Data Monetization in a Pro-Privacy World

(First published on Dataconomy)

For over the last decade, some of the most successful companies on earth have made their riches by mining user data and selling it to advertisers. The big question is whether this will continue to be a sustainable business model with the ever-mounting scrutiny on data privacy and if not – what’s the alternative?

Many say the Cambridge Analytica scandal sparked a great data awakening by bringing to light the ways in which some companies were amassing and monetizing personal data about their users. As a result, Facebook was recently slapped with a record $5 billion fine and new privacy checks.

This isn’t a problem that is exclusive to the giants of Silicon Valley. In Europe, hefty fines have also recently been meted out to British Airways and Marriott for data breaches. As data protection complaints have doubled year-on-year, regulators will be getting tougher on companies to ensure their compliance with GDPR (General Data Protection Regulation).

Meanwhile, GDPR has driven a global movement as governments outside the EU, from Australia to Brazil, are set to introduce similar data protection regulations.

In addition, GDPR has helped to create greater awareness about data protection among the general public. The European Commission’s March 2019 Eurobarometer survey showed that about 67% of European citizens surveyed know what GDPR is.

The convergence of a compliance culture within organizations, stricter data privacy regulations globally, and consumers becoming more aware of their rights will continue to have a huge impact on businesses that profit from personal data, and even any business which collects it.

The situation demands urgency as the stakes have never been higher. According to a report by Gartner, by 2020, personal data will represent the largest area of privacy risk for 70% of organizations, up from 10% in 2018.

But better privacy for individuals doesn’t mean it’s bad for business. On the contrary, companies can use this opportunity to establish trust with customers while becoming more thoughtful and innovative about their approach to data monetization.

For many firms, data monetization has been inextricably linked with the personal data of their customers. However, they could be collecting, generating or archiving other types of non-personal data that could be valuable to certain end users. That is, the alternative data that may even be overlooked by the business generating it.

This data might be structured or unstructured, but new tools and technologies have made it easier to mine and process such data into insights. These insights could serve as timely intelligence to those in other sectors, like economists, analysts or investors looking to identify patterns and trends.

In fact, there are many use cases for such alternative data in the world of investing when every bit of timely information helps to gain an edge. This is where anonymized and aggregated data matters most and personally identifiable information has zero value. What economists and asset managers most want to know is how many soft drinks Coca Cola is selling across Europe this quarter, not whether John Doe bought a Coke.

The growing focus on privacy doesn’t mean data monetization has been taken off the table. Data will always be an important and valuable asset for any organization, but it needs to be harnessed with the full respect of individual rights to privacy. 

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.