The opportunity of alternative data for the banking and finance industry

(First published on Financial IT)

Thomas Egner, the Secretary General of the Euro Banking Association recently referred to data as the ‘new superpower of the financial services world.’

Despite the huge appetite for data across the industry, its disruption has been muted. Research from PwC highlights how little data most financial services firms use when understanding and engaging with customers – with estimates that businesses are only using 0.5% of available data. At the same time, according to Deloitte (2017) 44% of companies say that there are no clear accountabilities for data management or defined data processes and procedures. 

This poor use of data is partly cultural. Quarterly earnings reports and whitepapers, for example, have been the staple of banking executives when making decisions and analysing the marketplace. However, these papers are often slow and infrequent and as these sources are readily available they tend to offer limited intelligence or insight.

For those institutions that are using data, there has been a growing understanding that simply having mountains of data is not enough. The conversation has moved from having big data to having fast and relevant data, with a growing demand for alternative data to provide intelligent, accurate and actionable information. Companies are beginning to see the value in purchasing alternative data as a way of gaining a competitive information advantage that is so vital in the banking and finance marketplace.

Alternative data consists of data obtained from hard to access or non-traditional sources such as satellites, point of sale transactions and the Internet of Things, and can be used to better predict market movements and trends.  By harnessing alternative data, corporations, economists and investors are able to access the very latest data to inform the best decisions.

Alternative Data: an information advantage

Alternative data presents an information advantage, especially prominent in the field of investment management, with hedge funds among the pioneers in the alternative data field.  A 2017 report from JP Morgan suggested that asset managers were spending up to $3 billion on alternative data per year, with the number of alternative data analysts quadrupling in the last five years.

Recent research from the University of Toronto highlighted the potential, with researchers analysing nearly 1 million tweets that mentioned nearly 3,600 companies to perform textual analysis on them.  This analysis was then fed into a machine learning algorithm that was able to accurately predict whether each company would meet their quarterly earnings target or not. Indeed, the approach was even able to accurately predict how the share price of each firm would respond to that event.

In addition, work undertaken by the School of Business and Economics at Friedrich-Alexander-Universitat Erlangen-Nurnberg, which saw a deep learning algorithm trained on 180 million data points about members of the S&P 500 over a 22 year period from 1992 in order to generate better quality stock picks.  When compared to existing methods, the algorithm was capable of achieving double digit returns, with an especially strong performance during turbulent financial periods.

Such outcomes are not confined to the stock market, with researchers at the University of Plymouth showcasing how commodity prices can be predicted with similar accuracy.  Their work saw algorithms trained on vast quantities of data to accurately predict movements in the price of oil.

Capitalizing on the opportunity of alternative data

Despite the tremendous promise presented by data, its collection is not devoid of risk.  Traditional data gathering is a big threat to an organisation’s security and reputation. Methods involving mining, scraping and analysing vast amounts of personal data from day-to-day business operations – to inform marketing and advertising campaigns -are putting companies at risk. 

Data breaches such as those experienced by Equifax and Capital One highlight the disadvantages of depending on personal data and traditional data collection.  At the same time, there needs to more robust cyber security measures for existing data sets on customers and stakeholders.

By using alternative data there can be significant reductions made to the security risks associated with relying on highly personal data – because any data that is being collected will be anonymised and aggregated.

Once companies have successfully adopted alternative data as part of their business strategy, we’ll see an evolving risk landscape. The real threat to banks and financial institutions won’t be data breaches but will relate to businesses that aren’t capitalising on the opportunity to stay better informed.

Firms that fail to update their investment processes to incorporate a wide range of alternative data sources run a considerable strategic risk, and are likely to be outflanked by rival firms that are able to incorporate such data sources into their valuation and trading processes. 

Alternative data is set to transform banking and finance in the coming years, so missing out is a risk you really can’t afford to take.

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. 

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.



Disrupting payments and unlocking the value of data

(First published on Instapay Today)

PSD2: the latest tightening of data regulations will require strategic, operational and infrastructural changes for banks and financial institutions. 

Is it an opportunity or a threat though? Judging from current opinion, it appears the financial industry hasn’t quite made up its mind. If there’s anything worse for the sector than a clear and present threat, it’s uncertainty.

In a recent survey conducted by open banking platform Tink, one thing is clear, financial institutions dislike regulation. They named it as the biggest threat to their current business models. With the final PSD2 deadline looming on the horizon, there is little time for firms to get wrapped up in an existential crisis though. Most are soldiering on, despite their doubts, to ensure they can comply with the new directive. They are investing in digitization, greater security and privacy. 

However, it’s clear that they need to do more than the bare minimum in order to not only survive, but thrive, in this new ecosystem. For banks, payment service providers (PSPs) and other players, PSD2 unearths an opportunity for them to innovate and compete.

Data should increasingly be viewed as a natural resource like oil. Yes, data is the new oil is a somewhat tiresome cliché, but sitting on an oilfield is not much use unless you have the right tools, infrastructure and capabilities to make something out of it. In that sense, firms need to grapple with how they can turn what is essentially a commodity, into a competitive advantage.

To benefit from the opportunities that will arise from PSD2, there are two key approaches any financial or payments services firms can take in the new landscape:

1. Monetize their data – Increasingly, no one party will have a monopoly on data. This means firms will need to start thinking about how to leverage their distinctive data sets as part of a data monetization strategy – without compromising sensitive personal information.

