The finance industry is full of rapid change and heated competition. Analysts need to stay a step ahead of their rivals to provide the latest information and trend breakdowns to clients. At the same time, they need to be careful that their assessments are backed by enough legitimate data to avoid too much risk to their clients’ investments. That’s why many financial analysts are now consulting sources of alternative data to reinforce their research.
But what is alternative data, and why is it useful to financial analysts? This guide will explain by covering where alternative data comes from, what forms it can take, where you can get it, what advantages it has over traditional financial data, and how different types of financial analysts can use it. Here’s a brief overview of the article:
We’ll start off with an alternative data definition so you understand a bit more about what alternative data is.
Alternative data refers specifically to data that is used in financial analysis, but is generated or collected in non-traditional ways. That is, the company doing the analysis doesn’t create the data itself, nor do they get it from official sources like press releases or quarterly earnings reports.
So where, then, does alternative data come from? The next section will explain.
In general, alternative data is produced by three types of sources:
Generally speaking, alternative data that comes from sensors or individual actions tends to be inexpensive to acquire. But it also often doesn’t come in very workable formats, requiring a significant amount of processing to be made usable. In contrast, alternative data that comes from corporate actions tends to require little processing and can be mined for insights almost immediately. However, it’s usually more expensive.
We discussed in the previous section that different sources produce different types of alternative data. While neither those mentioned nor the following list are meant to be exhaustive, here are 5 examples of alternative data categories that SafeGraph deals in.
People buy and sell things every day, and data on these transactions is one of the prime sources of alternative data. Companies may post some of this information publicly (like on their website or in earnings reports), but typically only do so when they are mandated to by law. However, there are other ways to access this data.
As one example, it could be possible to track a company’s sales and other transaction information through receipts they send out via email after a customer completes a purchase. In addition, many transactions these days are carried out through third party companies, such as financial institutions or payment processors (or both). So information on anonymous and aggregated debit card, credit card, and online account transactions may be available for purchase from the companies that facilitate them. This latter method is how we at SafeGraph compile our Spend dataset via permissioned consumer spending data for places.
Human mobility data refers to anonymized measurements regarding people’s movements within a limited geographical area over a certain period of time. At base, these include attributes like which specific places people visit, how many people visit, and how long people stay at one place before moving to another one nearby. It can also include places or directions from where people enter the area or to where they go after leaving the area.
Point of interest (POI) data refers to general information about non-residential places people may want to visit. Sometimes, these are monuments or other landmarks that attract tourists and other visitors. Often, however, they are places where people can buy or sell products and services. A few common attributes are hours of operation, price range, affiliated brands, product/service classification, street address, and contact information. Our Places dataset contains reliable, accurate POI data that is regularly updated.
Property details refer to information about a parcel of land or any building(s) on it. It can include attributes like ownership and financial details, including leases, mortgages, previous sales, and assessed value. It can also include specifications about particular buildings, such as square footage, number of rooms, HVAC specs, construction materials, and so on.
Another important attribute is the property or building footprint(s) - visual representations of the actual physical dimensions the property and its building(s) take up. This can include spatial hierarchy metadata of buildings that are separate units inside a larger building, such as apartments, mall stores, or offices in a business complex. Have a look at our Geometry dataset to see what we mean.
Demographics data is aggregated information about people in a neighborhood, city, state, country, or other geographic region. That includes attributes such as age, ethnicity, sex, income, employment status, marital status, and highest level of education achieved. Demographics data is important because it gives a general overview of people in a certain area, in terms of what their lifestyles are like and (therefore) what they are likely to spend money on.
Much of this data is publicly available for free, but it isn’t always easy to access and organize for alternative data analysis. That’s why SafeGraph offers a cleaned-up version of data from the US Census Bureau’s American Community Survey for the years 2016 to 2019.
We still haven’t answered a very important question: why use alternative data at all? Why not just rely on financial data from official sources? As it turns out, there are at least four very good reasons to factor alternative data into financial analysis:
Now that we’ve established advantages to using alternative data analytics in finance, we’ll discuss examples of how investors might use it.
