Companies that are slow to incorporate alternative data into their research and development (R&D), marketing, investment, risk analysis, and other key processes expose themselves to lost opportunities at best and operational peril at worst.
As one prime example, active investment management firms, including hedge funds and even private equity funds, incur the strategic risk of being outmaneuvered by competitors leveraging alternative data in their securities valuation and trading signal processes. This has emerged as an essential tool for investment management firms seeking market outperformance, known as “alpha.”
The last decade has ushered in a myriad of new types and sources of alternative data. Unlike traditional data made available by financial exchanges and indexes, Security and Exchange Commission (SEC) filings, financial statements, corporate filings, analyst predictions, press releases, management presentations, and other well-entrenched mainstream sources, today’s breed of alternative datasets are being compiled from wide-ranging and disparate sources — everything from financial transactions, satellites, sensors, and IoT-enabled devices to e-commerce portals, public records, mobile devices, social media, web traffic, and more. However, web scraping and financial transactions are, by far, the most common methods of alternative data procurement.
So lucrative is the economic upside of alternative data assets, the category is experiencing a veritable gold rush mentality that is driving extreme growth worldwide across practically every industry sector. In fact, the global alternative data market size is expected to reach $143.31 billion by 2030, a staggering increase from $2.7 billion in 2021, according to Grand View Research.
Investment firms are actively expanding their informational advantage by incorporating alternative data into their investment and risk processes. An EY Global Alternative Fund survey found the majority (70%) of hedge fund managers and more than half (56%) of private equity funds currently use or plan on using alternative data to support their investment process.
The banking, financial services, and insurance (BFSI) industries collectively led the alternative data market in 2021 with a revenue share of more than 15% during the period. However, there are other early adopters of alternative data. A few notables making great strides in the space include online retailers, SaaS purveyors, and the hospitality sector. These, and other such industries, are tapping into the power of this alt intel for an array of projection activities with predictive and algorithmic modeling, demand and trend forecasting, lead generation, and competitive intelligence among them.
“There are numerous categories of alternative data, and the businesses that fare best are those with the capability to mine insights from the collected data and cross-reference and combine it with other types of data, thus enabling investors to identify profitable trends and strategic opportunities,” said Julia Valentine, managing partner at AlphaMille.
According to the Alternative Data Global Market Report 2022, North America was the largest region in the market in 2021. The main categories of alternative data are credit and debit card transactions, email receipts, geolocation (foot traffic) records, mobile application usage, satellite and weather data, social and sentiment data, web-scraped data, and web traffic.
“The driver behind this phenomenon is twofold: investors’ appetite for using the data and the providers’ willingness to sell credit card transaction data,” Valentine said. “Moreover, data providers have been enhancing their capabilities of sorting credit card transaction data by gender, age, seller, geography, and other metrics.”
Of course, these types of drill-down insights can make it much easier to identify and evaluate opportunities, especially when advanced analytics and data science are applied to examining alternative data sets.
According to Valentine, these offerings produce a crucial differentiator, generating alpha for buy-side entities, like hedge funds, mutual funds, private equity funds, pension funds, unit trusts, and life insurance companies.
"It’s essential for investors to have curated alternative data to make their teams, innovation, and companies more competitive,” said Tracy McWilliams, CEO of Inspire Global Ventures. “Machine learning-enabled alternative data analytics assist our clients, mid-market companies, and investment firms make faster and more informed decisions about investments, innovation, M&A [mergers and acquisitions], and partnerships with early-stage and private placement companies.”
The benefits of employing alternative data are seemingly innumerable.
“Among the most important is its ability to derive proprietary, real-time signals, providing alternative viewpoints, unforeseen insights, or perhaps both,” Valentine said. “The ability to go beyond standard financial data to understand company performance, market dynamics, or consumer behavior is extraordinarily valuable for companies and investors who desire to plan and execute in a calculated, enlightened, and intentional way with mitigated risk.”
Even amid the extreme upside, a number of challenges plague processes for incorporating alternative data into the investment and risk models.
“As compared to the traditional financial data collection, alternative data assets are known to be unstructured; lack specific patterns; and, given its high collection frequency, require significant storage and processing resources,” said Vita Koreneva, managing partner at AlphaMille.
“Collecting and analyzing alternative datasets certainly requires navigating any number of difficulties or outright obstacles,” Valentine said. “This includes the procurement of expert personnel and cutting-edge technologies, like analytics, fluid data architecture, and data science platforms as well as testing tools to actually leverage meaningful insights gleaned from the data. For example, AI tools, such as ML and natural language processing [NLP] are used for analyzing alternative data, unlocking its insights and value, and boosting the growth of these assets. ESG [environmental, social, and governance] data is an key example of alternative data where multiple providers in the public markets are supplemented with the use of multi-modal AI to collect data used by private markets that is unavailable through existing data providers.”
According to Valentine, starting or enhancing an alternative data platform involves multiple steps: designing, planning, sourcing data, integrating, transforming, using ML, deploying, supporting, and evaluating. A shorter, five-step implementation model is also available for entities that are ready for a fast route to value creation.
With such specialized tools and skill sets involved with mining and distilling alt data, many understandably outsource the function.
“A few key considerations for a prospective professional services partner involve their ability to quickly integrate new solutions with existing infrastructure; cost of data feeds; and proving what they deem to be optimal, uncorrelated datasets genuinely add quantifiable value rather than noise,” Valentine said.
They should also demonstrate an aptitude for the key requirements of an alternative data platform, such as rapid and efficient onboarding of data sources; combining structured, semi-structured, and unstructured data sets; and data preparation and normalization, among others.
“Data mastering is fundamental to gleaning insight from this seemingly limitless universe of information,” said Christian Robertson, CEO of Datasynthesis. “It means tracking the data life cycle from its source — be it real time or historical, structured or unstructured — through a strict, rules-based validation process, generating actionable data used to feed the various business intelligence tools used in decision-making. However, to distill meaning from so much information, one must adopt an active data mastering approach, which can only be achieved by leveraging the latest open-source technologies with capabilities that far exceed anything possible with existing legacy systems.”
“Alternative data hasn’t nearly reached critical mass as of yet, and there is tremendous growth ahead in this space,” said Rick Lutz, chief revenue officer at AlphaMille. “The big winners will be those that onboard the right ‘kind’ and caliber of experts who can adeptly navigate this highly specialized and ever-changing field. Done right, the financial upside is stratospheric.”
Digital transformation demands agility. Companies that can quickly identify and adapt to ever-fluid business conditions both survive and thrive. Having the ability to adeptly procure and process alternative data provides a tremendous advantage, especially for those needing to pivot in the short term.
No matter the industry in which you operate, now is the time to architect a sound and scalable alternative data plan, ensuring your company can keep pace in the 21st century digital age.