Good Life or the New Pollution?A guide to data monet isat ion for NewFinance by Mark Pearce

Copyright © 2015 | Profit From Data

C O N T E N T S

CHAPTER 1

Introduction

CHAPTER 2

Current thinking around the uses of data and why this is a watershed moment

CHAPTER 3

Data monetisation: the big questions (and answers)

CHAPTER 4

Companies that are changing the data monetisation industry

CHAPTER 5

The blockchain, a technology that could change the data industry forever

CHAPTER 6

Conclusion

CHAPTER 6

Biography

CHAPTER 6

References & sources

C H A P T E R 1

Introduction

DATA IS UBIQUITOUS; OUR LIVES LEAVE A TRAIL OF IT EVERYWHERE WE GO.

While the data created by mobile devices is set to

expand exponentially, as we swap cash and credit

cards for digital wallets, that volume of data will be

dwarfed by the mass of information created by the

Internet of Everything (IoE) devices, which encom-

pass people-to-people (P2P), machine-to-people

(M2P), and machine-to-machine (M2M) connec-

tions. Add to this the vast amounts of data that ac-

ademics, companies, governments and scientists

produce and you have a universe of data that is

so large it’s difficult to comprehend. That mass of

information may seem daunting and unmanagea-

ble but many see it as an opportunity.

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For example, companies are already using data to:

Understand and accurately predict demand and supply;

Enable knowledge management;

Innovate more successfully;

Support informed logistics and deliver just-in-time production;

Embrace the drive to massclusivity – mass produced services

and products tailored to everyone’s tastes and needs (Apple);

Power new business models that reduce the need for

traditional middlemen: hotels (AirBnB), investment advisers

(eTrade) and taxis (Uber), although arguably these are just

new types of middlemen.

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D A T A I S E S S E N T I A L

T O E V E R Y B U S I N E S S

I N T H E W O R L D

senior executives know that with the right data, modern sta-

tistical methods will outperform an individual or small group

of people every time. This is a seductive story but still there

are companies that are holding back. I would argue that data

monetisation is something you can’t afford to miss and that

it should be a core consideration for all companies and ven-

tures be they large or small and here’s why:

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Yes, laws around data are changing and are focused on the

rights of the individual but don’t view that as a hurdle, see it as

an opportunity to gain the trust of consumers and build a loyal

customer base;

Yes, monetising data in the wrong way is risky but if you do it

the right way your customers will help you innovate;

If you think growing your business, remaining competitive and

increasing profits is important then so is data monetisation.

It’s only too time consuming if you get poor advice and

approach it the wrong way - it doesn’t have to be costly;

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Steve Jobs sa id ‘ i f you don’t cannibal ise yoursel f someone e lse wi l l ’

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I shouldn’t line up one of my quotes against a goliath like Steve

Jobs but I believe that ‘if you don’t monetise data, you will lose

customers to companies that do’. It might be a culture shock

to implement such a strategy and the fruit that it bears might

be after your tenure but running a company isn’t just about

completing your shift and collecting your pay cheque, it’s about

good stewardship – keeping an eye on the future and making

sure your company evolves to be the best it can be. Customers

will praise you if you monetise data the right way. Even if you

can’t do it within your tenure you could, at the very least, set up

the infrastructure ready for your successors to switch it on.

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For organisations doing exactly that, it will provide large

sustainable profits for investors and owners alike, in other words

the good life. In the many years that I’ve been monetising data,

it’s clear to me that we’re now at a watershed. It’s true that the

old ways of doing business are changing but I would argue that

they’re changing for the better.

