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This wealth of data empowers businesses, governments, and society to serve their stakeholders with hyper-targeted solutions. It also helps inform which products to develop and enables some of the services consumers regularly use. A new wave of technologies and interdependent data relationships further enables (and adds complexity to) this process. In the era of big data and privacy-focused companies, individuals' information can reflect a spectrum of personal details. Every time we use an app to order a taxi or make a purchase, log our exercise data, look up directions, buy a coffee, or post a picture, a wide range of data may be collected - depending upon the platform and the choices made - adding to an individual's digital footprint. While consumers appreciate data-enabled convenience and personalization, they expect companies to use data in a principled way. Leveraging technology to process the vast amount of raw, ever-increasing data to make it actionable information goes way beyond traditional data analysis. While businesses embrace a historic opportunity to create data-driven insights, products, and services, how do we safeguard the privacy of individuals and keep them at the center of our product and solution design? In this issue, we explore how our data-driven world requires both individuals and organizations to balance data innovation and personalization with the collection and use of data. What do individuals expect, and how should responsible organizations engage? We examine the future of consumer engagement and how insight-driven institutions are building methodologies and ecosystems to help deliver hyper-personalized customer-centric value. We also spotlight the privacy-enhancing technologies organizations use to maximize data utility while protecting data privacy. Finally, we bring you the latest advances in spatial audio and 3D audio, where an acoustic revolution is underway and sonic profiles transform our virtual experiences.
Sinéad Fitzgerald Jeannie Hii Maja Lapcevic Elisabeth Riedl Nima Sepasy
Nic Buc Andy Capener Travis Carpenter Jean Crawford John Derrico Steve Flinter Jeremy Garber Brian Goncalves Kelly Grayson Keith Jordan Caroline Louveaux Alexander Purcell JoAnn Stonier Stephen Vasconcellos Ananya Wanchoo Florian Werner
Hayden Harrison Vicki Hyman Carolyn Ksiazek Ben Rubin
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Data-driven organizations access, create and integrate unprecedented amounts of information in our interconnected world.
In this issue
07
The empowered consumer and the future for financial data
06
The emergence of secure data sharing
05
Striking a balance: how companies can maximize data utility while protecting data privacy
04
Protect and serve: privacy versus personalization & rewards
03
In data we trust
02
Beyond silos: the next generation of customer engagement
01
A growing digital footprint
08
Spatial audio
Total Data
2020
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Apps
The average person has 40 apps installed on their phone. Out of those 40 apps, 89% of the time is split between 18 apps.
40
Time
New Apps
Data per Second
Data per Day
Mobile Use
APPS
There are 5.2 billion mobile users, which equate to more than 85 percent of all the people on Earth aged 13 and above.
mobile users
5.2
BN
downloads in 2020
NEW APP
218
The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 79 zettabytes in 2021.
in 2021
79
zb
In total, we created 2.5 quintillion data bytes daily in 2020.
Data BYTES DAILY
2.5
quintillion
On average, every human created at least 1.7 MB of data per second in 2020.
PER SECOND
1.7
MB
Users spend 4.2 hours daily on their phones.
DAILY
4.2
HRS
Beyond silos
Protect and serve
Striking a balance
The empowered consumer
Home
Sources: https://www.simform.com/blog/the-state-of-mobile-app-usage/ https://datareportal.com/reports/digital-2021-global-overvi https://time.com/6108001/data-protection-richard-stengel/ew-report https://www.appannie.com/en/go/state-of-mobile-2021/ https://www.statista.com/statistics/871513/worldwide-data-created/
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Beyond silos:
In the years to come, data will enable products and solutions that are ever more hyper-personalized and that will contain customer-centric value. As this happens, the transformation of customer engagement will be organized around three vectors: data networks, partnership ecosystems, and responsible data practices that align with consumer expectations and desires. Data networks Over the last decades, data networks have played a critical role in optimizing business operations and the way we reach customers. Across many industries, partners have engaged in information sharing to optimize their supply chains. Such supply chain collaborations are often managed through closed data networks, which can be expensive and limited in scale, but with significant value in terms of providing a structure for participants to share critical proprietary information. Closed data networks provided a safe mechanism to share information utilizing common frameworks, rules and security standards to protect the information they contain, as well as ensure that privacy and security of information is consistent as information and value is created across the network. But closed data networks evolved into other areas of the market, such as in digital advertising, where we have seen more open data sharing with few rules and standards resulting in data that is shared at a higher scale, and is highly effective at tracking data.
