Editor’s note: This blog is part of a series called Meet the Google Cloud Data Champions, a series celebrating the people behind data- and AI-driven transformations. Each blog features a champion’s career journey, lessons learned, advice they would give other leaders, and more. This story features Jan Riehle, Principal at Brazilian investment firm Rising Venture and founder and CEO of a Brazilian company that runs a beauty e-commerce platform, B4A.
Tell us about yourself. Where did you grow up? What did your journey into tech look like?
I grew up in Karlsruhe, a town in southern Germany. Directly after high school, in the early 2000s, I opened my first tech company, an agency creating web technology for small and medium-sized businesses.
Several years later, I relocated to Switzerland, Singapore, and France, and I took roles in several tech companies while also collecting experiences in M&A and private equity. After pursuing an MBA at INSEAD, I relocated to Brazil in 2011, where I co-founded and ran various technology ventures between 2011 and 2015.
In 2017, I started a search fund that acquired two businesses in the “beauty-tech” space in Sao Paulo, Brazil’s commercial capital. These two businesses were the beginning of what today is B4A (“Beauty for all”), a platform that creates an ecosystem, connecting and mutually benefiting consumers, beauty brands, and digital beauty influencers.
What’s the coolest thing you and/or your team have accomplished by leveraging our Data Cloud solutions?
Our main objective is to provide value for our ecosystem participants, which are consumers, beauty brands and digital influencers. All of our technology efforts and success are made to serve that purpose and create value for these three groups.
The coolest thing we achieved by using Google’s Data Cloud solutions was to extremely shorten load times for our consumer-facing ecommerce platform, B4A Commerce Connect. You can see it in action at www.glambox.com.br or www.mensmarket.com.br. The performance gains are visible when you load heavy collections (like the “Para Ele” Collection on the Glambox website, for example). For such large collections, we were able to reduce load times by about 90% by implementing AlloyDB for PostgreSQL.
Our platform combines data about customer characteristics with machine-learning algorithms so that a user of our website only sees products that make sense for their individual profile. This raises a challenge because every load in our ecommerce platform requires more computing power than in a standard ecommerce platform, where such optimizations are not needed. Therefore, using the right tools and optimizing performance becomes absolutely crucial to provide smooth, fluid performance and a solid user experience. The user experience benefited dramatically from implementing AlloyDB. You can learn more about our journey in this blog.
Technology is one part of data-driven transformation. People and processes are others. How were you able to bring the three together? Were there adoption challenges within the organization, and if so, how did you overcome them?
B4A is a beauty company with technology in its DNA (we also call it a “beauty-tech”). We always strive to use the best technologies for the benefit of our ecosystem participants. Implementing solutions from Google Cloud was very beneficial to our processes and did not result in any additional challenges, nor resistance from the team. We actually had the opposite reaction, to be honest: infrastructure requirements and maintenance efforts decreased by more than 50%, which our IT Operations team very much welcomed. At the same time, and as I mentioned before, our customers also benefited from integrating Google Cloud, and specifically AlloyDB, creating a win-win situation for the organization.
What advice would you give people who want to start data initiatives in their company?
The starting point is the most fundamental moment. It will determine the success of your implementation. After all, a small difference in your steering angle at the start will make a big impact at the end. It’s not easy to set a course when you don’t know where you want to go. So, even if you are far away, you need to have a clear vision about where you want to go and a roadmap of how to get there.
With that in mind, I recommend preparing a data organization framework that will be able to support your plan. Even if you start small, you’ll need to set aside time to document, and cross-functionally review what you’ve envisioned.
Before you jump into action and develop a technology project, try to have a clear blueprint about your data structure. Map out all the data you need to track and try to predict what you will need in the future. The better you plan in the beginning, the better your end result will be.
What’s an important lesson you learned along the way to becoming more data-driven? Were there challenges you had to overcome?
I think in terms of data, we are different from most organizations. Our relationship with data has always been pronounced, and even our organizational structures are designed to use data in the best possible way, with data-focused squads accompanying many organizational processes. We already have a five-year track-record of extensive data usage at B4A. In the end, we need data for our main products to work, and we also sell it in an aggregate form to beauty brands.
The first important learning I already provided in my previous answer: above all, it is important to define data structures and oversee company-wide processes well before starting to implement an actual database. In this step, it is extremely important that business and tech teams work hand-in-hand. The business side needs to have a certain degree of technical thinking in order to make this integration work in a productive way. Overall, the better you plan in the beginning, the better your end result will be.
The second learning is more subtle and it’s something I only realized recently, after years of using data for everything at B4A. The learning is that looking at the data’s current trajectory, rather than envisioning its potential trajectory, can actually limit an organization.
Data-driven organizations can train themselves to assume past data will evolve in a linear fashion. The problem with this approach, when you are in technology, is that you often work on potentially disruptive products. Instead of just looking at problems in a linear fashion, you should also think of the potential exponential curves that may develop once your features or products achieve a sufficient degree of product-market fit.
This different behavior is often not considered when making projections using classical regressions or linear projections on top of data. Therefore, I sometimes provoke the team to look at the data from a different angle—an angle of where we want to go and how strong the disruptive effect of a new feature potentially could be in the market. In the end, the organization needs to find a way to combine both approaches and balance one with the other.
Another best practice is to avoid letting the organization become obsessed with certain key metrics, or KPIs. When looking at metrics, never forget that they often reflect simplifications of reality. The context in the real world can be more complex, integrating many more variables that should be considered to get a full picture of a situation. Applying common sense should always be more important than trying to judge a situation only using one or several metrics.
Thinking ahead 5-10 years, what possibilities with data and/or AI are you most excited about?
I think we are only at the very beginning of AI having an impact on productivity. Looking at five or even ten-year periods, it is difficult to predict the amount of disruption that will happen from AI. It’s already starting with generative AI, and I think over time, the entire Software-as-a-Service industry will end up being disrupted by more “Model-as-a Service” oriented companies. Instead of using a software product, you may be able to just ask the model to provide whatever you need at a specific moment in a very customized way.
At B4A, we always strive to be at the top of new developments. We consider how we can implement them for not only the interest of our ecosystem participants, but also the interest of our company’s efficiency. Again, looking at the next ten years, I think the disruption from data and AI will be immense, larger than anything we have seen over the last 50 years.
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