Two different-sized firms both struggled with the exact same problem: the costs and time associated with setting up hardware and software infrastructure for trialing new datasets and investment ideas.
Firm A, a relatively small APAC asset manager, was considering creating a new team focused on quantitative investing. Before making this commitment and approaching the firm’s senior management, several analysts wanted to test their ideas. To do this, the firm required the implementation of new hardware and software, as well as access to several large datasets.
However, the small firm was reluctant to purchase the necessary hardware or third-party software and data licenses before obtaining the buy-in of senior leadership and committing to this new strategy. This presented a chicken-and-the-egg conundrum, as analysts were unable to make a convincing argument without the necessary infrastructure.
Firm B is one of the world’s largest asset managers, with over $1 trillion in assets. Though the firm’s equities team is supported by a large IT group and benefits from years of experience setting up new environments, corporate policy that allocates steep costs to requesting teams hindered its ability to implement a temporary infrastructure. What’s more, implementation time could range from weeks to months based on the firm’s existing queue and priorities. Firm B wanted to trial a single FactSet Standard DataFeed, without taking money from elsewhere in its budget for a temporary need. It also wanted to avoid delaying its projects while waiting for the internal setup of the required hardware and software.
Users at each firm were able to access proprietary and secure environments inside Microsoft Azure in a matter of hours. Each environment included only the hardware each firm required, alongside premiere software and data. FactSet Data Exploration provided a turnkey solution, and granted users across Firm A and Firm B access to industry-standard tools such as Microsoft SQL Server, MATLAB, Python, R Studio, and Tableau. In addition, all of FactSet’s Standard DataFeeds, as well as the third-party alternative data providers available through the Open:FactSet Marketplace, were made accessible for analysis and visualization.
Both firms were up and running within a day, with team members accessing state-of-the-art tools and all necessary data. Further, FactSet’s data science team was able to assist both firms in their initial navigation of the varied datasets with sample SQL queries and Python scripts. The FactSet support team walked users through the data dictionaries and the mapping of the various tables, helping to flatten their learning curve and empowering them to spend more time refining and testing their investment ideas. In addition, the data science team provided example data visualization reports that illustrated new possibilities for both firms, taking them down paths that they hadn’t previously considered.
Firm A avoided making a major investment during its trial, and was able to put its quantitative investment ideas to the test without incurring the risk of permanent costs that might not be ultimately necessary. With access to premier datasets and analysis tools, analysts were able to run their algorithms with the elastic computing power provided by Microsoft Azure. They used only the resources they needed, and only when required. After a few weeks, analysts were able to convince senior management of the efficacies of their work, and a new style of investing was introduced at the firm.
Firm B experienced an almost immediate turnaround of FactSet’s scalable environment, implemented and maintained by FactSet and hosted by Microsoft Azure. The firm was able to commence trialing the dataset it was interested in after only a few hours—a previously unthinkable outcome, given the weeks-to-months timeline the team had experienced in the past when introducing infrastructure to support new environments. What’s more, in the trial environment, FactSet provided not just the dataset the firm was researching, but all FactSet Standard DataFeeds. With the help of FactSet’s data science team and the sample reports and scripts provided, the firm discovered value in a completely different dataset than the one it had been considering.