Data, if collected and used in the right way, is gold

Le

t m

e t

a k e yo u t o a p l a c e I k n o w y ou w

an t t o g

o

I t ’s a G o o d L i f e2

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In this guide I will:

Explain some of the current thinking around the uses of dataIntroduce some companies that are monetising data in a ‘good life’ way and others, which are creating a ‘new pollution’3

List 10 main questions and provide some guidance, which you should ask yourself and use, to see if you have an opportunity worth pursuingCatalogue the many companies that are changing the data monetisation industryLook briefly at a technology that could change the data industry foreverConclude with my thoughts on where data monetisation might go

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Please note that when discussing data, the terms ‘commercial-

isation’ and ‘monetisation’ are used interchangeably. Howev-

er, for the purpose of this guide the commercialisation of data

is the use of company and other sources of data to improve the

performance of a business. Whereas the monetisation of data

is to generate revenue from available data sources by selling

it to third parties and/ or by building benchmarks and indices

that are sold as a separate revenue stream. It’s common for

both to exist simultaneously although the commercialisation

of data is likely to come before its monetisation. Information

on the commercialisation of data is widely available whereas

guides on its monetisation – at least according to my definition

- are sparse, which is why I wrote this guide.

C H A P T E R 2

Current thinking around the uses of data and why this is a watershed moment

Alan Moore, in his book No Straight Lines 4, describes how billions of people are searching for information on their devices to help them make decisions and transactions. As well as lots of opportunities, this wealth of data also creates problems around privacy and data protection. There’s a paradox with privacy. On the one hand everyone fears losing it – Scott McNealy of Sun Microsystems famously said that consumer privacy is a red herring: we have zero privacy and we should all get over it 5 – but the alternative is of the need to respect the sovereignty of the individual whether that be in a commercial or civil context. Esther Dyson, former journalist and Wall Street technology analyst, argues that we need

more granular control over our data.6 Despite these concerns and obvious friction new mutually beneficial ways to share data are evolving, Doc Searls, an American journalist, columnist and a widely read blogger, coined the phrase the ‘intention economy’.7 This is where users are given control so that they lead themselves to services, subjects and ideas that interest them and they will do this outside of any one vendor’s silo. In his book he describes an economy driven by consumer intent, where vendors must respond to the actual intentions of customers instead of vying for the attention of many. Demand will drive supply far more directly, efficiently and compellingly than ever before.

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Adding even more gravitas to that view is Andreas Weigend, data expert and Stanford University Professor who believes our world ‘Has shifted to a model of collaboration and explicit data creation. Successful firms develop systematic ways to encourage and reward users who contribute honest data. A good system does not try to trick customers into revealing demographics or contact information that is useful for the company. Rather it rewards users with information that is useful to them.’ 8

So what does that mean for companies that are looking to monetise data? Here’s a view from Micah L. Sifry, cofounder and editor of the Personal Democracy Forum, who observes ‘From

Wikipedia to Craigslist to Amazon to Google, the Web keeps rewarding those actors who empower ordinary users, eliminate wasteful middlemen, share information openly, and shift power from the centre to the edges.’ 9

Good life companiesAre companies that respect the privacy of ‘data subjects’, who are building services that benefit both businesses and consumers:

Datasift10 enables companies to capture, analyse and act on all types of human-generated data, without compromising consumer trust; the platform filters and extracts information from billions of social posts, news sources and blogs providing insights and finding trends in everything from brands, businesses, financial markets, news and public opinion.

Personal.com11 is a cloud-based Personal Data Service and identity management system for individuals to aggregate, manage and reuse their own data.

Swipely12 there are lots of payment services out there, but Swipely’s selling point is helping merchants better understand their customers. Its cloud servers crunch the data left by card swipes, strip out personal identifying factors for security reasons, and turn the data into nicely designed customer dashboards showing which customer types bought what and when with insights such as how specific factors (like the weather) affect profits.

The new pollutionThere are however other examples of data monetisation that are detracting from society. ‘The New Pollution’, a song by Beck, is an apt title for these practices and although they have been a necessary marketing tool for businesses for generations, the enormous amounts of data now available through social media and the coming of the Internet of Everything means these practices, if un-checked, will lead to a new pollution as companies infiltrate every aspect of consumers’ lives as they fight for their attention.

In February 2014 Chairman John D. (Jay) Rockefeller IV and Senator Edward Markey

introduced legislation in the U.S. that would require data brokers to be accountable and transparent about the information they collect and sell about consumers.13 The Data Broker Accountability and Transparency Act of 2014 (DATA Act) prohibits data brokers from collecting or soliciting consumer information in deceptive ways, and it allows consumers to access and correct their information to help ensure maximum possible accuracy. Under the DATA Act, consumers are also able to opt out of having their information collected and sold by data brokers for marketing purposes. The legislation empowers the Federal Trade Commission to enforce the law and to

impose civil penalties on data brokers that violate consumers’ privacy and trust.

