What is Data Scouting?

Data scouting is the process of finding new and interesting data sources that can help companies advance key business objectives, whether that’s gaining an advantage over competitors or generating alpha within their investment portfolios.

There are two main aspects of data scouting.

1/ Firstly, it's about trying to find new, interesting sources of data that allow companies to understand more about the problems they’re trying to solve. In the corporate world, companies typically call this type of data external data, which is defined as data that comes from outside a company’s existing data sources. In financial services, firms call this type of data alternative data, which usually means any type of non-market data.

When scouting data, companies are trying to find interesting new data that's relevant and can be additive to existing workflows. They’re looking for data that can provide them either completely new insights, or a new lens on the insights they’re already deriving from elsewhere.

2/ The second aspect of scouting data is to then try to understand how these new sources of data actually solve the organisation’s problems.

Where to Start?

In the corporate world, many companies start with the data that they already have internally; however, this is only useful to a certain extent. Internal data gives very limited insight into things that happen outside of a company. For example, it cannot provide insights into what a firm’s competitors are doing, macroeconomic events or things like consumer sentiment – and any window it does provide is not going to be very timely.

If a firm doesn’t have access to internal data or wants to expand its insights outside of itself, the next step is to explore traditional data sources. For many financial services firms, an essential first step is to use market data to inform decision-making processes. Market data allows participants to see a security’s price information and see historical pricing trends for instruments such as equities, fixed-income products, derivatives, and currencies.

On the corporate side, traditional data can come from larger data providers and provide insights into a company’s financial performance, retail strategy or customer information.

Pursuing these types of traditional data sources is a great first step. However, many firms decide to ‘level up’ their insights through the exploration of alternative or external data sources.  

Scouting Alternative Data (or External Data)

Once firms have explored internal sources of data, as well as traditional or market sources, many decide to look for an edge elsewhere. That’s where alternative data (a term more common in the financial services world) or external data (more common for corporations) comes in.

Because alternative data is often less accessible than traditional data, it is frequently more unique, delivers more timely insights and can provide more granular insight into behaviours. For financial services firms, it has the added benefit of generating alpha, which means that it provides insight that’s so valuable that it can drive higher returns in a financial portfolio (as compared with how that portfolio would have performed without using the insights generated from that alternative data source).

At Neudata, we divide the alternative data landscape into 19 main categories: crowdsourced, economic, ESG, event, financial products, fund flows, fundamental, Internet of things, location, news, satellite & aerial, search, sentiment, social media, surveys & polls, transactional, weather, web- & app-tracking, web-scraping.

One benefit of using alternative datasets is that there is typically more than one use case for each dataset. Shipping data, for example, can help track commodity movements around the world, but it also generates exhaust data that can be useful for linking together company supply chains. Looking for the applicability and tangential use cases of data is important – it’s also what makes alternative data superior to traditional data.

As firms evaluate more of the data providers that exist, challenges arise. It becomes more difficult to:

1. a) find new sources of data, and
   
2. b) find data sources that are relevant – there are millions of providers selling similar types of data, but the ones that are providing valuable data are significantly lower.

Data Scouting Services

Naturally, many data scouts will first seek data from very well-known providers, like Bloomberg, FactSet and more. These providers have been around for a long time and are trusted in the market to provide reliable data, based on their reputations and known sourcing strategies.

However, many different types of firms exist that can provide data and other, less well-known providers may provide a data source that’s better suited to your specific goal.

Things to consider when deciding upon a provider are:

• Where does the data come from?
• What does the data measure?
• What are its applications or use-cases

These questions can help determine what data – from which provider – will be the most effective. Data scouting firms like Neudata exist in order to streamline this process by finding the data, confirming its validity and delivering research on a dataset’s key attributes, strengths and weaknesses to the person looking for data.

Ultimately, using a data scouting service is the easiest way to discover new, untapped sources of data that your competitors aren’t using yet. Many data scouts also go directly to data providers – however, actually finding the new sources of data can sometimes be the most challenging part of the data ingestion process. The hardest types of data to find are often the most valuable, primarily because they are less well-adopted in the market.

Data sourcing firms can also help bring new data sources to market – frequently, firms like Neudata will approach companies that they believe have interesting data and will explain the benefits and challenges of selling to a specific vertical. In that way, firms like Neudata will often receive advance notice when a new firm begins monetising its data to a new vertical.

Questions to Ask When Scouting Data

It’s crucial to understand data scouts must ask a lot of questions before deciding to buy a dataset. Scouts are typically looking to understand how reliable the data is, how much value it will provide and whether it’s compliant. Scouts will also need to understand factors like the dataset’s frequency, history, price and more.

Below is a list of questions that firms should ask when interviewing a new data provider:

• What is the data product that you’re offering?
• Where does the data come from and how do you collect it?
• Do you process and clean the data? If so, how?
• How are you converting the raw inputs into outputs?
• Do you have permission to resell this data? Can you show us the chain of consents?
• What are the applications or use-cases relevant to this data? Are there any tangential use-cases that this data could be relevant for?

The Importance of Knowing the Data’s Source

It’s imperative that data scouts know the source of the datasets they are buying, as well as the strategies that companies use to collect the data. However, many data providers do not voluntarily give up this information. Many firms will tell data scouts that that information is proprietary.

However, it is important for firms to understand where their data sources are coming from, both from a compliance perspective and from a data utility perspective.

• From the compliance angle, companies must know that their data partners have permission to collect and resell the data.
• From a data utility lens, knowing where the data comes from gives insight into how reliable it might be and how they might be able to use the data internally.

What are the Most in Demand Types of Data Currently?

Popularity of datasets depends on a user’s use case and the industry in which they operate. There are some types of data that have seen to be consistently very popular, primarily consumer transactional data, which shine a light on consumer spending behaviour and therefore have a wide variety of use-cases across many industries.

ESG data is also constantly in demand and datasets that shed light on activity in China are also extremely popular. This is partly because of China’s strict data export laws, but also because of the size and influence of China’s economy on world markets. And while many ‘global datasets’ will cover US markets by default, data users typically need to proactively seek out data coverage in markets like China.

What does the Future Look like for Data Scouting?

The market for scouting new and interesting sources of data is growing, particularly among emerging manager funds, private equity and venture capital firms, consultancies and corporations. As more firms discover the benefits of alternative data usage, many new suppliers are likely to enter the market (particularly as everyday life becomes more digitised).

However, the quality of new data sources isn’t automatically guaranteed. While a vast number of providers offer data, only a small handful provide data that is useful, reliable and compliant with legal regulations.

At Neudata, our team of 18 research analysts works incredibly hard to find new sources of data that are additive to your data scouting process, applicable to your use-case and gives you an edge. The market will continue to grow and produce an abundance of data. Understanding what's valuable, and what isn't, will always be the most important aspect of data scouting.

Talk to us…

If you have any questions or would like to learn more about data scouting, get in touch with us at info@neudata.co. One of our research analysts will be more than happy to discuss what alternative data can do for you.

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