Unstructured alternative data (alt-data) is challenging. Whether raw text with little or no structure, or semi-structured data such as textual news items like press releases, SEC filings, investor presentations, public records, ratings, job postings, product reviews, press releases, and social media, there is much noise when sifting through alt-data sets.
Alas, alt-data is invaluable as a powerful supplementation to more traditional financial data sources like stock prices or necessary company information in a business decision-making process. However, without correlating alt-data to more conventional data sources, it simply isn’t useful.
Financial firms exploring unstructured alt-data will find it is costly to collect, extract, store and analyze due to size and the effort it takes to maintain data. Multiple, disparate sources of unstructured data, must be normalized and cleansed of junk before analysis by AI can even begin.
Next, relevant entities can be extracted, sentiment can be determined, business-relevant signals can be identified, as well as any other things you'd like to extract, using a lot of machine learning, and human expertise. Despite these challenges, unstructured alt-data analysis is in demand for its ability to provide deeper insights and a competitive edge in the financial space.
At Bitvore we focus on using advanced AI techniques to aggregate, cleanse, tag and normalize massive amounts of unstructured data from multiple disparate sources, so your data science teams can get on with building predictive models to add value to your business, rather than spending 60-80% of their time doing what Bitvore does themselves.
At Bitvore, we tag unstructured alt-data with what we refer to as a ‘signal.’ A signal is an indicator that something business impactful has occurred and we use entity extraction to identify the company that experienced the event. When the company and the signal are combined, we are providing precision intelligence, a highly reliable indicator that something significant has happened to a company you are interested in.
In this downloadable FAQ, the following important questions are answered:
To discover more about how financial institutions can use this type of data to make better decisions, download the FAQ - Unstructured Alternative Data in Predictive Modeling.
Read more from our Chief Data Officer, Greg Bolcer