In response to these criticisms one of the largest data brokers, Acxiom, is taking steps to change the public’s perception of what it does. The company created aboutthedata.com, which is aimed at helping consumers get a better understanding of how their data is used by marketers.

These concerns about privacy and data protection are not confined to the U.S. In Europe, the effort to pass a General Data Protection Regulation (GDPR) has moved forward. The European Parliament passed its latest version of the Regulation14 and in June 2015 the European Council of ministers gave a clear signal that it looks to reach agreement

on the GDPR by the end of the year.15 The U.S. and the European Commission continued to discuss changes to the Safe Harbor Framework16 and the Court of Justice for the European Union ruled that Europeans’ fundamental right of privacy includes a right to be forgotten.17 These are global issues and to demonstrate that Brazil18 and several countries in Asia19 are in the midst of revising or adopting data privacy laws.

This ‘new pollution’ is not limited to data brokers. In June 2015, Apple chief executive Tim Cook heavily criticised tech companies, which attempt to monetise customer data for advertising purposes.20 While Cook did not explicitly identify the companies, his assertion that some of Silicon Valley’s most prominent and successful companies

“have built their businesses by lulling their customers into complacency about their personal information” is a swipe at Facebook and Google, who use targeted advertising and store vast amounts of user data.

His remarks aren’t unfounded as in March 2015 Google lost a Court of Appeal bid to stop consumers having the right to sue in the UK over alleged misuse of privacy settings.21 A group of users claimed that Google bypassed the security settings on the Safari browser to install tracking cookies on their computers in order to target them with advertising. Google has already paid fines of over $40m related to this matter in the US. It was fined by the Federal Trade Commission and separately by 38 states.

Companies need to be sensitive about these issues and the furore around Spotify changing its Ts&Cs on the use of personal data in August 2015 is a prime example of why. Daniel Ek, CEO of the company, was forced to respond in a blog post: “We are in the middle of rolling out new terms and conditions and privacy policy and they’ve caused a lot of confusion about what kind of information we access and what we do with it. We apologise for that. We should have done a better job in communicating what these policies mean and how any information you choose to share will - and will not - be used. We understand people’s concerns about their personal information and are 100 percent committed to protecting our users’ privacy and ensuring that you have control over the information you share.”22

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It takes time to build trust, there is no way to circumvent it or build it quickly. Customers are naturally sceptical for all the reasons above about the use of their data and companies are equally reticent to innovate in this area as they do not want to fall foul of any privacy or data protection laws. Both of these problems need to be resolved as there are huge benefits to both sides from using data in the right way. Ultimately, I see customers and marketers entering into a trusted relationship based upon transparency where customers share data about themselves and entrust the marketers with their data in return for better, more personalised, services. You will see, at the end of this guide, that there are a number of

great companies building that trust, please have a look at their websites and show them your support by trying out their services.

The Digital Catapult Centre, a UK Government funded initiative, is looking at a voluntary trust framework that companies can follow. They recognise that building trust and removing the friction, has by necessity got to be a communal activity where organisations between which data is passed need to agree to common standards, if customers are to have any confidence in the end-to-end flow. To highlight the importance of this issue they estimate the potential value at stake in the UK at around £40 billion by 2020.23

Trust is the critical ingredient

C H A P T E R 3

Data monetisation: the big questions (and answers)

Are there any legal and/or regulatory constraints that prevent you from

monetising the data?

Q1

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Data Protection Act 1998 (DPA)This act regulates the use of personal data in the UK. Here is a link to the Information Commissioner’s Office (ICO) website, which explains the act in detail and has lots of helpful advice: https://ico.org.uk/for-organisations/guide-to-data-protection

Here is a link to the ICO’s self-assessment where you can see if you need to register or not: https://ico.org.uk/for-organisations/register/self-assessment

The Privacy and Electronic Communications (EC Directive) Regulations 2003 (PECR)Here is a link to the ICO website, which

explains the regulations in detail and has lots of helpful advice: https://ico.org.uk/for-organisations/guide-to-pecr

Competition and Markets Authority (CMA)Here is a link to the CMA’s competition law and unfair pricing agreements website: https://www.gov.uk/competition-law-unfair-pricing-agreements

Financial Conduct Authority (FCA)Here is a link to the FCA’s description of how they regulate financial benchmarks: https://www.fca.org.uk/firms/markets/benchmarks.