As society advances, the most successful companies will organize ecosystems to create new categories of data networks where they can share different kinds of information responsibly. Here, each participant will share targeted data to personalize their offerings and in return receive more integrated experiences. Cloud-based data exchange services are enabling this shift as they host large data marketplaces where companies can share their data. The possibilities for new data applications are endless. From professional sports we can imagine a single service that would connect retailers, sports leagues, and teams to other sport-tech creators to improve team sentiment, fan engagement, and experiences that tie the in-person and fan experience together. From connected cities we can envisage combining automotive and satellite data to revamp urban planning, alleviate traffic congestion, and create safe, inclusive connected cities for the whole population.
A great example of the potential of these connected data and data networks is emerging in the travel industry, where small and medium-sized enterprises (SMEs) are developing offers around a model of consumer permissions. Their platform allows travel providers, such as airlines, hotels, taxis, and more, to use and share consumer data to build unique experiences. Travel companies using such data can arrange to have a car waiting for a customer when they land, automatically update hotel or dinner reservations after flight delays, and have a glass of their favorite wine ready upon arrival at the hotel. Given the obvious benefits, it is no surprise that data and analytics leaders who collaborate by sharing richer proprietary insights generate three times more measurable economic benefits than those who do not.
Partnership networks Company partnerships are becoming more valuable assets than ever before because they connect a relevant third party’s offering with an existing customer. Previously, these agreements were difficult and time-consuming to organize. They involved issues related to proprietary information analytics, customer consent, legal and privacy concerns, and accurately tracking the value created by the partnership and the related fees due - which can be addressed by new partnership networks.
Tight partnership networks organized along supply chains to provide transparency to participants alongside guarantees to the end customers. For instance, the Mastercard Provenance Solution (MPS) helps brands get visibility into their product journeys with a blockchain-enabled solution. MPS transforms supply chain management across industries and verticals, bridging the gap between data silos and creating businesses’ transparency to make better decisions. This solution enables trust and accountability, essentially creating close cooperation similar to what has existed in, e.g., just-in-time car production across all sorts of ecosystems at scale with a simple deployment. Brands can then share critical product attributes, such as sustainability information and environmental compliance, to empower conscientious consumers to make more informed purchasing decisions.
New technologies also enable broader partnership networks. For example, a US airline-owned company has experimented with blockchain to track airline ticket sales with an extensive network of travel agencies and their due commissions. This network provides a source of truth for enterprises, which can then automate expense reporting for their employees. New data-driven partnership networks also include loose networks connecting millions of participants. A good example is a technology company, which provides an online community of 8 million data and machine learning practitioners with the opportunity to work with 50,000 datasets. It allows these practitioners to build models in a web-based data-science environment, entering contests to solve challenges raised by companies. Crowdsourced competitions reach across a broad range of industries and applications. For example, the NFL is launching a competition in partnership with a cloud service provider for automatic helmet assignment in videos of games. In previous competitions, the online community had helped detect helmet impacts between players. As a next step, the NFL wants to assign specific players to different types of helmets, which would help accurately and automatically identify each player's “exposures” throughout a football play and improve health and safety – with a prize of $100,000. As the future unfolds, companies will unlock more value for themselves and their customers by partnering more effectively with others.
Next generation of consumer engagement
For example, Mastercard’s Carbon Calculator uses data from all types of partners and industries to create a new tool for contributing to the fight against climate change. The tool is a partnership with Swedish fintech Doconomy and the Priceless Planet Coalition. It assists banks in empowering consumers to understand the environmental impact of everything they buy and ultimately offset their carbon footprint. The best companies will deliver more hyper-personalized value to their customers and engage them in unique ways. They will accomplish this by leveraging data networks, participating actively in data-led partnerships, and demonstrating social and ethical responsibility. In a world with endless options for consumers, this type of differentiation will allow companies to survive and thrive while earning consumers’ trust over the long term.
Source: 1. Gartner’s Sixth Annual Chief Data Officer Survey (2021) 2. https://unctad.org/page/data-protection-and-privacy-legislation-worldwide
1
Carbon Calculator View Demo
Data-led mission alignment Companies are increasingly engaging with customers and shareholders directly on ESG (Environmental, Social, and Corporate Governance). Most customer-facing brands will need to demonstrate how they align with the societal issues their customers care about – and that includes data privacy. Companies will have to prove how they actively protect their customers’ data and, more broadly, how they align with their customers’ emotional and ethical values.
