Datagrids and screeners proliferate across financial markets - and are often at the center of how market participants engage with data and make decisions. But research suggests that users struggle to get results from the tools they are using due to a wide variety of issues. The result - investors don’t get to the end of their stock or fund selection process - and fail to rebalance or trade. Institutional investors don’t generate the ideas that lead to outperformance, or bankers fail to spot the next IPO or M&A opportunity.
Fundamentally, screeners are tools that enable users to make data-driven decisions in a transparent and understandable way. The inputs are a combination of hard facts (the data) and assumptions and/or criteria. But the ability of the tool to deliver good results is largely driven by the usability of the tool, as it is inherently a human interface to potentially large and complex data.
Unlike other data analytics solutions, screeners typically lack comprehensive statistical analysis of the underlying data, unless such measures are already included in the data. Screeners are largely numeric, but can also include non-numeric data. The data is often sourced from multiple data sets.
Screeners also suffer from usability challenges - with antiquated designs, inflexibility, and poor interoperability. Meanwhile, the very act of touching the product can trigger the need to implement new and more stringent accessibility standards that at best are hard to interpret, and at worst almost impossible to apply to a grid or screener use case!
Datagrids and screeners proliferate across financial markets - and are often at the center of how market participants engage with data and make decisions. But research suggests that users struggle to get results from the tools they are using due to a wide variety of issues. The result - investors don’t get to the end of their stock or fund selection process - and fail to rebalance or trade. Institutional investors don’t generate the ideas that lead to outperformance, or bankers fail to spot the next IPO or M&A opportunity.
Fundamentally, screeners are tools that enable users to make data-driven decisions in a transparent and understandable way. The inputs are a combination of hard facts (the data) and assumptions and/or criteria. But the ability of the tool to deliver good results is largely driven by the usability of the tool, as it is inherently a human interface to potentially large and complex data.
Unlike other data analytics solutions, screeners typically lack comprehensive statistical analysis of the underlying data, unless such measures are already included in the data. Screeners are largely numeric, but can also include non-numeric data. The data is often sourced from multiple data sets.
Screeners also suffer from usability challenges - with antiquated designs, inflexibility, and poor interoperability. Meanwhile, the very act of touching the product can trigger the need to implement new and more stringent accessibility standards that at best are hard to interpret, and at worst almost impossible to apply to a grid or screener use case!
