• Lenny Kessler

Building an experience

Investment intelligence to empower investors

I believe “CEO” should now mean “Chief Experience Officer”. I know the acronym “CXO”, but it should really be our responsibility as CEOs to make sure companies offer an optimal experience to customers, partners, employees and ourselves. Executive officer implies command and control, execution of orders and management of ressources. It can be very efficient and is perfectly adequate in many situations; think of a large organisation that needs to act fast like the army. Experience officer somehow implies people; the company is a node in a network of human beings that are stakeholders. This implies collaborative work of the type that is necessary for progress and innovation.

When we started WHYO, one of our ideas was to build a solution that would combine the complexity of specialised financial software and the simplicity of everyday electronics. Could we make a financial software that was as easy to use as our smartphones? We all have a computer in our pocket that is simple and intuitive. Why is it that things get much more complicated when we use our “real” computers at work? Why does it have to be so frustrating?

We want to offer investors the same empowering experience we are used to with our smartphones.

There are basically two types of software in the investment industry: the front-to-back accounting applications and the software used to understand our investments or the world around us. I call this second category Investment Intelligence applications by analogy to the Business Intelligence ones. For various reasons, including the fact that my business partner had already build a front-to-back system in the past, we decided to focus our efforts on building an empowering investment intelligence solution.

Many applications already exist and all offer a window into your data. At their core, they help you query your data: “what is the result of this calculus on that data set?” The question can be simple: how much cash do we have left? The calculus is “count cash” and the data set is “our accounts”. Or the question can be more complex: what is our exposure to the oil price? That calculus is “sum of” “correlations to oil price” for “each investment” in “our portfolio”. Increasing complexity a bit more: what were the drivers of our performance versus our benchmark year-to-date? We will spare you the “performance attribution” calculus here…

Even if we decide to use very complex calculus like artificial intelligence and very large data sets, i.e. big data, the structure of the question remains the same: “what is the result of this calculus on that data set?” It looks simple, yet most existing applications are complex due to a variety of reasons: legacy technology, centred on processes or products rather than users, trying to be everything to everyone, etc. Recently, one of my friends told me how a bank had a great new solution in which he could query their database in just a few lines of Python. That empowers data scientists, but leaves many on the sideline feeling powerless.

We want to empower every user. We want the user, to query data without the need to call in a data scientist and write a few lines of Python each time a question arrises. This is the experience we want to offer: the user is empowered by the system rather than slowed down as is too often the case. The user can be an asset manager, a risk officer, a private banker, an institutional investor, a final asset owner. We needed something that is easy to use, with as small a learning curve as possible.

So we had to rebuild everything from scratch.

Modern technology helps with the “must haves” of a professional solution: robust, interactive, scalable, available, open, up-to-date, cost-efficient, etc. We built a state of the art SaaS solution. But the user does not need to see this. His IT or purchasing department might want to check the quality of our solution, but the user’s problem is questioning data, not IT.

Focusing on the investment industry helps simplify the task as we are not trying to be everything for everyone. We can bake in our industry knowledge to make things more intuitive for the user. For example, when an investor looks at risk, we use trade dates. But if he is looking into the books, we use accounting dates (usually one or two days later). The user knows this and the system “talks” the same language, which makes things much easier. Designers would say the solution is build on the user’s mental model.

By keeping the user at the center of our reflexion, we can simplify things greatly: chose a portfolio (a typical investor’s data set), chose the indicator you want (the calculus) and voilà! From this simplicity comes the empowering experience that we allude to in our tagline “leverage simplicity”.

I recently wrote on the reasons why we started WHYO in “Consciousness versus Volatility ?”. Here, I delve a little more into what we actually do and the empowering experience we want to offer beyond the broad term of investment reporting. Next, I guess I will have to write about how we actually deliver that experience…

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