Did you enjoy school?
Yes I did love school! I loved math, which isn’t surprising for someone who grew up to become a data person, but I also loved English. That’s important when you want to use data to tell stories. I had a dream of writing the next great American novel, as many do, but I did manage to bring those two loves together through numerous articles and books. Through understanding numbers and making them into powerful narratives it’s given me a great career and brought me to my role at ThoughtSpot.
What qualifications do you have?
I went to the Jones Graduate School of Business (Rice Business) at Rice University for my MBA. Much of my early learning in tech was self-taught while working for Dow Chemical in Switzerland. With a two-hour train commute each way, I read constantly and taught myself Basic, Novell Networking, and Focus. My undergraduate degree was in English with a BA from the University of Maryland, where I focused on journalism, writing, and sociology! I continue to take a hands-on learning approach, whether testing software or working through strategy.
Has your career path been a smooth transition, a rocky road or a combination of both?
My own career path has been a combination of both ways forward. In some ways I have always been a pioneer: building one of the first data warehouses for Dow Chemical at the beginning of my career, and of course simply being a woman in the days when there were much fewer in the technology field. Running the BI Scorecard, which was a subscription based advisory service was also a first in the industry. The transition to Gartner was straight forward because it was advisory work but at a larger scale.
Now at ThoughtSpot, it’s been rocky because everything is about disrupting the status quo. It’s another first for a vendor to have a strategic advisor for customers. Podcasting is new for me. At the same time, we are innovating in the cloud, co-innovating with our customers and partners. There is no playbook for much of what we are doing.
What is the best career advice you can give to others?
We devote so much energy and time to our jobs and often at the expense of other commitments, whether family or friends. You have to believe in and love what you do. Part of that is choosing the people you want to work with who will challenge you and enable you to grow.
I would like to see women have kinder internal voices. Sometimes we are our own harshest critics.
From where do you draw inspiration?
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You can’t work with such a community of data chiefs day-in and day-out and not be inspired! It’s a diverse group of leaders and change-makers who understand the transformational power of data. More importantly they are trailblazers, courageous enough to bet big and try new and innovative projects. That commitment to better, to not settling, to pushing the envelope, would be inspiring for anyone!
What is the biggest challenge you have faced to date?
Because some of my biggest challenges were earlier in my life, I continue to know any obstacle can be overcome. In college, I had to work sometimes three jobs to be able to stay in school. My guidance counsellor thought it was impossible, but it wasn’t. In working at Dow, the vendors at the time thought we were crazy to attempt a global deployment. But it wasn’t impossible. We were simply the first. So I believe in the view of commit, then figure it out. Work the dream and the problem from every angle.
What qualities do you feel makes a good leader?
A good leader is someone you can trust and who has a high degree of empathy. Part of why we are seeing so much resignation and reshuffling is because of outdated leadership styles – whether command and control or a loss of trust. Leaders can push their employees to breaking points or to a point of burn out.
From a work viewpoint what has the last 12-24 months been like?
Navigating the global pandemic has been tough for everyone yet the accelerated digital transformation has been astounding. It’s been a forcing function for many organisations to more aggressively digitise, transform, and move to the cloud. But it has shown an even bigger gap between the data and analytics leaders and those who lag behind. Multiple analyst reports show that those who mastered this, who are in command of their analytical power, have double the revenue growth and higher profits. Moreover, there’s the belief that giving analytics power to frontline staff will empower them to create incremental gains. Helping organisations navigate this rapidly changing landscape and execute on their data strategy is what has been keeping me busy and ThoughtSpot growing so rapidly! So despite the many challenges, the analytics sector has growing opportunity and success.
What would you say are the biggest tech-based challenges we face today?
Innovation, regulation, and ethics make an interesting triangle of factors. Europe tends to lead in regulation but the US administration has signalled a more proactive stance towards AI regulation too. There’s still a lot of fear in the debate about data and AI and society needs well-informed regulation to progress safely. Society will suffer if AI is allowed to perpetuate bias at scale, but it will also suffer if we over regulate. We have dreams of closing gaps in equality of healthcare and hiring that properly trained AI can support. Companies building, testing, and deploying AI are responsible for ensuring its outcomes.
Innovations in technologies like artificial intelligence, natural language processing, search, and machine learning have fundamentally changed the way companies interact with their data, making it easier than ever for the everyday business person to ask questions of their data and get valuable business insights. Diverse teams are needed to build these systems, to recognise data gaps and biases in the data used to train the models, monitor outcomes and drive innovation. As part of any analytics and AI strategy, diversity must be part of the process to recognise biases in training data and to scenario plan possible impacts. Ensuring AI is explainable with transparency in data, data gaps, and algorithmic logic can reduce bias at scale. These are some foundational elements to mitigate very real risks from improperly used technologies.
Give us a fact about you that most other people wouldn’t know.
Well I’m a voracious reader – though that may not be a surprise. I hope my friends and family would say that I’m an incredible baker.