture-backed companies, it’s a couple per cent, but women in analytics, AI tech, and women and property you're looking at someone very uncommon. I couldn't have done it without the kind of support that I was fortunate to have at my stage of life.”

• “We were working with Oxford and deploying billions of dollars off the back of the models. But then you have to have the technical talent to maintain them, and that is hideously expensive, so we realised that there needs to be an industry utility. So, we started to look at how to take this idea and create a standalone company.”

• “To get my first investors on side, I said ‘we now invest in a data-driven world, and the real estate industry is digitising quickly. So, you need this. It isn't there. You can't buy it. You could cobble it together through a lot of different enterprise level solutions, but it would be really expensive.’”

• “We had to give enough confidence that we were going to deliver on what we were promising without locking the startup into something that we didn't know how to handle.”

• “One of the things that I had to justify with the capital was that they're saying ‘well where you are, you should be doing MVP prototypes’. They did not think we should be spending a lot of our capital on cybersecurity, privacy and so on, but the thing is, I know what's coming from the customer. And it's a non-negotiable, so while it looks odd, like we're building the startup in reverse, I'm very clear that this is how we must do it.”

• “The goal is to start providing templates for people to democratize. That way you're not burning your people for 48 hours to get a new cut of the data, and the data engineering and data science teams actually get to do what they're good at instead of the sorts of things that we waste our best people on.”

• “One really exciting thing is the large language models and interacting with analytics through text. This is really important, I think, from a democratisation standpoint for non-technical users. How many times do you talk to someone who tells you they’re not technical because their math teacher in seventh grade scared them or something, and because of that they look at a graph and their mind goes fuzzy? For me, the large language model interfaces, text interfaces, is a new UI that will be less scary to these people.”

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