Alternative data can be used for a number of different functions in financial analysis. But how exactly it’s used can depend on whether an analyst is working for a specific client, or for a more public-facing firm that services multiple clients simultaneously. These are commonly referred to as “buy-side” and “sell-side” positions; we’ll briefly explain more about them below.
Financial analysts typically fall into one of two categories: buy-side and sell-side. They both use alternative data in similar ways; their main differences are in who they work for and what their roles are.
Now that we’ve established some basic differences between buy-side and sell-side financial analysts, let’s look at how each type of role might use alternative data.
As we mentioned, alternative data use cases can differ depending on the type of role a financial analyst plays. But there are quite a few use cases that can benefit both buy-side and sell-side investors. Here are eight examples.
Role type: Both
A lot of things are being done on the Internet these days, so it makes sense to pay attention to what goes on there. Alternative data such as online transactions, web traffic, and mobile app use can give clues as to what people are interested in. So can comments on social networks or news sites, or online product reviews. Analysts may even be able to gain insights from data on logistics companies, as shopping from home is becoming increasingly common.
Role type: Both
Using alternative data to account for geospatial relationships between businesses can also be a useful financial analysis strategy. Certain businesses that cater to similar lifestyles, but don’t directly compete, tend to do well when they are close and accessible to each other. So it’s important to take these complementary business relationships into consideration, rather than just competitive ones.
Role type: Sell-side
Certain kinds of alternative data can help sell-side analysts hypothesize if demand for various types of products or services will rise, fall, or remain steady. Online social sentiment and footfall counts around brick-and-mortar stores can be useful for this, but generally transaction data is a clearer indication. Points of interest data can be helpful, too, if it includes attributes on what kinds of stores are opening or closing in an area, and how many.
Role type: Buy-side
Buy-side analysts can use alternative data to look at how consumers interact with stores and brands from different angles. They can look for things like competing or complementary points of interest in an area, how much foot traffic an area gets at certain times, how many people enter specific stores, and what brands are popular at stores relative to the ones they stock. This research can become even more informative if done across areas with similar geography, or in the same area within comparable time periods.
Role type: Sell-side
Measures of consumer demand are just a part of modeling how a potential investment’s financial situation will change in the near future. Alternative data can allow sell-side analysts to factor in other things such as supply chain performance, online vs. offline sales, sales in areas with similar geographic profiles (e.g. points of interest, demographics, and human mobility patterns), social sentiment. Overall trends in an industry, or in related industries, can also be indicators of how an asset will perform for the foreseeable future.
Role type: Buy-side
Being able to assess potential investments on multiple levels is also important to buy-side analysts when they perform due diligence. They often deal in very large sums of money, which leaves little room for error. So having a greater volume and variety of data with which to examine all of an investment’s potential benefits and risks is a huge boon.
Role type: Sell-side
Sell-side financial analysts are often expected to collect, analyze, and distribute reports on financial information quickly to keep up with competing firms. Since alternative data is usually fresher and more frequently produced than traditional financial data, it can be very helpful in this case. To illustrate, an investor may follow the social media accounts of particular companies to stay on top of their announcements and gauge how customers feel about them. Or they could look at POI data to track store openings and closings in a particular geographic area as an alternative measure of a company’s or industry’s performance.
Role type: Buy-side
Even after investments are made, buy-side analysts need to monitor the market to ensure that assets are performing as expected. This is where being able to get timely and frequent updates from alternative data sources comes in handy. It allows analysts to spot, assess, and take action on unexpected changes quickly before they potentially result in huge losses.
Alternative data is a pretty broad field, so it should be little surprise that there are all sorts of different alternative data providers out there. But you want the ones that are going to get you the data you need at a price you can afford. This section will cover things to look for and questions to ask, as well as introduce you to reliable alternative data companies that you can count on for certain types of data.
Whether or not you choose to buy from particular alternative data vendors should depend on more than just them having the kind(s) of data your organization needs. Here are points to consider to help you avoid compromising between quality and quantity of data.
Scope: Does the supplier provide data in enough breadth or depth that you can make as objective an assessment as possible?
