Valuing Your Data: A Checklist For Companies Looking To Monetize

Not all data is created equal.

When companies make their tentative first steps on the road to direct data monetization (that is, selling non-personal data they own), they have to start by understanding the value of their data. They need to assess what types of data they are collecting or generating in the course of business, and whether these could potentially be a new driver of revenue.


It’s often said that data is an important and valuable asset in any organization, but there’s a reason why it never appears on a balance sheet. Data valuation is a complex and challenging exercise. But knowing what their data is worth can help companies explore monetization, allocate resources and properly structure their technology infrastructure. 

In addition, they have to understand the market’s perception of value. Some firms might be overestimating the monetary value of their data, while others are unaware that their data is valuable, often to people in sectors and industries they may not have even thought of. A Forbes contributor compared the latter to the instance when companies realize they’re sitting on patents they don’t really need, but actually have value to someone else.

So what are the fundamental characteristics of high-quality data that organizations need to consider when trying to measure its value? Or more simply put – what makes data valuable and how much is your data worth?

1. Does it tell a story?

Does the data tell you something about the economy or market trends? Does it track which brands are growing or which products are in high demand? The better your data reflects real world behavior, the higher its value.

2. Is it unique? 

Generally, the more exclusive the dataset, the more lucrative it is. Do you have data that nobody else has, or is it already widely available from other sources? 

3. Is it anonymized and compliant?

If you are planning to share raw data, it needs to be stripped of all personally identifiable information (PII) to protect the privacy of individual customers. This is critical in order to monetize data responsibly, as data privacy is not optional but essential.

4. Is it timely? 

Is your dataset updated on a weekly, monthly or a near real-time basis? The latter is most desired, especially for economists and institutional investors that are looking for faster insights to stay ahead of the market. 

5. Is it specific? 

The more granular and detailed the data, the more valuable it is. (Though to reiterate, personal details should definitely be excluded!) For example, data showing a million smartphones were sold last week is valuable. But its value grows significantly if it also indicates how many of those smartphones were iPhone X or Samsung Galaxy, etc.

6. Is it complete?

Do you have data for every day, without any gaps? Missing data could be as bad as inaccurate data, as it provides only a partial view of the real trends. 

7. Is it reliable and consistent? 

If there are multiple servers where data is collected or stored, do they all add up properly? Or do they contradict with one another? Are there potential duplicates or other data errors?

8. Do you have archives of historical data? 

The further back your data goes, the better. Historical information is used in all kinds of analytics. In most use cases, two or more years of data are important to see how trends are changing over time. 

Can Data Monetization and Privacy Co-Exist?

Spoiler alert: Yes, they can.

The media often makes it sound like a choice has to be made between monetizing business data and maintaining privacy. But it’s not an either/or situation, it’s possible to do both at the same time.

Since the EU rolled out sweeping data protection directives through the General Data Protection Regulation (GDPR) in 2018, firms have been questioning how to leverage their data while being compliant.

Indeed, the Business Application Research Center (BARC) found the issue of data security is one of the major stumbling blocks for organizations in monetizing their data. If they fail to find a way past these barriers, they are not only missing out on a valuable opportunity, but they could also end up eating the dust of more agile competitors.

In its report, BARC stated that, “for many the risk of using data for internal and external monetization seems to outweigh the potential benefits.”

Maybe it is because businesses have been so unnerved by negative headlines regarding data privacy scandals that they fail to truly grasp what is possible under these regulations. 

Let’s focus on external monetization, which is basically about leveraging your internal operational data to create a new revenue stream. But today, the mere mention of “selling data” creates a fear of reputational risk. 

Ensuring data privacy should rightly be a chief concern for every company that is dealing with highly sensitive customer data. It’s not important just from a compliance perspective, but essential for building customer trust and loyalty. 

However, there are ways of monetizing non-personal data and this is often an opportunity that is overlooked. Companies may ask, “How can my data be valuable if it’s missing certain pieces of the puzzle?” That is because they assume the personal details form the critical components. But in fact, even an aggregated, anonymized form of the data could still form a complete picture for others. These could be people in different markets and industries, like economists, analysts or investors looking to identify patterns and trends. 

In addition, new tools and technologies have made it easier and faster to extract, refine, enrich and anonymize this data. It is this process, enabled by technology, that helps to wring the maximum value out of a company’s data. 