When it comes to monetizing data, many are enticed by the opportunity, but they may view it as a challenge. They may raise questions over data ownership and privacy. 

However, there is great value in anonymized, aggregated information that is used for business or investment insights. In finance, the interest is in identifying broad trends and patterns – the focus is never on the who but the what and how much. That means it’s possible to extract value from this data while preserving privacy.

Outside finance, there are other examples of how sensitive data can be used in a way that benefits the public. For instance, Uber shares anonymized data aggregated from billions of trips taken by its users in order to help urban planning around the world. 

Transparent and responsible use of this data can open the door to new revenue streams. Data might not be the core business for many of these firms, but revenue from this can quickly become meaningful as the quantity and quality of data grow over time. 

The value of their data can also increase when combined with multiple sources for consumption by third parties.

It can sound counterintuitive to deal with the threat posed by open data by sharing it even more widely. But this allows firms to strengthen existing data and play a more important role in the transactional ecosystem. Payments providers are well-positioned because they have unique insights into both merchants and consumers. 

2) Get better customer insights – The changes that will be brought on by PSD2 will show that no incumbent can afford to rest on their laurels. The classic mindset of getting all your financial services from one provider is going to change. Many payment experiences will change and become more seamless.

One hot topic is instant payments. While consumers are the biggest benefactors of this trend, merchants can also benefit from it in a number of ways. Instant payments are data-rich so they can leverage real-time data like never before. 

What does this mean for firms in this industry both big and small? Well, it will become more important than ever to convert data to actionable insights. They can use such insights to improve the customer experience, drive loyalty and even introduce better offerings.

This can help incumbents become much more data-driven and customer-centric in their approach, leading to better decision-making. Meanwhile, smaller players that can nimbly respond to these insights can outmaneuver bigger competitors and eat away at their market share. 

Ultimately, firms need to tackle PSD2 from a strategic perspective and not just from a compliance perspective. The ones that proactively capitalize on these opportunities can future-proof their business and disrupt, rather than be disrupted.

An Alternative Way of Seeing Data Monetization

From early-stage payments fintechs to giant acquirers, every company is asking themselves the same question: “How can we turn our data into dollars?”

After all, most companies these days are to some extent data companies, whether they are aware of it or not. Many businesses try to leverage certain types of data they capture, but there’s also a lot of valuable ‘data exhaust’ they could use without ever sharing any personal or sensitive information. This is known as alternative data and it is being rapidly monetized and shared in the US and Europe.

What is data exhaust?

No, it doesn’t refer to the exhausting nature of big data. (Though there is something to be said about that too!)

Data exhaust refers to the excess data that is generated as a byproduct of a company’s operations. Simply put, it’s all the data the firm might not know what to do with, or might not think is relevant to its core business. This amount is much bigger than you think – Forrester reported that on average, between 60% to 73% of all data within an enterprise goes unused.

However, with advances in IoT, machine learning and artificial intelligence, this rapidly growing volume of exhaust could hold much untapped potential. In fact, this data exhaust could end up being converted into valuable fuel, whether for better decision-making or new ancillary revenue.

Why is data monetization so hard?

Firstly, many firms struggle with what data monetization actually means. Some paths to data monetization are more obvious than others. We’re living in an era when exploiting data for advertising or marketing purposes has become a huge concern. Even when there is no threat to personal privacy, organizations still have to navigate reputational risks if there is even a whiff of data misuse.

Secondly, trying to glean insights from all this raw and unstructured data can be like finding a needle in the haystack. It’s a significant challenge in terms of resources and infrastructure, requiring data expertise that is usually not found in-house.

So what can companies do to tackle this?

Two routes to monetization

These are the two primary paths to data monetization that companies can choose to take, though they are not mutually exclusive. In fact, both paths can intersect and one can lead you down the other:

1) Getting new business insights – This is an internally focused path that may not directly lead to money on the table. But it’s about leveraging data to improve operations or the customer experience. In turn, this could lead to higher profitability or greater efficiencies that result in reduced costs.

Alternative data can yield insights that we may have otherwise not considered. But it’s easier said than done because, as Forbes reports, 87% of executives are still not confident they’re able to leverage all customer data.

But first, every organization needs to take stock of its data assets and figure out which types of data potentially hold value. Then they need to assess whether they have the data management infrastructure, tools and resources to be able to extract value from it.

2) “Externally” monetize data – These days, the mere mention of “selling data” conjures negative reactions. But there are ways of monetizing non-personal data that is aggregated and anonymized. This can be valuable to people you may not be thinking of in ways you might not have imagined.

Opportunities may exist in markets that are new and unfamiliar to the data owner. For instance, firms can open up new revenue streams by selling their data to economists, analysts, investors and any other parties that are seeking to gain new and unique insights.

Raw data by itself can be one-dimensional. It is when data from different companies and sectors is combined and enriched with complementary data sets that real value is created. For instance, a company working with vendors across the country might have data on national beverage sales. It could track these sales and provide additional insights back to the vendors to help them improve sales and promotions. The company could also share this data with beverage brands so they can finetune and optimize marketing by city.

Think about it this way: Doing nothing with your data is the equivalent of keeping all your savings under the mattress. It seems like a safe bet, but it’s outdated and you get zero returns. Data monetization is a smarter investment – it seems daunting at first but if you can find a safe, meaningful use case, your company’s data becomes a revenue driver rather than a sleeping asset.