Rhodri Preece, Senior Head of Research, CFA Institute, says emerging technologies can help investment professionals draw insights from unstructured ESG data.

Data is being generated at an exponential rate, and the technology powering the algorithms used to parse it is growing just as fast, opening up both new opportunities for investing and innovative ways to leverage alternative data. Investment professionals are now navigating a landscape supplemented by unstructured, alternative, and open-source data. A survey on alternative and unstructured data conducted by CFA Institute in July 2023 revealed that more than half of investment professionals are incorporating unstructured data into their workflow, and 64% indicated using alternative data. This shift has prompted a reevaluation of analytical methodologies and frameworks within the industry.

Over the past few decades, the predominant approach to financial analysis has centered on leveraging structured, numerical data. As the digital revolution continued, new alternative data providers sprouted up, capitalising on the notion of data being the ‘new oil’. The exponential growth of unstructured data boosted demand for methods to process and extract valuable insights, leading data science to emerge as a highly sought-after domain of expertise within investment firms.

Understanding data in financial analysis

The first level of distinction in defining the data used in investment decision-making processes is understanding the various generators of the data, which include companies, governments, individuals, and satellites and sensors.

Company data include, for example, financial statements, operational metrics, strategic plans, and data that arise when individuals or entities interact with the company’s products and services. Examples of such interaction data include credit card transactions, app download statistics, and email receipts. Government data include economic statistics on the health, performance, and status of a country’s economy, while government interaction data include data that are generated from the day-to-day functions of government activities, including business permits, patents granted, and public service usage, such as transport ridership and facility utilisation. Individuals generate data through their online activities, such as social media engagement, consumer reviews, and search engine queries. Lastly, technologies such as satellites and sensors generate data in the form of geolocation information, satellite imagery, and internet of things (IoT) devices, like manufacturing equipment usage patterns.

The second level of distinction is the type of data, which refers to whether the data is traditional or non-traditional. Non-traditional or alternative data is defined as any data that differs from traditional investment sources, such as financial statements,