Alternative data (finance) – Wikipedia
Alternative data (in finance) refers to data used to obtain insight into the investment process.  These data sets are often used by hedge fund managers and other institutional investment professionals within an investment company.  Alternative data sets are information about a particular company that is published by sources outside of the company, which can provide unique and timely insights into investment opportunities. 
Alternative data sets are often categorized as big data,  which means that they may be very large and complex and often cannot be handled by software traditionally used for storing or handling data, such as Microsoft Excel. An alternative data set can be compiled from various sources such as financial transactions, sensors, mobile devices, satellites, public records, and the internet.  Alternative data can be compared with data that is traditionally used by investment companies such as investor presentations, SEC filings, and press releases.  These examples of “traditional data” are produced directly by the company itself.
Since alternative data sets originate as a product of a company’s operations, these data sets are often less readily accessible and less structured than traditional sources of data.  Alternative data is also known as “exhaust data. ” The company that produces alternative data generally overlooks the value of the data to institutional investors. During the last decade, many data brokers, aggregators, and other intermediaries began specializing in providing alternative data to investors and analysts. 
Examples of alternative data include:
Geolocation (foot traffic)
Credit card transactions
Web site usage
Mobile App or App Store analytics
Obscure city hall records
Social media posts
Online browsing activity
Shipping container receipts
Internet activity and quality data
Example of sentiment analysis against stock price (S&P 500)
Alternative data is being used by fundamental and quantitative institutional investors to create innovative sources of alpha. The field is still in the early phases of development, yet depending on the resources and risk tolerance of a fund, multiple approaches abound to participate in this new paradigm. 
The process to extract benefits from alternative data can be extremely challenging. The analytics, systems, and technologies for processing such data are relatively new and most institutional investors do not have capabilities to integrate alternative data into their investment decision process.  However, with the right tools and strategy, a fund can mitigate costs while creating an enduring competitive advantage. 
Most alternative data research projects are lengthy and resource intensive; therefore, due-diligence is required before working with a data set. The due-diligence should include an approval from the compliance team, validation of processes that create and deliver this data set, and identification of investment insights that can be additive to the investment process. 
However, the usage of the alternative data is not restricted by investment sphere, it’s successfully used in economics and politics as well as retail and e-commerce spheres. It’s possible to predict geopolitical risk through a profound alternative data analysis, while social media sites reveal a host of data for consumer sentiment analysis.
Alternative data can be accessed via:
Web scraping (or web Harvesting, performed by computer programmers that design an algorithm that searches websites for specific data on a desired topic)
Acquisition of Raw data
In finance, Alternative data is often analysed in the following ways:
Scarcity: the data Information overload within financial markets
Granularity: the level of detail and aggregation of data (including time)
History: the trajectory of data
Structure: the form of the data (csv, json etc. )
Coverage: the stocks or geographical locations that data can be linked with
While compliance and internal regulation are widely practiced in the alternative data field, there exists a need for an industry-wide best practices standard. Such a standard should address personally identifiable information (PII) obfuscation and access scheme requirements among other issues. Compliance professionals and decision makers can benefit from proactively creating internal guidelines for data operations. Publications such as NIST 800-122 provide guidelines for protecting PII and are useful when developing internal best practices. Investment Data Standards Organization (IDSO) was established to develop, maintain, and promote industry-wide standards and best practices for the Alternative Data industry.
Legal aspects surrounding web scraping of alternative data have yet to be defined. Current best practices address the following issues when determining legal compliance of web crawling operations:
Review of the terms and conditions associated with the websites crawled
Control over the potential interference with crawled websites
Web scraped data refers to data harvested from public websites. With 4 billion webpages and 1. 2 million terabytes of data on the internet, there is a mountain of information that can be valuable to investors when analyzing a corporate performance.
The companies that specialize in this type of data collection, like Thinknum Alternative Data,  write programs that access targeted websites and collect and store the scraped information on a periodic basis. In some cases web scraping requires use of public APIs as a way to access the data within those pages directly without visiting the actual website.
Types of web scraped data include:
Job listings: A company that is increasing hiring and headcount is likely experiencing growth.
Company ratings: Sites like Glassdoor allows employees to rate their company; increasing ratings, especially (in conjunction with increasing job listings) can be another growth indicator.
Online retail data: High product rankings on online retailers suggest strong sales for those product manufacturers. On the flip side, heavy discounting of products suggest weak sales. 
Standards Board for Alternative Investment (SBAI) is the global standard-setting agency for the alternative investment industry and guardian of the Alternative Investment Standards. The agency supported by approximately 200 alternative investment managers and institutional investors and collectively manage $3. 5 trillion. The SBAI has published the Standardised Trial Data License Agreement which addresses investment managers’ issues when comes to new data trailing process, like alternative data and big data.  Thomas Deinet, Executive Director of the SBAI said: “This Trial Data Licence Agreement template highlights a number of very important issues, including personal data protection, which has become a hot topic in light of the overhaul of data protection regulation in many jurisdictions. It also includes key protections for managers in areas such as prevention of insider trading and ‘right to use data’. It is crucial that managers and data vendors fully understand all risks when selling and using new data. “
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Alexander Denev and Saeed Amen, The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers (Wiley 2020)
Marko Kolanovic and Rajesh T. Krishnamachari, Big Data & AI Strategies: Machine Learning and Alternative Data Approach to Investing (JP Morgan 2018)
What is Alternative Data? – Examples of Alternative … – AMPLYFI
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.
