The existence of every business entity is, from the very outset, to solve a problem standing out, so clients are willing to pay to get their needs met.

Working at a big company, assuming a particular job role, employees tend to forget what‘s the origin and the core competence of the firm.

I always like to ask this question: what is the core competence of Google (GOOGL), of Facebook (FB), of Bloomberg, of FactSet, of my team – index solution, and of myself.

Google has developed and owned the IP of the search engine, which by far is the only and most effective engine now, pushing Yahoo basically out of business. Even though Google never claims to be a monopoly in searching space, they are, according to Peter Thiel, purposely misguiding the mass to believe they are facing a lot of competition in the marketplace.

As for FactSet and Bloomberg, the business models are quite similar, providing a handy user view or analytical windows to conduct financial analysis. With the prevalence of these tools, obviously, add-on value is diminishing. With big data, machine learning, and artificial intelligence now in reality, being limited to hit buttons on the terminal is falling behind. Traditional fintech firms like Bloomberg and FactSet have to adapt and transform. Microsoft’s (MSFT) current CEO is catching up, making strategic moves of purchasing LinkedIn two years ago and Github today (June 04, 2018) for $7.5 B. Seeing the pioneer of windows creator – Microsoft – drifting away from their own foundational product, there is no reason for FactSet and Bloomberg to not consider carving out or diving into new space.

Index Solution group was created and grown now thanks to our distinctive approach, differentiated from the traditional indexers. This business is no longer about calculating an existing market make-up and weigh them according to each security’s market capitalization. The core essence of this business is to find “related” and “similar” companies in a fast and accurate fashion. There is no way to employ manual, fundamental research work, as has been carried out in last decades, to keep the edge. Our possession of a rich swathe of data and capacity to apply state-of-the-art machine-learning algorithms to parse data into executable financial investment vehicles are standing out and growing up.