By Olaf de Senerpont Domis, senior editor at DataStax
Premji Invest is an evergreen fund formed to support the Azim Premji Foundation, which was founded by Azim Premji, the former chairman of IT services consultancy Wipro. Premji Invest deploys a “crossover format” (investing in both private and public companies) across the technology, healthcare, consumer, and FinTech landscapes; it has backed market leaders like Outreach, Sysdig, Heyday, Anaplan, Coupa, Moderna, Carta, Flipkart, Looker (acquired by Google), and DataStax. Premji Invest-US managing partner, Sandesh Patnam, established Premji Invest’s US presence in 2014 in Menlo Park, California.
We recently spoke with Sandesh to learn more about the firm’s investment strategy and his excitement about the database market.
What’s Premji Invest’s investment strategy?
We deploy a direct crossover investment strategy with a roughly equal split between mid- to late-stage growth equity and public equities. Our evergreen structure informs and supports our long-term duration investment approach: We think in 5- to 15-year horizons. While many investors view an IPO as a potential point of exit, we see the opposite. We’ve often participated meaningfully in the IPO events of standout members of our private portfolio and continued our partnership well beyond going public. We have a team in India and a team in the US, which I set up about 8-and-a-half years ago. We’re active investors and look to partner with the founders and management teams that are on a mission to create enduring companies.
What qualities do you look for in investments?
We want to invest in companies that thrive in the public markets. On the flip side, our public portfolio in many ways reflects our private practice conviction. We have deep private and public practices that operate under one hood, so it’s through this continuity that we understand the durability of a business model, pricing, quarterly cadence, value creation, and the rigor of a team. All these metrics are easier said than accomplished, but they’re a clear proxy for quality.
We also want to see significant product-market fit. I usually use the term “wild market fit.” A lot of companies can spend a lot of dollars and get a “push-based” model, but that can generate false positives. We want to see significant market “pull.” That requires some level of codification of the go-to-market strategy and process. A lot of companies have heroic sellers or unique customers — but still fail after lots of misspent dollars.
We’ve all heard software is going to eat the world. But more importantly, I’d say that AI [artificial intelligence] and ML [machine learning] are going to eat software. There are a lot of companies that build software that are often fairly basic workflow tools. For software to be actionable, data must be at the center. If you think of the way the world is headed with AI and ML, how is that going to get more intelligent? What is the basis of ML? In these cases, the most important aspect is: Can you organize your data and can you learn from it and piece together information in real-time that can take real action on that data?
There’s a second element that we look for: With the speed and volume at which we are accreting data, can it be stored in an efficient way? If so, your AI and ML can get better over time, so all software should be predictive in some way.
Why is the database market more interesting today than ever before?
The need for real-time information. Ecommerce, ad spend, and real-time events that happen once need to be moderated. If you don’t have the right instrumentation and analytics, provided on a real-time basis, you’re going to fall behind.
Think about the companies that have taken share and disrupted industries; think of all the things Amazon has done and Netflix has done — all the things that the tech challengers have done to existing businesses. They all stem from the fact that they were able to instrument their business much more so than others.
At Amazon, the office of CFO is, in many ways, more like the office of the CRO [chief revenue officer]. Amazon can instrument its business at the SKU level. Think about toothpaste with a particular flavor that’s sold in the Northeast. Amazon can tell you what the profit contribution is, what the cycle of buying is, who the potential buyer is, what the supply chain logistics look like. You start breaking that down at the SKU level, and that enables the promotion of certain products in certain geographies and enables you to make specific contribution margin decisions and make near-perfect promotions.
When you think about how that’s even possible, then you start understanding the power of AWS. But you also understand that underneath AWS is the power of a very dynamic, highly distributed database. The relevance of the right data model and the right database and the impact it has on businesses is much more pronounced today. You can say that data is destiny, but I’d add that the right database is destiny — it really impacts the business model in a very profound way.
Learn more about DataStax here.
About Olaf de Senerpont Domis:
DataStax
Olaf is senior editor at DataStax. He has driven awareness via content marketing roles at Google and Apigee. Prior to that, he spent two decades as a journalist, reporting on the financial services industry and technology M&A.