A1

Which solicitors could you approach for advice on the regulations listed above?I have used several solicitors in the past and would be happy to make introductions but there are many solicitors out there that you can contact.

You might find the list in this link useful: www.entrepreneurhandbook.co.uk/law-firms-solicitors-in-the-uk/

A1

Is regulation changing and if so could future iterations of laws render your proposed

data strategy unworkable?

Q2

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A2

Yes regulations are changing and to assess their impact on your data strategy, please consider the following:

Personal DataThe European Commission proposed a major reform of the EU legal framework on the protection of personal data and a single data protection law, which they refer to as the General Data Protection Regulation (GDPR), looks set to replace the existing patchwork of national laws.

The new law will impact almost every business operating within the EU and, consequently, many businesses outside it. The EU data protection reform promises to be an enabler for data services in Europe by providing

a single, strong, and comprehensive set of data protection rules for the EU that will boost growth and innovation by enhancing legal certainty and strengthening trust for consumers.

Among other things, it will guarantee stronger rights for citizens including an explicit right to be forgotten, a right to object to data processing, and a right to be informed when data security is breached.http://ec.europa.eu/justice/data-protection/index_en.htm (certainly an amusing video)

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BenchmarksThe European Commission first proposed a regulation on benchmarks in September 2013 to improve the functioning and governance of benchmarks produced and used in the EU and to ensure they are not subject to manipulation. The regulation upholds the principles agreed at international level by the International Organization of Securities Commissions (IOSCO) in 2012 and 2013. When adopted, new proposals will contribute to the accuracy and integrity of benchmarks used in financial instruments and financial contracts.http://ec.europa.eu/finance/securities/benchmarks/index_en.htm#maincontentSec1

Do I need to get consent to use the data?

Q3

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A3

If it’s personal data then yes you do. One of the conditions for processing data is that the individual has consented to their personal data being collected and used for the purposes in question. You will need to examine the circumstances of each case to decide whether consent has been given. In some cases this will be obvious, but in others the particular circumstances will need to be examined closely to decide whether they amount to an adequate consent.

Successful companies are honest and clearly communicate with customers and consumers about how they are using personal data. A rule of thumb that organisations should follow

is to always think about the customer/ consumer/ employee experience and their personal benefits from data projects. Data projects that create a negative experience with users, despite the company benefits, should be redesigned.

How might you anonymise and or aggregate the data?

Q4

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A4

The ICO has developed a code on anonymisation and published it on its website: https://ico.org.uk/for-organisations/guide-to-data-protection/anonymisation

There is also an organisation called UKAN the UK Anonymisation Network http://ukanon.net/ who have some helpful guides on anonymisation.

What could the data you arecollecting be used for?

Q5

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A5

Direct marketingAlerts are an early warning of a significant change in a customer’s life. For example, potentially selling their home, renting a property or changing jobs. These can provide a valuable insight into the acquisition of new customers and the management and retention of existing customer relationships. Marketers use these alerts to help them tailor their marketing messages on an almost real time basis.

Predictive analyticsThis is where models are used to create predictions that help companies develop more profitable relationships with their customers.

There are three main types of predictive model for marketers:• Clustering models (segments);• Propensity models (predictions); and• Collaborative filtering

(recommendations)

Benchmarks and indexesThe terms “benchmarks” and “indexes” are often used interchangeably, but actually they are unique terms that describe different things. An index is a statistical tool designed to measure momentum or lack of it over time. Whereas a benchmark is something that serves as a standard by which others are measured or judged; it is a point of reference from which measurements are made.

Do indexes, benchmarks and data have value?