The early adopters in the use of alternative data, institutional investors, are rapidly increasing spending to acquire information that helps them make better decisions. Unlike advertisers, they have absolutely zero interest in personally identifiable information (PII). What they want is empirical, anonymized data that tells them how companies and markets are performing. How many beers are being sold by Heineken across Europe this quarter? Is Deliveroo seeing more orders than UberEats? Economists and analysts  have strict compliance procedures and actually demand that the data they buy are stripped of consumer-level data.

Service providers are well-positioned to capitalize on the rapidly growing opportunities to leverage their data in the digital economy. But there shouldn’t be a tug-of-war between monetization and privacy. Forward-thinking firms will understand how they can turn their data into profit while having the utmost respect for privacy. 

How Spotify Uses Alternative Data

What are you listening to right now? Right now, as you read this? With streaming apps like Apple Music, Pandora and Spotify, the answer for most people is something rather than nothing. And alternative data probably helped. 

At Suburbia we often talk about how alternative data is used in finance to get an investing edge. We’re not the only ones – do a Google search for “alternative data” and almost all the top search results will focus on finance and hedge funds. But the enormous potential it wields for businesses and governments is often overlooked.

Even tech companies like Spotify are already using alternative data in some shape or form. While the music streaming giant already has a ton of data about its users and the music they listen to, it’s actually looking beyond traditional data sets to make its customer experience better. 

Before we talk about how Spotify uses alternative data, let’s first explore what the term really means.

What is alternative data, anyway?

Most organizations outside of finance might simply think of alternative data as “big data”. So why not just call it big data? 

Because big data is a term mainly used in reference to the amount of data, not the source. Alternative data emphasizes the fact that insights can be uncovered from places you weren’t even looking to begin with. It shines a light on “data exhaust”, the data that’s generated as a by-product of a company’s normal operation. It might not be core to the business, but it could provide elements of information useful to others. 

In addition, when companies think about big data and analytics, it’s often in relation to the data they have under their own roofs. The capability to process and make sense of your internal data is good. But why stop there when there is a wealth of data out there that could be mined and combined? 

In order to unlock the full power of data, businesses need to figure out how to use external data and integrate it into their analysis. It can come from a variety of sources, especially as more non-personal information is being made available as part of open data policies. For instance, the UK government publishes more than 40,000 datasets on data.gov.uk, with similar projects all across Europe. Those that can combine data from multiple sources can end up making better decisions.

To be innovative, think alternative

These days, we’re swimming in a sea of data so identifying what is useful can often feel like an attempt at boiling the ocean. 

There are many sources of alternative data, from geolocation and tracking to web data (scraping) and transaction information based on aggregated and anonymized credit/debit card data. 

Many may not see the value in alternative data until they see an actual example of how it can help their business.

Let’s go back to Spotify, which provides a strong example of how web scraping is used in a creative and resourceful way to improve the user experience. 

Many Spotify users love the curated playlists, like the Discover Weekly playlist. Some have even tweeted that the streaming service knows their taste better than their loved ones.

One of the tools Spotify and other music streaming services use to generate these playlists is similar to what ecommerce sites use to recommend items.

To make its predictive algorithm more precise, Spotify doesn’t rely on just one source of information or its internal data alone. It combines a few different approaches. One of these is natural language processing (NLP). You’ve probably heard of it before but never realized the important role it plays in Discover Weekly. Basically, Spotify is continuously scouring the web, analyzing blog posts, articles and other text about music. It looks at which terms are used in reference to artists and songs, and each term is weighted by relevance. This enables Spotify’s engine to determine which songs or artists are similar to each other.

That could even inadvertently throw out a lifeline to music journalism, a field that’s seemingly on the brink of extinction! Especially if it serves greater utility than just being an arbiter of taste – after all, Rolling Stone’s reviews may be helping to decide whether or not Fleetwood Mac makes it to your Discover Weekly playlist…or that of Spotify’s 217 million active users.

The use of alternative data helps to surface unique insights for businesses that they might have otherwise failed to find. Like technology, data can be a real driver of innovation and differentiation. If you’re not doing it differently, then you’re never going to break from the pack. 

If Spotify’s spiders are crawling this article to look for descriptive words, we’d like to finish with some awesome, excellent, amazing recommendations we put together the old-fashioned way: hand-picked from our team to yours.