How to Collect Alternative Data – Chain of Demand
Alternative data has made its mark in many industries, serving as useful data sources that help investors make smarter, better business decisions. It is steadily overtaking conventional and traditional data sources as a go-to for investment choices. There are plenty of different types of alternative data out there, but what’s interesting to note is the various ways of collecting alternative data.
Ways to Collect Alternative Data
Using alternative data allows traders to analyze portfolios and funds at a granular level of detail, with information from a huge range of data categories and industries. By understanding how this is done, it can help investors in alternative data approach the space with a more informed idea.
Web scraping is the general practice of extracting data from several websites on the internet. Scrapers or bots are mostly concerned with web pages and download relevant information, which is then processed through a collection of text processing functions. This information can then be extracted and transported in a spreadsheet or transformed into a form that can be very easy to understand. Web scrapers extract contacts and other details from a page. In marketing, web scraping is often used in lead generation, market analysis, price comparison, and competitive analysis.
Collecting Raw Data
Raw data is a collection of unstructured data in its original form but can be processed and used for greater insights. Sensors are one example of raw data that can be cleaned and used to gather market intelligence. Image processing is another important raw data that can be collected. The downside to collecting raw data is that there is a lot of time to be put into this. Oftentimes, raw data is not as valuable for investors.
Some companies can get licenses for collecting exhaust data. This is the data that is a by-product of a business process. Different companies can have different selling licensed exhaust data rates such as POS transactions, debit or credit card transaction details, etc. This data is then processed in a structured format and sold to various companies.
Challenges in Collection of Alternative Data
Collecting alternative data for investment is still a relatively new area to explore for businesses. For this reason, there are several challenges that a manager can face when trying to collect alternative data. Below are some common challenges in the collection of alternative data.
Non-Traditional Data Sources
Gathering logistics data that can quantify the shipping activities of a company is usually non-traditional. One problem that is observed while handling non-traditional data sources is the lack of expertise. If the company doesn’t have a department pro at collecting data from non-traditional sources, this might not be as useful as it can be for a company.
Collection of High-Quality Data
The collection of quality and valuable alternative data is perhaps the main challenge. There are various sources of alternative data out there, but knowing what’s useful and not can be the tricky part. One prevalent matter of collecting high-quality alternative data is figuring out its authenticity. One has to be vigilant about the source and legal accessibility.
Unstructured Data Sources
As mentioned above, one of the issues with getting alternative data is that many of it might come in unstructured form. Collecting unconventional data sources can only mean that the people who possess this data have not cleaned it properly. This means that there is a significant investment of time and resources to clean and process the data.
High Costs of Aggregated Transactions
When it comes to financial transaction data, there are often high licensing fees attached to them. Not only is the collection method are expensive, but they also require a lot of computing power. Each day, there are 2. 5 exabytes of data being generated, which requires a huge storage server, processing capacity, computing power, and analytical resources. And this is not even a fixed amount.
As stated earlier, alternative data can pose concerns for data privacy. There are various things to account for when collecting alternative data sources. You need to understand as a business whether or not you are breaching privacy during the collection phase. Attributes of alternative data change over time and, therefore, can be tricky to maneuver around. If a consumer is not critical about the privacy concerns while dealing with the alternative data, it will ultimately bode well for the company or organization.
Overall, the collection of alternative data, if handled well, can be of great tremendous use for a business. However, it’s important to be realistic with your expectations and be wary of all the challenges of collecting alternative data sources. Not only can it be time-consuming and resource-intensive, but it is also a sensitive space to explore.
For this reason, companies rely on data-analytics firms such as Chain of Demand to provide help and ultimately make data easy to use. With hundreds of millions of data sources processed, Chain of Demand has a wide range of data covered. It can help retail investors and hedge fund managers make business decisions right.
Frequently Asked Questions about alternative data sources
What is alternate data in finance?
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.Jul 8, 2021
How do you gather alternative data?
Ways to Collect Alternative DataWeb Scraping. … Collecting Raw Data. … Third-Party Licensing. … Non-Traditional Data Sources. … Collection of High-Quality Data. … Unstructured Data Sources. … High Costs of Aggregated Transactions. … Privacy Concerns.May 26, 2021
What is alternative market data?
More than 400 companies are engaged in selling alternative data to hedge funds, thereby contributing significantly to market revenue. Alt-data refers to undiscovered data that is not within the traditional data sources, such as SEC filings, financial statements, press releases, and management presentations.