Q6

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A6

Yes, they have value but you’ll need to research and survey your market to identify what that value is. Here are some examples of the sort of value you can ex-tract:

Direct marketing leads• Move alerts = 50p to £1 per alert• General insurance = £3 to £6 per qualified lead• Life insurance = Up to £50 per qualified lead (perhaps more for specific lines)• Independent Financial Adviser (IFA) leads = Up to £65 per qualified lead• Benchmarks and Indices = Free with PR benefits to £100,000s for investment indices

Although there are some clear fixed pric-

es in the market for specific leads, if your data is more timely or detailed than what is already out there then you will be able to negotiate a higher fee. Conversely, if you have a commoditised data set you will still be able undercut the rest of the market and extract some value; however, that is a dangerous strategy, it’s better to devise ways to increase the intrinsic val-ue of you data. You might, for example, partner with a third party to derive a data set or service that leverages your data to provide some analysis that isn’t accessi-ble anywhere else in the market. This is worth considering and many data brokers are prepared, in fact encourage, their data providers to work with them in this way as they want to distinguish themselves from their competitors.

Who would buy it?

Q7

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A7

Direct marketing data (alerts and propensity data) would be bought by:• Direct marketing agencies• Financial Services companies (from banks to Independent Financial Advisers to Insurance companies)• Healthcare providers• Industrials• Retailers• Real estate companies• Technology companies• Utilities providers

Benchmarks, from:• Associations• Companies• Governments• Investment firms/ industry analysts• Pressure groups

Quite often there is more than one use for the data and as you’re the expert in your market, it’s likely that you can think of many scenarios where the data might add value. You might know of ways to use the data to improve your customers’ use of your core services but at the same time you might be able to monetise that same data with third parties. Before the start of any data monetisation project you must spend a month or more researching your target market and questioning potential users to define their likely uses of the data and uncover its intrinsic value. That research will also be critical to assess whether the intrinsic value can be crystallised in a time frame that suits you.

Do you distribute the data yourself or do you use a specialist third party?

Q8

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A8

If you go it alone you’ll have to develop a relationship with every potential buyer of your data. This means you’ll get a higher fee but your costs will be higher as you’ll need to hire a sales team and manage the process. In addition your time to market will be longer as you’ll need your team to get up to speed and you’ll need to build trust with your target market. Alternatively, you could work with a partner who is already known in your target market. Often you can go through industry associations or one of the many data brokers.

Can you build a Data Warehouse and analytics team using your current available resources?

Q9

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A9

The quickest and most cost effective way to do this is to get expert advice. If you don’t you can waste a lot of resources fumbling through development plans that are sub-optimal, which you’ll need to re-engineer at a later date. Expert advice will help you develop a robust plan so that you can deploy your valuable resources with confidence.

If you decide to build the Data Warehouse and analytics platform yourself what agile best practices can you use to build it? There are many specific agile development methods out there including: lean software development, Kanban, Scrum etc. My skills are

innovation, product management, business development and sales not IT but having said that, I recognise a good process when I see it. One agile process that I’ve seen used is the Disciplined Agile Delivery (DAD) decision framework. It’s not so much a framework as an approach. Scott Ambler + Associates is the thought leader behind the Disciplined Agile Delivery (DAD) framework and its application: www.disciplinedagiledelivery.com

Who can you outsource this data warehousing and analysis to?

Q10

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A10

As data analysis becomes more mainstream every business that wishes to pursue data driven decisions, at scale, will need to develop cost-effective analytical capabilities and processes. The choice of data analytics vendors and platforms is overwhelming: from open-source software frameworks to offerings from giants such as IBM, Microsoft and Oracle and a growing pack of start-ups. To complicate matters, different software is needed to prepare data, manage data and analyse data.

Cloud analytics is offered through different delivery models, such as private, public, hybrid and community clouds. These solutions are more flexible than traditional IT systems, reducing

deployment time, requiring less storage space and have a high processing capacity; versus traditional on premise, slow and expensive analytics solutions, several companies have developed cost effective, scalable, cloud-based, networked solutions:

Traditional players include:• Birst https://www.birst.com• Domo https://www.domo.com• IBM http://www-01.ibm.com/software/ analytics/cloud/• Oracle https://cloud.oracle.com/analytics-cloud

A10

• SAP http://hcp.sap.com/capabilitiesanalytics.html

New cohorts include:• Alteryx http://www.alteryx.com• GoodData http://www.gooddata.com• Rosslyn Analytics http://www.rosslynanalytics.com

If, after answering these questions, you’re still interested in commercialising the data available to you, how do you build a data strategy?