128 out of 194
countries have legislation to secure the protection of data and privacy
2
Companies may face challenges leveraging data while navigating a dynamic regulatory environment. Yet, data-driven innovation and privacy compliance don’t have to be at odds. They can be one and the same. In fact, in today's business climate, when considering data-driven innovation, companies should take a deeper look at their privacy policies. Consumers have lived through a series of well-publicized scandals involving weak data practices by social media platforms. Public trust has been further eroded by frequent data breaches from all sectors of the economy and these same issues are top of mind for legislators and policymakers as they create new legislative frameworks. In face of these challenges, companies must continue to strengthen their commitment to and respect for privacy.
Broken trust In 2013 The Economist magazine coined the term "techlash" to describe a backlash against big tech. While this phenomenon initially suggested a hypothetical risk, it soon became a quantifiable trend. In the wake of the 2016 elections, over half of Americans lost trust in their preferred social media platform after Cambridge Analytica used artificial intelligence to mine their data. In addition, consumers concerned about how organizations
Consequently, the Pew Research Center found that most people felt that companies' data management practices posed more risks than benefits and that it is not possible to go through life without being tracked. And while nine out of ten people say that data privacy is important to them, only one-quarter think companies are doing a good job of handling data. Today, almost all consumers (91%) want to take back control, edit and delete their digital records, implying they’re not satisfied with the status quo. If not properly addressed, this broken trust can undermine digital engagement, erodes influence, and eliminates opportunities to deliver on data-driven innovations.
Towards a new contract Resolving the trust deficit to allow safe innovation requires moving the data privacy discussion beyond advertising use and the common perception that all data collection is nefarious. Tech leaders are calling for principled guidelines to meet the public's concerns and enable responsible data sharing to benefit society. Meanwhile, consumers are becoming more data-aware, even more so as digitization accelerated during the pandemic. They want to control whom they share their data with and benefit from that sharing. In a recent survey, 95% say that they want more control over how companies use their data. Early research shows they are more likely to embrace data-sharing when there is a strong value proposition for convenience or better experiences.
Looking ahead, as IoT and wearable technologies become more popular, retailers are exploring how they can harness properly consented data to optimize customer interactions, improve the in-store experience, customize offerings, and promote exclusive deals. TIM COOK QUOTE Businesses have a responsibility to individuals and society. They must exercise a duty of care towards the individuals whose private data they handle. Thus, to drive innovation and socio-economic development, we need to create an environment that can deliver the promise of the new economy while safeguarding data privacy.
Balancing rights with the opportunity While safeguarding privacy needs to be a focus of data-driven innovation, so does the potential for tackling complex societal challenges in fields such as health care, environmental protection, connected cities, and social inclusion. An example is in Sub-Saharan Africa, where traditional lenders historically overlooked 350 million unbanked adults because they lacked credit histories. Credit bureaus were the sole source of consumer credit information in the past, which lending institutions needed to build risk profiles. Incumbent financial institutions and fintechs can now offer services to this long-neglected group using alternative credit scoring.
Towards an ethical future? While data privacy laws have long been adopted before the introduction of GDPR, the EU regulation and approach called attention to a global privacy shift. Responsible data leaders have been developing new principle-based data practices and frameworks that constitute an emergent field of data stewardship.
"Data is a precious thing and will last longer than the systems themselves."
Prioritizing data privacy rights represents the most sustainable way forward for business, society, and consumers. This path may represent the only way companies can navigate their way through a shifting regulatory landscape, consumer "techlash," and eroded trust. Privacy and quality insights do not need to be at odds with the right approach. For JoAnn Stonier, this is the reason "responsible data is sustainable data." This understanding was the basis of Mastercard's six data responsibility principles, setting out the company's commitments to its stakeholders.
If the Fourth Industrial Revolution is the convergence of quantum, AI, nanotech, IoT, and more, big data is its lifeblood. Extracting the insights needed to keep this technological revolution on track hinges on the ethical data practices that can win back consumer trust so that individuals see that their data is protected, alongside a transparent value exchange. Consequently, the future will almost certainly be shaped by responsible data practices. Understanding the critical value of trust, the very best companies have embedded ethical data practices into their corporate cultures and brand narratives. As a result, these companies will be more transparent about the value exchange at the heart of the consumer-brand relationship and can potentially earn back a trust dividend from today's more skeptical consumers.
"Only better regulation can create an even playing field for innovation by placing the individual at the center."
