So, what exactly is alternative data in finance? It refers to data that exists beyond the sphere of traditional data sources.

A couple of the most popular traditional data sources are surveys and fill-in forms. However, you can only get a little bit of information from those. The main reason behind it is the fact that people do not want to answer lengthy questionnaires for your benefit. The more questions you ask, the better reward they want, and sometimes it becomes disproportional. As a result, relying on traditional data sources is no longer a viable solution in the age of digital domination.

As far as the sources beyond traditional measures are concerned, these alternative sources are utilized by investors, analysts, and hedge funds to aid in company and investment evaluation. Some brief examples of such data include public social media/sentiment, satellite and weather sensor data, transactional data from credit and debit cards, email receipts, survey data, and much more.

In increasingly data-driven financial markets, alternative data providers supply investors with fresh sources for enhancing their alpha. Alpha, also known as an active return on investment, measures the performance of an investment based on an appropriate market index. Ultimately, big data providers help investors with unique and timely insights for their investments, increasing their active return on investment.

Alternative data, sometimes referred to as exhaust or external data, involves data generated by three sources: individuals, business processes, and sensors. Whereas traditional data is exclusive to data that's produced by companies themselves, alternative data relies on the collection and web scraping services. For example, traditional data can be things such as investor presentations, SEC filings, financial statements, and press releases.

While both data types provide financial insights into a given company, the progress surrounding the alt data sector provided investors and hedge funds with a more complete picture of short-term and long-term investments and business decisions.

Over the past decade, big data solutions have grown significantly. In its infancy, alt data sources included credit card data, web-scraped data, and geolocations. As alternative data continues to prove its success, along with the advent of new data sources, experts in such data are finding new ways to retrieve these datasets as well as analyze them.

According to Forbes, alternative data has expanded to include a taxonomy of 24 different types of data. Beyond these 24 data types, alt data, as mentioned earlier, is generated from three main sources: individuals, business processes, and sensors. Let us take a more detailed look at what they entail.

Individuals

While individuals provide large amounts of robust qualitative data, alternative data generated by individuals also proves to be highly unstructured and often difficult to sift through. For this reason, it's important that datasets consisting of individual data are well-structured and that only the essential data points get picked. Some individual data sources include:

• Social media
• Product reviews
• Search engines
• Comments and web interactions

Business processes

Unlike individual-driven data, businesses tend to produce structured data that provides investors with comprehensive financial insights for a given business. Some business process data sources include:

• Credit card transactions
• Sales data
• Corporate data
• Government data

Sensors

Another largely unstructured data type, data generated from sensors, is expanding in the alternative data field. Due to the interconnectivity of devices and the development of sensor-based technology, analysts and investors alike find this data extremely valuable. Some sensor data sources include:

• Geolocation
• Satellites
• POS data
• CCTV
• Other devices

As mentioned previously, alt data is acquired through data harvesting, acquisition of raw data, and third-party licensing.

Data harvesting

Data harvesting collects unstructured data from web pages and online sources through web-scraping solutions. With the help of web data scraping services, you can gather a huge amount of data which can then be processed and prepared for use. By using data parsing methods, the data is transformed into structured and readable CSV and JSON formats ready for interpretation by analysts and investors. This is the sort of data that data providers, such as Coresignal, offer. In short, by using this technique you can get data from websites and other online sources.

Raw data acquisition

Raw data acquisition is the process of obtaining sizable amounts of unprocessed data, obtained from multiple source types. This data type contains lots of noise and requires significant processing in order to be analyzed and utilized by investors.

Third-party licensing

Third-party licensing refers mainly to recovered financial data involved in transactions. Big data companies recover said transactional data like POS data, credit card transactions, etc., and package it in easy-to-read formats for investors.

You can either select one of the data collection methods described above, or you can confide in an alternative data provider. Top big data companies have numerous datasets that you could use. The only thing you need to do is choose which one fits your needs the best.

Data providers such as Coresignal make sure that the data you get is fresh and relevant. Therefore, you do not need to be concerned about its accuracy. It can be conveniently delivered in comprehensible CSV and JSON formats. Also, the information you have gathered from one data source is trumped by many data sources that data providers have access to.

Our vast public company database, including people data, offers fresh and relevant information for improved business decisions. For instance, with our Company data API, you can gather insights into both company firmographics and people data. A company dataset will give you all the information you need regarding a selected business. Furthermore, our company data includes a company profile database, allowing for insights into the employees working there. In turn, you can use that information to enhance your business strategies.

Generally speaking, buying new data from data providers is more effective and cost-efficient than gathering the data on your own.

Collecting your own data requires an in-house team of data experts that can structure and interpret the new data, and hiring a team of experts can take a lot of time and resources. Those processes are required for the new data to be usable and valuable.

Data providers take care of that for you.

The only thing you need to do after making a purchase is to use it to boost your operations.

As a result, buying business data (or alternative data) is more convenient and useful in the long term than acquiring it on your own.

Due to alternative data's multiplicity of use cases, many professionals have found a use for alt data. There are various ways businesses and investors can leverage alternative data. Professionals using alternative data the most are algorithmic traders, also known as quants, who use the datasets they acquire to construct computer models for trade. However, alternative data is also utilized by hedge funds, analysts, and other institutional investment professionals.

Alternative data covers a broad range of factors that influence a company's alpha, making it a commodity for predictive investing. Specifically, investors consider factors such as scarcity, granularity, history, structure, and coverage when choosing what types of alternative datasets to invest in.

Investors utilize some of the most well-known alternative datasets, including social sentiment, credit card transactions, and web traffic data. These data types provide investment firms with competitive intelligence and risk management insights due to their pervasiveness in the alternative data space.

Additionally, financial firms find alternative data more favorable due to its accessibility compared to traditional, which sometimes takes longer (due to legal processes) for official figures.

Further, some widely used datasets such as credit card transactions and traffic patterns, are important to both private equity firms and hedge funds. On one hand, private equity investment decisions tend to focus more on the long-term, while on the other hand hedge funds focus on both long-term and short-term.

Both hedge funds and private equity firms benefit from alternative data companies because it provides robust short-term and long-term insights.