SUBURBIA LAUNCHES EUROPEAN CONSUMER TRANSACTION DATA SOLUTION FOR INVESTMENT COMMUNITY

19 September 2019, Amsterdam – Suburbia, a technology company specializing in alternative data solutions, today launched its first-ever offering that leverages millions of anonymized transactions across Europe to provide predictive insights into consumer goods companies.

A multi-source platform with granular insights into brand performance, it delivers daily signals on over a hundred publicly listed and large private companies. This data product has been built specifically for hedge funds, asset managers and other institutional investors to generate alpha and manage risk.

“Investors have long been tapping into transactional data to anticipate trends and consumer behavior,” said Hamza Khan, CEO, Suburbia. “But we realized most of the existing data out there is generated by a panel of users which could lead to an opt-in bias and less accuracy. In addition, this data is much harder to come by in Europe because it’s such a diverse and fragmented landscape – every country has its preferred payment methods. We believe our unique approach has resulted in the industry’s most actionable dataset.”

Suburbia’s proprietary technology is capable of processing millions of consumer purchases from thousands of hospitality and retail channels across Europe, with a strong focus on Germany and the Benelux. No personal information is ever used or shared in the process. This data is updated on a daily basis so investors can get up-to-date insights for making decisions faster.

Other highlights of this product include:

  • Ticker mapping to easily see performance of publicly traded consumer packaged goods (CPG) companies over time
  • Granularity such as product details, item pricing, basket composition, geography and time of transaction
  • Historical coverage, with over two years of data available for backtesting

How can Facebook solve its privacy crisis? Just ask Otis Elevator

You’d be hard-pressed to think of two terms that have captured the tech zeitgeist more than “big data” and “data privacy”. So what do they have to do with a 160-year-old machine?

Firstly, you might ride this humble box several times a day without realizing its significant contribution to urban life. The elevator was a transformative technology that ushered in the era of the modern city and made skyscrapers possible. 

Like any technology, its evolution over time has had ups and downs, but the advancements made in its history can teach us some important things:

  1. Focus on building trust through action, not communication.

When the first passenger elevators were introduced in the early-to-mid-nineteenth century, the rate of adoption was slow. After all, there was always the risk a cable would snap, plunging the elevator and all its occupants to their possible deaths. “Thanks, but I’ll take the stairs,” was likely the common rejoinder at the time.

The makers of elevators could have dismissed them as one-off incidents, or showed how statistically rare elevator-related injuries and fatalities were. But it wouldn’t have mattered as people simply didn’t feel safe getting in there.

What really changed people’s perception was a critical safety feature that was first demonstrated by Elisha Otis at a world’s fair in New York. As detailed in the book Lifted: A Cultural History of the Elevator, the American inventor stood on a platform high above the audience when the only rope holding it up was cut with an ax on his orders. The safety mechanism kicked in immediately, preventing the platform from plummeting to the ground. 

After this, public confidence in elevators soared, particularly in Otis’ safety elevators. He became inundated with orders, which doubled every year. 

It’s a crucial lesson to social media and tech companies that the elevator pitch for their technology matters less than than their ‘elevator moment’. Most will pay lip service to the notion of privacy, without demonstrating the tangible and practical steps they’re taking to ensure the safety of users’ data. Any organization dealing with personal data needs to plan for worst case scenarios and prepare for them appropriately by having safeguards in place. Only then can they truly protect individual privacy and earn consumer trust. 

2. What seems like an obstacle now will be a pivotal opportunity in hindsight.

When GDPR (General Data Protection Regulation) was first introduced, many companies viewed it as a hurdle to overcome. How could they now monetize their data or personalize their marketing? 

It helps to take a step back into a time when elevators were still manually controlled by an operator. Sitting in an elevator to press buttons all day was an actual paying job. Then, in the 1950s came automatic elevators that didn’t need human operators, though there was just one little problem: People hated them. 

As a professor of architectural history tells The Globe and Mail, there are “stories of people walking into elevators and walking back out”. In fact, it took a good part of a decade for the technology to become commonplace and for people to get used to it.