As mentioned previously, the quickest and most cost effective way to do this is to get expert advice. If you don’t you can waste a lot of resources fumbling through development plans that are sub-optimal, which you’ll need to re-engineer at a later date. Expert advice will help you develop a robust plan so that you can deploy your valuable resources with confidence.

You should look to develop a data strategy that benefits your customers, giving them greater insights into their markets as well as uncovering a new revenue stream for your company. The symbiotic nature of this approach increases the probability of the project’s success.

McKinsey & Company identify three keys to building a data-driven strategy: 24

1. Choose the right data• Source data creatively• Get the necessary IT support

2. Build models that predict and optimise business outcomes

3. Transform your company’s capabilities• Embed analytics in simple tools for the front line• Develop capabilities to exploit big data

Data strategies are about setting the business objective and then assessing which data types you can access to achieve that objective. When setting the objective, you really need to narrow the scope of these projects and focus on specific business problems/ opportunities.

C H A P T E R 4

Companies that are changing the data monetisation industry

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For the purposes of this guide I refer to personal data as small data. These com-panies and organisations are at the small data frontier. They are building on the intention economy ideas promoted by Doc Searls and are using data in a way that benefits individuals and embraces the inevitable changes in data laws:

Allfiled https://www.allfiled.com/ UK head office

Cozy http://cozy.io/en/ France head office

CTRLio https://www.ctrlio.com/London head office

Customer Commons http://customercommons.org/about-us/US head office

Datacouphttps://datacoup.com/US Head office

Small Data Frontier

Digi.Me http://digi.me/ US head office

Handshakehttp://handshake.uk.com/hs/index.htmlUK head office

The Hub of All Things http://hubofallthings.comUK head office

Meeco https://meeco.me/Australian head office

Midata https://www.gov.uk/government/news/the-midata-vision-of-consumer-empowerment

https://www.gov.uk/government/publications/midata-voluntary-programme-reviewUK initiative

Mydex https://mydex.org/UK head office

OpenPDS/SAhttp://openpds.media.mit.edu/US head office

Personal https://www.personal.com/US head office

Qiy Foundation pronounced “key”, https://www.qiyfoundation.org/en/Netherlands head office

Qustodian http://uk.qustodian.comJoint UK/ Spain head offices.

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Big Data Frontier These organisations are combining large and small volumes of data with internal and external sources and in structured and unstructured formats. Algorithms are the medium through which those data sources are being processed into curated information to give financial profession-als an advantage over their competitors. By using cognitive intelligence and ma-chine learning to calibrate and evolve these algorithms, in real time, financial professionals are discovering clearer mar-ket entry and exit signals. The following are examples of fintech companies who are searching for and consuming large amounts of data to inform these algo-rithms on a real time basis:

Dataminr https://www.dataminr.com/US head office

Datasifthttp://datasift.com/US head office

Kensho https://kensho.comUS head office

Market Prophithttp://marketprophit.com/US head office

Quidhttp://quid.com/US head office

C H A P T E R 5

The blockchain, a technology that could change the data industry forever

When creating Bitcoin, Satoshi Nakamoto wrote ‘what is needed is an electronic payment system based on cryptographic proof instead of trust.’25 He (for ease of reference I will refer to Satoshi Nakamoto as he) devised an inviolable universal ledger, which he dubbed the blockchain, against which anyone could verify the validity of the transactions, as well as the unique set of monetary incentives to encourage the network’s computer owners to keep the ledger up to date.

