Learn about how Opensee brings data and analytics to the hands of the users, enabling them to improve their business intelligence.
Our corporate ambition is to get to a point where using huge amounts of data is as simple as clicking a button. We explain in the latest podcast Opensee: Streets Talks To why we think this is achievable, in spite of the complexity of the data journey.
Opensee started because we couldn’t find an analytics tool that allowed us to leverage all the data we wanted to use. At the time, we were working on bank trading desks and were frustrated that we couldn’t run valuation adjustments for derivative instruments under certain ‘what if’ scenarios. We set out to build our own. Since then, we have brought solutions to banks and other financial institutions as mounting regulatory demands called for specialist and in-depth analysis of vast volumes of data. Opensee’s superfast analytics tool allows any user to unlock the value from 100% of the data and to turn it to their advantage, by acting significantly faster than ever before.
As experts in banking and technology, our focus on the business needs of clients has allowed us to build everything from the ground up and to simplify the analytics tool for users, so they can slice and dice the data on demand, unlimited by the level of detail or history. As regulations in banking and finance become increasingly standardised across the world, we’ve developed editable Python modules that are easy to implement, giving users complete flexibility to use the calculations embedded in the solution or to create their own.
Banks and financial institutions have invested massively in storing risk, liquidity, trade and market data. They are now looking beyond the regulatory reporting imperative and into how they can extract more value from all their data to improve the way they do business. Take the example of Best Execution, which is about being able to demonstrate to the regulator that you did well on a trade after the fact. This requires analysis and reporting. Now clients want to go deeper by using the data to find out how a trade could go even better in the future. Opensee allows them to inject their own business intelligence, using machine learning models, for example.
We started out to provide real-time analytics for the data collected by banks and financial institutions and often found ourselves looping back to optimise every single step in the data journey. Having taken the data analytics tool to where it is today, we find ourselves looping back again to the start of the data journey as clients focus on data ingestion.
One exciting area where this is happening is in ESG (Environmental, Social and Governance) data. The growing pressure on corporations to report and act on their commitments presents huge challenges in sourcing the right kind of data, monitoring ESG performance and running climate stress tests. From a data point of view, the question for them is how to combine fragmented data, which may be in the form of a physical or a financial data set.
Usually financial institutions take data from different sources, needing it to be structured to facilitate the analytics and the data models. Cleaning and analysing the data is a critical need if the business intelligence produced by the analytics is to add value. Clients want us to work with them on simplifying the data ingestion. While many have their own algorithms, we are bringing business intelligence tools to help them accelerate this data journey. This means connecting to the data sources and then structuring it at the point of ingestion. The second phase of cleaning is the ongoing daily activity to weed out the errors in the data sets. The platform has been designed to be completely data agnostic and to grow with the requirements of the client.
The click button journey of the data collected by banks and other financial institutions is where we are heading. We have already travelled such a big part of this long and complex journey already, that the end of the road lies just ahead. With some of Opensee’s clients, we think we've already done it.
Guillaume Félix and Christophe Rivoire have written a compelling blog on the benefits of a well-thought-out risk data journey strategy for financial institutions. Find out why it is incredibly necessary, here.