As privacy laws inspired by General Data Protection Regulation are not always consistent and often in conflict, companies like Mastercard have instituted responsible data practices ahead of changes made to the law, re-establishing consumer trust. Accordingly, JoAnn Stonier, Mastercard's Chief Data Officer, suggests that "only regulation can create an even playing field for innovation by placing the individual at the center."
Yet, there are challenges with this historic opportunity to leverage the insights we extract from data to transform our societies. While policymakers increasingly recognize data privacy as a human right, the issue of trust is critical. As this right becomes established, ownership, consent, and control become vital considerations--although legislative standards vary from country to country. “Organizations have a role to play to build or rebuild trust in how they handle data … As you can imagine, no one can solve this on their own. Getting it right will require a collective effort,” said Mastercard’s Chief Privacy Officer Caroline Louveaux on balancing data privacy and cross-border data flows, at the Financial Times’ digital conference, The Global Boardroom.
Pay on Demand
A platform to accelerate digital inclusion by solving the challenges of device financing and by getting more citizens access to smartphones. This solution unites financial institutions, original equipment manufacturers, and telcos to serve the underserved in emerging markets.
Here, credit histories generated by combining data from multiple sources, like airtime usage, mobile money transfers, geolocation, bill payment history, and social media, enable access to first-time payment facilities and micro-loans. Through consent management, consumers decide which data they want to share. Very basic consent management exists today as most consumers are aware of lengthy terms and conditions that detail a company's data practices. However, sophisticated consent management tools would make this a more dynamic, interactive experience: shifting from overly detailed disclaimers that we typically do not read towards simpler, more concise, user-friendly language. Multi-level permissioning is the next stage of consent management and gives individuals more control over how third parties use their data. An example is a global technology company’s recent decision to permit consumers to opt-out of apps tracking their activity. Consent management also plays a key role in open banking ecosystems where open APIs enable third-party developers to build applications and services around financial institutions – and security is a priority.
Sources: 1 https://www.pewresearch.org/internet/2019/11/15/americans-and-privacy-concerned-confused-and-feeling-lack-of-control-over-their-personal-information/ 2 Mastercard Survey 3 Deloitte Survey 4 Mastercard Survey 5 https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust 6 https://www.retailtouchpoints.com/topics/customer-experience/nrf19-36-of-shoppers-want-better-personalization-but-hesitate-to-share-personal-info 7 The General Data Protection Regulation 2016/679 is a regulation in EU law on data protection and privacy in the European Union and the European Economic Area. It also addresses the transfer of personal data outside the EU and EEA areas
3
5
This new value exchange is already taking shape in retail. Because of the recent growth in online shopping, understanding customers is now less about in-person interactions and more about the key insights the algorithms uncover from sales, demographic, and social media data. Data insights provided by consumers are helping brands customize offers. More than 50% of consumers are willing to share information on products they like to get personalized discounts.
"The best answer in this unsettled regulatory environment and a marketplace demanding best-in-class innovation is ethical leadership. This is why we have developed Mastercard’s data privacy principles."
commercialized their data without consent began to worry about these entities misusing their data.
"Trust is a key part of the data value exchange - the task of winning back the confidence of consumers is of paramount importance."
JoAnn Stonier Chief Data Officer, Mastercard
7
4
6
Tim Cook Chief Executive Officer, Apple
Tim Berners-Lee Creator of the Internet
"If we accept as normal or unavoidable that everything in our lives can aggregated and sold, then we lose so much more that data. We lose the freedom to be human."
Learn more about Mastercard's principled approach to data
Data has the potential to fuel the next generation of innovation.
Modern economies rely on mining, refining, and leveraging data. Invisible tracking codes on apps and websites collect user data to build profiles for advertising. The data points collected go well beyond demographics, managing behaviors, and predicting purchase probability. People are becoming increasingly concerned about their data, demanding more transparency and control. However, they also value personalization and rewards. With the increasing vulnerability of data and privacy, ethical and responsible use of data becomes a growing concern. Companies are developing technologies to protect consumers and conceal an individual’s data by anonymizing it. Using algorithms, which can be developed in the user’s best interest, it is possible to make predictions and recommendations, retaining a user’s anonymity.
say they understand very little or nothing about what the government does with the data it collects, and 59% say the same about the data companies collect.
of U.S. adults
78
%
would share their personal data in return for better pricing, special discounts or exclusive offers
of CONSUMERS
71
want more control over companies using their data
95
say they have no control over the data companies collect from them
81
say they understand very little or nothing about what the government does with the data it collects, and 59% say the same about the data companies collect
+
Communities
protect and serve
The term PET refers to any technical method that protects the privacy or confidentiality of personal and sensitive information. This article looks at how companies could use certain PETs and other technologies to create best-in-class solutions while protecting personal information. These solutions can also reduce the risk of potential data compromise in the face of increasingly complex cyberattacks. Below we explore five approaches that companies can use: anonymization, differential privacy, synthetic data, homomorphic encryption, and artificial intelligence (AI).
