Alternative data is auxiliary financial information useful for making investment decisions away from official or corporate sources. Used together with information from traditional data sources, alternative data give investors a full picture of an investment opportunity. Examples of alternative data include unstructured data emerging from a company or a person’s activity, public records such as non-farm payroll, mobile device data, Internet of Things (IoT) sensors, credit card transactions, point of sale transactions, website data, online browsing activity, product reviews, internet activity, app store analytics, ESG data, satellite imagery data and social media sentiment data.
It is widely recognised that big data is another essential factor of production in the modern world, as much land, labour, and capital were when early political economist Adam Smith was defining his classical theory of economic growth. Some argue that data is replacing labour and technological advances diminish the importance of land in the process of creating wealth. In finance, alternative data, a subset of big data, is used to provide competitive insights unavailable in traditional sources of investment information such as SEC filings, financial reports or market data.
The examples of alternative data mentioned above show that data is wide in breadth and provides a better predictor for trends and asset performance. Insights drawn from the data are profound, with implications spanning multiple assets and industries. Analysis and consumption of alternative data are made possible by AI technologies that collect, organise, and process raw unstructured data and use it for query or building applications that make evidence-based recommendations for users.
But there are several hurdles that institutions must overcome before they can benefit from alternative data. First is the vastness of it. Collectively, the world produces an estimated minimum of 2.5 quintillion bytes of data daily. In a corporate environment, only 2% of this data is used, while another 95% is stored in a non-uniform unstructured format. Meaning, different institutions have to work through the maze to find the data relevant to their industry and unique insights it will yield to help maintain their competitive edge. After identifying what’s appropriate, functional and unique, institutions must organise the data to speak to the nature of assets and companies of interest. Lastly, collating the data, entities must query and determine if the data will reveal alpha opportunities.
The second challenge concerns investors who seek to determine the sustainability of investments by leveraging alternative data in the form of ESG data. With 85% of S&P companies publishing sustainability reports in 2017, ESG is gaining considerable traction. However, sifting through the noise and building the data sourcing process with an inbuilt mechanism for eliminating greenwashing companies may pose a significant challenge for investors. The problem is compounded by the absence of a global standard for reporting ESG metrics, meaning companies can choose what and how to report. ESG data is published data across multiple publications, including company websites, annual reports and emission disclosures. Therefore, investors run the risk of missing or misinterpreting the sustainability of an investment.
However, investors can leverage technology, specifically tools developed by artificial intelligence experts like AMPLYFI. AMPLYFI has expertise in building intelligent machine learning tools that use unstructured data to enable users such to make evidence-based decisions to either move forward or change with conviction. Our DeepInsight tool structures sourced data and applies machine learning algorithms to extract information and generate unique insights. DeepResearch, on the other hand, searches over 400 web, paid and internal sources in one click, summarising results using machine learning, saving institutions up to half of the secondary research time. AI-driven tools such as these can be incremental in helping organisations create value from the vast amounts of data available.