The blockchain can be used for non-currency purposes and in those circumstances it is often referred to as Bitcoin 2.0 blockchain technology or

Whilst conducting my research for this guide I came across a new technology that could change the data industry forever, it’s called the blockchain

even blockchain 2.0. Michael Mainelli and Chiara von Gunten of Z/Yen Group26

expand upon the blockchain:

The blockchain can contain sets of documents and record assets. In short, a blockchain is a secure peer-to-peer ledger with storage, analogous to peer-to-peer music sharing systems such as Napster.

You could have all of your health records or driving history available to share with trusted third parties at any time. You might hand over your health record to a new doctor or to obtain a life insurance quote, or share your driving history at an airport counter for a car rental insurance discount. Your

personal data store might also have your biometric data, thus giving you the ability to prove your identity at any time. There would of course need to be some sort of contract and time window within which the receiver of the data completes their analysis and then deletes the data but If this is possible through the blockchain it could transform the personal data industry forever. Mainelli and Gunten go on to say:

Blockchain technology and related applications could transform the way people manage identities and personal information. Blockchain-based identity schemes could empower people with personal data storage and management,

permission frameworks for access by third parties such as insurance companies, and even distributed reputation ratings. Individuals would no longer need to trust centralised third parties to store or manage their information.

Some companies to keep an eye on in this space are: ERIS https://erisindustries.com US head office, moved from the UK due to the proposed UK Investigatory Powers bill

Ethereum https://www.ethereum.org Switzerland head office Hyperledger http://hyperledger.com Now owned by Digital Asset Holdings – US head office Proof of Existence http://www.proofofexistence.com Developer behind it is from Argentina

C H A P T E R 6

Conclusion

Driving much of this expansion is the sophistication of the devices that we now routinely access and the proliferation of new technologies. Adding to this is the exponential growth in processing capabilities,27 that will drive both the use of data by consumers and the ability of businesses to analyse it. This is an exciting time for data monetisation as it is not something that will fade away; in my opinion it will gain greater focus as

companies adopt models that are based on trust. What are the forces that will drive companies to adopt new data monetisation practices? Competition: I’ve listed the companies that are gaining consumer trust and I’ve outlined a technology that could revolutionise the personal data market. As consumers, en-masse, wake up to these services and as new entrants combine these technologies to build new offerings, it will open a data market that enables real-time accurate analysis rather than predictions. This will, for example, influence the way companies design and develop products, it will

Data generation is now happening at a rate that is hard to comprehend.

Conclusion

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change the supply chain as companies will be able to better predict demand, it will give investors real time analysis of company sales figures and combined with financial benchmarks it will give a good sense of profitability. This will lead to more cost effective business models, better services, greater competition and an increase in the speed with which traditional barriers to entry are being brought down.

Greater loyalty and less churn: if consumers feel they are part of a company’s history i.e. they know that they have actively contributed to the development of that company, not just by buying its products but by helping the company fine tune and tailor its services,

the more loyal they will be. If consumers also know that their data is regarded as a valuable asset that is loaned to the company and used in a way that benefits them and other customers, this increases the feeling of empowerment and will result in even more loyalty and ultimately less churn. Glocal – ‘think global, act local’: to do this one has to understand the driving force behind the development of local businesses and the issues that people face but also understand the global trends that are changing businesses, markets, the environment etc. Many cities strive to glocalize themselves in the hope of increasing their influence in international affairs.

Conclusion

On a smaller scale, being glocal provides local communities with access to global resources, as residents are encouraged to partake in the drafting of sustainable policies for global and local business affairs. New data monetisation practices will power this glocal phenomenon as companies help communities combine their data to take advantage of mutually beneficial opportunities. Simplexity: the increasing sophistication of end users and the upsurge in their demands, requires complex services supported by elaborate models and detailed data sets. Simplexity describes the delivery of such services with simple front ends where consumers can easily tailor them to their needs. As new

technology is developed to do the heavy analysis, more and more consented personal data will be required to enable these simplex products. Consumers will be happy to do so as they realise the benefits. Once adopted en-masse, what might these new data monetisation practices lead to?

