Data models – The next generation of analytics
Firstly, it is important to clarify exactly what we mean by a model-driven business. While the term may be unfamiliar, it is a concept every reader will likely come into contact with on a daily basis. A model-driven business creates a virtuous cycle of data powering advanced analytics, leading to the collection of more data which increases the value of the analytics, leading to the collection of more data and so on.
Take Google, for example. If I search for restaurants near me, Google can see which of the results it surfaces I choose to click on. Then, when other people make the same search, it can use that data to provide them with a more relevant set of results, helping them get the information they want faster. Because it is so efficient, more people use its search, giving it more data to further improve its search and generate yet more relevant results.
This model is directly applicable to FX analytics. Banks which can use their transaction data effectively to generate analytical insights will be able to better service their clients causing them to win more business. This, in turn, means a larger share of the flow, and more data, moving through their business. The more data their analytics models have to work from, the more valuable the insights they can deliver. It creates a virtuous cycle of data and improved client service driven by analytics.
As a business model, this approach to analytics has been shown to be extremely effective in the world of consumer technology. As financial institutions recast themselves as technology and data-driven companies, the approach is sure to make an impact in the financial markets.
This approach has a key implication: it creates a strong early mover advantage. The firms which make moves into data analytics early will have a significant head start on those which come later.
With previous technological changes, a company can catch up quickly by buying in the latest technology and learning from the experiences of its more forward-looking rivals. With a model-driven approach, it isn’t just about who has the most sophisticated analytics. The company with the combination of advanced analytics and the widest pool of data to draw on has the advantage. Early movers quickly build up a data, as well as a technical, advantage. This creates a network effect which makes catching up more difficult.
If a new team of researchers launched a search engine with better algorithms than Google’s tomorrow, they would still struggle to deliver a better search experience because Google has around 3.5 billion searches every day, constantly improving its service. It is the same in any model-driven market.