Organizations are finding ways to respect data privacy while gaining the insights they need to innovate, the value of which is known as data utility. Historically, there were limited options available in the use of data. Privacy Enhancing Technologies (PETs) and other emerging technologies like distributed Artificial Intelligence (AI) have presented companies with new options to improve data utility while protecting individual privacy.
Anonymization In general, anonymization is a powerful tool since it allows to innovate with data while providing a high level of privacy protection as the data is no longer personally identifiable. However, anonymization methodologies must meet increasingly strict requirements under privacy laws as well as high expectations from privacy regulators. Techniques, such as k-anonymity, can lower the risk of reidentification while maintaining data utility.
Moving ahead: what the future holds As data-driven innovations continue to grow, organizations will be increasingly expected to deliver enhanced privacy alongside their new technologies. Indeed, whether it is regulators calling for the management of safe and secure processing of personal data, organizations prioritizing data protection for virtuous reasons, or individuals themselves demanding that their personal information is being handled in ways that are ethical, compliant, and to their benefit, PETs are here to stay. New privacy threats will continue to surface, requiring new solutions. Meanwhile, organizations will continue to innovate, leveraging a combination of various PET solutions and other emerging technologies to respect the confidentiality and protection of personal data while also driving product innovation.
Synthetic data Rather than sharing an original data set, companies are also increasingly turning to synthetic data generation, which utilizes varying mathematical approaches, including artificial intelligence, to create a statistically similar dataset. Synthetic data has close to the same statistical properties as the original without sharing specific values from the original data set. Thus, the beauty of synthetic data is that it can mimic the values and attributes of the original data set and its underlying properties and utility without including any of the underlying personal data. Synthetic data uses have advanced beyond model creation and can be used in production applications because of their increased utility.
How companies can maximize data utility while protecting data privacy
Differential privacy As an alternative or supplement to existing anonymization techniques, differential privacy has been gaining momentum as a tool for protecting privacy while enabling analysis. Differential privacy allows programmers to work with decrypted, aggregated results while protecting personal information. Differential privacy works when so-called “noise” is added to the data in a way that obfuscates information about any one record while preserving the accuracy of aggregate statistics. The more noise, the greater the privacy protection. Additionally, data users have a “privacy budget” that limits the risk of re-identification of an individual. After the budget is exhausted, further querying is impossible. This trade-off in noise and the size of the privacy budget can be configured using a parameter called epsilon. This ensures that individual records remain private from direct querying and cyberattacks aimed to re-identify data.
Artificial intelligence Historically, training AI has relied upon a two-tier process involving: (1) the data collection from multiple sources and storage at a central location and then (2) the learning itself. If the proper protections aren’t in place, this two-tier process risks exposing any personal information used to train the AI at both stages. New methods do not use the central storage and training model, addressing this exposure concern. One such method is on-device AI, which moves intelligence to smart devices (phones, automobiles, watches, speakers, etc.) at the edge of a given network. AI functionality directly on-device, of the risk to personal data is limited since it does not extract information from the user to a location outside of their control. Another method is to train AI on the devices themselves – known as federated learning. This approach allows edge devices to learn collaboratively within a shared framework while keeping all the training data on the device, providing additional confidence to data subjects that their data will not be transmitted.
Homomorphic encryption Another exciting new development in the PET world is homomorphic encryption. Before, if you wanted to gain any utility from encrypted data, it would first need to be decrypted to be processed. In contrast, homomorphic encryption enables computation directly on encrypted data. In other words, analytics can be performed
on encrypted data (using cryptographic techniques) and to produce a usable result without exposing the encrypted data during processing. If there is a breach of the third party, no unencrypted data is exposed. Today, the biggest limitation is processing power, as homomorphic encryption is computationally intensive. However, companies are already leveraging the technology to create commercially viable applications.
The synthetic data that's generated has the same insights as the real data but none of the private information is shared.