It seems laughable now, the idea that people didn’t see it as their job to push a button and simply felt uncomfortable doing so. But aren’t we going to also look back at this era, when companies regard privacy regulations as a demanding obstacle, with incredulity? 

After all, GDPR and the growing wave of legislation worldwide should be seen as a watershed moment for businesses. This is a turning point for marketers to stop microtargeting with personal data when there is a wealth of other types of data at their disposal that can be used to generate relevant and effective content. 

There are many ways to personalize marketing without the use of personal data. For instance, there is what GDPR categorizes as pseudonymous data (data that can’t be used to directly identify an individual) like the customer’s local weather. Is it more relevant for a brand to bombard a customer with ads for umbrellas because he viewed them once, or to offer an umbrella to everyone living within a particular area on a rainy day? Does a brand have to know about your allergies, or can it use available pollen count data by geographic region?

Companies simply need to ‘push the button’ and stop seeing compliance as a chore. Instead, they need to embrace data privacy as a valuable opportunity to build trust and use non-personal data more creatively. 

3. Fast and reliable data makes it possible to predict things before they happen.

The elevator has come a pretty long way since Otis brought it into the mainstream. They have not only gotten better, faster, safer – but also a lot smarter. 

On the surface, elevators may not seem to have changed much over the last decades. In reality, the technology that keeps them moving smoothly is cutting-edge. AI and real-time data are being used by major elevator manufacturers for predictive maintenance – so they can spot problems before they arise and better anticipate breakdowns. For instance, ThyssenKrupp’s elevators are connected to the cloud, collecting data from its sensors, and transforming that data into actionable analytics. 

KONE has a similar system that incorporates IBM’s Watson IoT. Using data points transmitted by elevators across the world, KONE can glean historic failure rates of different elevator parts and the preceding conditions. For example, a temperature reading that’s slightly above normal could be a sign of engine trouble, but the system can also note if it’s a hot day, which could be a factor too. Its forecasting also improves as more data is fed into the model. 

Similarly, faster access to better data is needed to make critical business or investment decisions. Relying on traditional sources of information like earnings, filings and economic reports is akin to elevator manufacturers depending on written maintenance records. 

But why wait 90 days for a quarterly report when one can access a steady stream of intelligent data? New sources of information, or what we call alternative data, are constantly generated around us and investment managers can leverage them to get an unprecedented level of transparency into company performance on a near real-time basis.

From anonymized transaction data to price trackers, these can be used to generate predictive insights so proactive decisions can be made, instead of mere reactions to events as they occur. For investors, that can help them forecast market movements and trends, and manage risk. 

To sum it up, businesses and investors need to use data and privacy as the vehicle of change, much as the elevator was once upon a time.


Suburbia first and only Dutch startup selected for Tokyo accelerator program

Suburbia is the first Dutch startup ever to be selected for Fintech Business Camp Tokyo, an accelerator program for young fintechs. The two-month program, which will kick off in October, is run by the office of the mayor of Tokyo with Accenture Japan.

“We feel really proud and honored to be the first and only Dutch startup picked for this program,” said Hamza Khan, CEO and founder of Suburbia. “Standing out in a crowded field only proves that our technology is truly innovative and highly scalable – what we’ve built in Europe can work just as well in Asia-Pacific or the Americas… or anywhere!”

Suburbia was selected by the Tokyo Metropolitan Government (TMG) from a pool of over 120 applicants located in 29 countries, alongside 11 other startups. They were reviewed and chosen based on their innovative technologies and business models “which do not yet have a presence in Japan”. 

The initiative, first launched in 2017, is part of the Japanese government’s four-year campaign to revitalize Tokyo’s financial sector and cement the city’s status as a global financial hub. TMG considers fintech a key element in achieving its goals. The program aims to provide startups with access to some of the nation’s leading companies and support them with their entry into the market. 

Startups that have been accepted into the program are provided with support in localization, mentoring from top Japanese banks, and networking and business matchmaking opportunities. Three of the 19 foreign companies that have participated in the program in the past two years now have a footprint in Japan.

As part of the program, Suburbia will be going to Japan to meet with local companies and investors. This will culminate with a pitch in November where the company will present a business plan developed during the course of the program.

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.