Attribution: How much – and what kinds of – information does the supplier provide about each data point?
Accuracy: Does the data convey precise and correct information?
Freshness: Is the data current enough that it’s still relevant to present circumstances?
Interoperability: How easy is the data to work with, especially in the context of connecting it to other datasets for broader analysis?
Cost: Are you getting value for money by only paying for data that’s relevant to what you intend to use it for?
Other questions you may want to ask about alternative data suppliers you’re thinking of sourcing data from include:
We know that can be a lot to process, so we’ll make things a little simpler for you by listing some alternative data firms we trust.
There are companies out there that specialize in collecting and processing different types of data. Their goal is to make it easier for analysts to get the insights they need without having to do a lot of legwork. Here are our top alternative data companies for sourcing the information you need to make financial decisions faster, more accurate, and more creative.
Major data types: POI, property, transactions
Key use cases: retail investment, consumer insights, risk assessment, real estate site selection
SafeGraph is the market leader in global POI data. Our Places and Geometry datasets contain detailed information and building footprints for millions of locations worldwide. Our Spend dataset is the first US consumer transaction dataset that’s based on where people spend money, to give context to when and how they spend it. Use these datasets to analyze the relationship between consumers and retail stores, compare building locations with human traffic to assess accident risk, and more.
Major data types: US properties and historical weather patterns
Key use cases: real estate investment and risk assessment
ClimateCheck is a valuable resource for those investing or insuring in the US real estate market. It processes historical US weather data through over 25 internationally-recognized climate change models to predict weather and climate patterns for the next 30 years. The resulting data offers snapshots for over 140 million US homes of how vulnerable they are to the potential effects of climate change – droughts, storms, fires, floods, and more.
Major data types: financial, labor statistics
Key use cases: workforce analytics, talent acquisition and management
Like the “.HR” in its name implies, Greenwich.HR lets you look at companies’ financial viability from a human resources perspective. See how many positions are open, what kinds of workers are being sought after, what salary ranges are like (for over 80% of jobs), and more at over 5 million companies from over 200 countries around the world.
Major data types: UK points of interest, properties, and addresses; PDF document analysis
Key use cases: real estate investment, insurance risk assessment, logistics planning, fraud prevention
HARNESS DATA is one of the best alternative data sources hedge funds investing in the British Isles can consult. It has the most complete information on addresses, properties, and points of interest in the UK. That includes a free price per square meter assessment of over 16 million properties in England and Wales. It also has a software tool that can scan PDF documents for actionable, industry-specific data points.
Major data types: property, demographics, phone & email communication, automotive & other transactions, addresses
Key use cases: real estate
Infutor is one of the top alternative data providers for insights on US consumers. It offers a variety of alternative data on the US including demographics, address and property information, automotive transactions, online transactions, and email & phone communication metadata. So it’s a good source for getting a general overview of who US consumers are, where they spend money, and what they purchase. It can also be useful for real estate or automotive investment.
Major data types: vacation rental property
Key use cases: travel & tourism investment, hotel investment, real estate investment
Transparent tracks public data on over 35 million vacation rental property listings across all major rental property booking platforms. Its dataset includes over 50 attributes on each listing including its address, number of bedrooms, occupancy limit, minimum booking period, and price. Monitoring the supply, demand, and competition in this market is useful if you’re investing in real estate, hotels, or travel & tourism.
Major data types: property, mobility
Key use cases: visit attribution, consumer insights, site selection
Veraset aggregates its data from multiple sources to deliver accurate footfall measurements around major points of interest in over 150 countries worldwide. It also has a more precise foot traffic dataset for the US that includes building footprints of over 6 million points of interest. This makes it easy to tell if someone actually entered a building or just walked past it.
Major data types: online transactions, rental property, transportation, business summaries, points of interest
Key use cases: real estate investment, corporate research, retail insights, travel & tourism investment
Vertical Knowledge sources public data on the Internet and processes it into a privacy-compliant form. So it has lots of different alternative data examples: lists of best-selling books, air travel statistics, cruise metrics, company summaries & reviews, retail locations & information, short-term rental property details, and more.