Most predictions fail to live up to their hype but as you’ve probable noticed, I’m very excited about the future of data monetisation and I feel optimistic about the outcome. Some of these are really out there but that’s the point, predictions need to be fun, so here follows mine:

Conclusion

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Greater innovation: Innovation isn’t optional it’s an imperative and today innovation is a result of collaboration. Rather than a polymath producing cutting edge work across a range of subjects and identifying a world changing product, service or idea, it’s groups of people that come together to make those discoveries. Solving the problems that matter is becoming ever more complex and it makes sense to believe these problems/ opportunities will be solved/ realised through collaboration.

Open source software, architecture and creative commons licenses will enable that collaboration as will the use of consented personal data. By using

that data companies will know where to innovate, they will tackle the hardest problems knowing that there’s demand for the solution and as their customers, who have donated their personal data to the cause, will support them, there will be greater comfort with ambiguity as they search for answers. That donation may not be entirely free but it will vest their customers’ interest in the outcome and perhaps it will mean they show greater patience and loyalty.

It is said that we live in one of the greatest times of change in the history of our species but I believe that with greater collaboration that pace of change will increase even further. Data monetisation, or maybe a better title would be data

Conclusion

democratisation, will facilitate an even greater period of innovation that benefits us all. New types of social network: the Internet of Everything will create a massive amount of data about everyone and everything in the world and as a result, personalised content – content you will want to read or act upon not just noise – will become the focus of social networks. Algorithms working on your behalf – simultaneously protecting and monetising your data – will create and dissolve content specific social platforms. Like a living neural network they will dissolve and create new neural branches – some branches will thrive and others will whither as they fail to

attract interest. The algorithms will search for content that’s relevant to you and if they can’t find it they’ll create a content specific social platform in the hope of attracting other algorithms.

These algorithms are not passive they will learn through creative destruction and they’ll become fitter. People will boast about the strength of their algorithms and the unbelievably good content they receive and act on, which improves their lives both socially and financially.

Subsequently, people will begin to trade these algorithms almost like a currency. But the algorithms won’t be a slave to their human creators they will

Conclusion

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market themselves to people or groups who are the best at creating content.

This will mean that there are no more centralised platforms (no Facebook, no Twitter etc.), leaving publishers and brands to build their own advanced algorithms that follow and participate in these stories rather than telling them. Individuals will spend their time focusing on more demanding tasks like creating original content and, more than likely, will work with others under creative commons licenses to give themselves an edge. This might sound far-fetched but perhaps companies like Insightfully are on that journey http://insightfully.co/index.html

New industries and more jobs: there are many industries experiencing shifts toward decentralised models that bypass middlemen gatekeepers, for example: hotels (airbnb) and taxis (ubber). People have figured out that if they have idle assets, they can lend them to people who need them, while those people have in turn realised that they don’t need to go through expensive central distribution points to find those assets.

Data is an asset and although on an individual level the amount of remuneration won’t change your life it could make a big difference to communities. Imagine mutual data centres where villages, towns, cities or even counties and states can combine

Conclusion

their constituents data and apply that economy of scale to projects that benefit them (better use of council and state money, new pressure groups) or monetise it for the benefit of the mutual (sell it to services that help companies and investors identify new trends early). Collective purchasing schemes are an example of such mutuals.

The partnership between 38 Degrees and Which in the big switch is an example of collective purchasing schemes and perhaps a portent of things to come https://secure.38degrees.org.uk/pages/the_big_switch_phase2

Greater philanthropy: by both consumers and companies as both

donate consented data to solve some of society’s bigger problems e.g., the environment, humanitarian crises, poverty and education.

Conclusion

C H A P T E R 7

Biography

My career started almost 20 years ago after I graduated from university with a degree in Economics. In the first 10 years of my career I developed a deep understanding of finance as an equity analyst and then as an investment manager. I enjoyed those years but the more I analysed and invested in companies the more I saw data change the business landscape. I wanted to be at the heart of that change so I made the jump to a technology focussed role with the monetisation of data at its core; it was a great decision and I haven’t looked back.

Over the last 10 years I’ve focused on innovation, product management, business development and sales roles that monetise data at large multi nationals and SMEs.

If you’d like to know more about me then please connect with me on LinkedIn www.linkedin.com/in/profitfromdata, on my website profitfromdata.net, or contact me directly at mark@profitfromdata.net

Mark Pearce

C H A P T E R 8

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