Rogayeh Tabrizi, CEO and Founder of technology consultancy Theory+Practice, shares her top achievements of 2021 and strategic goals for 2022
Would you please start by telling us a bit about your greatest achievements at Theory+Practice over the past 12-24 months?
I have a very specialized team. We come from a broad range of disciplines. I’m a physicist and economist, and I have several other people who have very similar backgrounds to me. There are also computer specialists and data scientists.
I feel very proud when I think about how a very culturally, intellectually and professionally diverse group of people has come together and learned the languages of each other and our clients with humility and curiosity. By doing this, we are unlocking incredible potential, creating tangible value and tackling seemingly impossible problems, and to me, this is one of our greatest achievements.
I did my master’s degree while working at CERN in particle physics in Geneva. And then earned my PhD in economics. By the time I finished my PhD and started working as a Data Scientist Specialist, I realized nothing in my education had prepared me to talk to non-technical people.
So, it is not lost on me how difficult communication can be for people with a technical background. It takes hard work and humility to truly learn a very different language: The language of business. But this is an essential requirement if we want to translate our technical skills to create value for the organization and truly connect theory and practice!
I’m also proud of how effectively we partner and communicate with our clients. We help them find the right questions, shed light on the assumptions they are making without realizing, and decode their problems by extracting signals out of their data and turning them into action.
What new challenges is that giving rise to and how are you working to overcome them?
We work in the space of AI and big data. But we sit at the intersection of behavioral economics and AI. We use AI and ML models to extract behavioral signals for customer-centric industries, such as retail and finance, and then feed these signals back to different deep learning and ML models to identify the best decision and intelligent interventions.
These are two extremely fast-growing fields. One side of it is behavioral sciences and all the experimentation and advances that are happening in that area. And then the other part is seeing innovation daily; a new model, a new algorithm, a new methodology, both on the engineering side and the data science side.
The real challenge is to find the best solution for the business questions that our clients have, especially when they deal with legacy systems. Another big challenge is related to education and adoption of these solutions, in other words, how to turn the black box of AI and ML models to a clear box that our stakeholders can trust.
Flexibility and scalability of our solutions is extremely important, as there are very real technical challenges that you face. Science and technology are becoming more advanced every day. We have to be on top of all of that.
What are some of the key things that you want to achieve in the next 12 months?
We have been working with Fortune 10 and Fortune 100 companies on the problems and challenges that they are facing: There are a lot of common, sometimes hidden patterns.
So, what we have been able to do is bring these patterns and questions together and create an ecosystem of use cases. In data, we often talk about things like siloed or isolated datasets, legacy data and all sorts of associated problems. But there’s another problem, and that is siloed mentalities towards use cases.
The beauty – and perhaps one of our differentiators – is how we see the interconnectedness between different use cases and how we have created solutions for an ecosystem of use cases. The odds are if you want to really understand your customers, you’re going to use the same data that you’d use for understanding price sensitivity, or promotion affinity, or the same data that you’re going to use to build a recommendation system!
Well, we can create a lot of efficiencies by bringing these use cases together: By creating pipelines of data that hit several birds with one stone. So, for the next 12 months, we’re going to focus on creating as many efficiencies as possible for this ecosystem, productize this solution, and go to market with it.