Alternative Data’s New Gold Standard: Syndicated Scientific Consumer Research.October 1, 2021
Hedge Funds and Investment Banks are continuously being squeezed to decrease risk and increase alpha while also lowering their costs. As a result, over 82% of hedge funds are believed to be using some type of Alternative Data. Alternative Data refers to data that is not traditional data such as financial statements, management presentations or, SEC filings. The goal is to use alternative data to create an alpha edge through development of better forecasts or predictive analytics before their competitors.
To meet the need for Alternative Data, numerous data resources and platforms to find and access data have been launched. Most platforms act as grocery stores stocked with as many datasets as they can find. Many of the datasets are not vetted, and oftentimes contain unstructured, unusable, and even data not compliant with privacy regulations. Users are left to spend considerable time to vet and analyze the data and must assume it complies with various consumer privacy regulations.
One Alternative Data platform that is leading the way in bringing order to this this confusion is the Bloomberg Enterprise Access Point through their Data Catalogue. Making sure all data is vetted, compliant and organized for user access is foundational.
I asked Phil Rist, senior vice-president, strategic initiatives of Prosper Business Development to discuss how the Alternative Data market is evolving and why he thinks Bloomberg Data Market Place is one of the leaders.
Gary Drenik: What are some of the challenges alternative data buyers are facing?
Phil Rist: First, the rapid expansion of the Alternative Data market has driven the availability of data resources and vendors. There are over 445 data vendors now and that number is growing. By comparison, in 1990 there were only 20 vendors. Second, you must recognize that all data is not created equal. Once you realize this, you begin to understand the immense challenge faced by Alternative Data buyer/users who must separate the wheat from the chaff. It is time consuming and tedious figuring out which data may provide a real sustainable alpha edge. For example, many have used transactional data such as credit cards and others have experimented with web scraping or social media. While each of these data sets may present a degree of value sometimes, none are truly representative of a whole market. In addition, they represent an historical view that is one dimensional and requires several unverifiable assumptions due to the nature of their origin, a click or swipe. They also require a significant investment of time to vet and analyze. Last, Covid-19 disrupted the time series value for many of these data sets and their algorithms. The rapid changes in consumer behaviors have created a new consumer market not represented in old transactional data sets. The basic guidance all data for all scientists engaged in MLops…