Domain experts know what data they need but struggle to get it.
Domain experts such as asset managers, data scientists, market analysts, and others need a simple way to get relevant, accurate, and timely data from a vast array of documents from multiple sources. They know what information they need, can apply their expertise, and advise clients accordingly when they manage to get it. Asset managers, for example, may want to draw on non-financial and financial data from websites, news items, and social media, combining this with data from prospectuses, and annual reports, in their search for alpha, the ultimate investment.
High cost of data extraction – in manual effort, time, accuracy, and missed opportunities.
Unfortunately, data is rarely stored for convenient extraction, synthesis, and use. In many organizations, data is locked away in departmental silos and buried deep within large documents that come in different formats, both structured but often unstructured. It is not uncommon in large companies to rely on teams of researchers pouring over sizable documents and manually extracting relevant data, and transposing it into spreadsheets for analysis. This approach takes considerable time and effort and is prone to inaccuracies. The researchers may lack business domain expertise and inadvertently overlook critical data. Decision-makers and advisors then have a problem trusting the results, causing decision delays and missing time-sensitive opportunities, and sullying the firm’s reputation. Today’s clients expect asset and wealth managers to provide insightful and timely advice, particularly given the post-pandemic market volatility.