Synthetic DATA FOR FUNCTional ANALYSIS
Synthetic Data Generation
Real DaTA GENERATION
As the amount of data generated doubles within the next few years, companies will have unprecedented opportunities to use data-driven insights to support innovation. Meanwhile, we have witnessed a shift towards strengthening privacy regulations worldwide, with policymakers and consumers demanding increased protection. Following the enforcement rollout of the European Union’s General Data Protection Regulation (GPDR) in 2018, a steady increase of data protection laws has been adopted at regional and national levels across the
globe. While these rules lay down the foundations for ensuring the protection of personal information, where they have perhaps fallen short is how to do that in a world where technology – and much of the data that fuels it – relies on multiple parties and co-creation. Facing this challenge, many data-driven companies are currently working to establish frameworks across various stages of the data lifecycle: from data collection, storage, sharing, usage, archiving, all the way through to data deletion. The data sharing stage can be especially problematic because 75% of people distrust how their data is shared. Operationalizing secure data sharing can be a differentiator for many companies to enhance the speed of innovation and accelerate cloud computing. Luckily, new technologies allow access and data sharing securely and without compromising individual privacy.
Welcome to the rapidly evolving world that can enable safer data sharing including: of Privacy Enhancing Technologies (PETs), data management, and collaboration technologies, including consent management and secure data rooms- and sandboxes. PETs, as described in the data utility and privacy article, are technical methods that protect the privacy or confidentiality of personal and sensitive information. Here are two ways companies can use certain PETs and other technologies to manage and share data securely.
Secure multiparty computation With advances in computation and privacy technology over the past few years, companies can create shared outputs over discrete datasets. Secure multiparty computation (sMPC), an example of these advances, uses complex algorithms to glean insights from different companies' data. It is a technology that allows for computing values across multiple data sets without sharing the data. Instead, values are split up and shared across privacy zones, such that the individual values have no meaning, and none of the privacy zones can reconstitute the data themselves. However, the aggregate of all of them remains the same. This cryptographic technique allows multiple parties to run computations on data sets while protecting the underlying information from the parties involved in the computation. For example, imagine a group of people looking to find out average salaries – they might not want their details in the public domain or shared. By using secure multiparty computation, they could securely calculate the figure without ever sharing their details. sMPC provides possibilities for privately sharing data between companies to cooperate and gain mutually beneficial insights from that data, providing another path forward balancing privacy and data collection.
Regulatory sandboxes
What does the future hold? The future of ethical data collaboration will put consumer privacy and the advancement of new technologies front and center. While this future remains uncertain, it almost certainly involves much tighter regulation around consent management and protecting privacy - data sharing will be granular and consumer permission-based. Regardless of how this unfolds, consumer privacy will continue to drive ethical innovation.
distrust how their data is shared
75% of people
Secure Multiparty Computation
Sources: 1 https://medium.com/callforcode/the-amount-of-data-in-the-world-doubles-every-two-years-3c0be9263eb1 2 https://gdpr.eu/what-is-gdpr/ 3 https://www.internetsociety.org/wp-content/uploads/2019/05/CI_IS_Joint_Report-EN.pdf
Privacy enhancing technologies
PETs allow companies to protect data and provide privacy while delivering differentiated data products and insights. Technologies such as synthetic data, differential privacy, homomorphic encryption, and secure multiparty computation are just some PET approaches in a rapidly evolving space. While use cases vary, many businesses are looking to PETs to solve regulatory pressure limiting data use, support product development for privacy by design, and analyze more data using AI models to achieve more accurate results. As new data types allow for new insights, PETs increase the utility of data (data value) while protecting individual privacy and will be a foundational component of the new data economy.
Data SET PARTY D
Data SET PARTY C
Data SET PARTY B
Data SET PARTY A
SHARED OUTPUTS
The data elements within each data set are shared in a way where individually they are meaningless but when combined they provide meaningful results without the underlying data ever being exposed.
“The metaverse will dramatically expand the creation, collection and sharing of data – including very personal information. This raises complex privacy, security and ethical questions requiring dialogue amongst a diversity of cultures and disciplines.”
Regulatory sandboxes could also be explored to enable safe and trusted data sharing projects. Regulatory sandboxes can generally be defined as a controlled environment wherein for some defined period of time, companies are closely collaborating with a regulator(s) to test new data uses/sharing and technologies
while receiving regulatory guidance. Regulatory sandboxes have multiple potential benefits. Such sandboxes provide a safe space for companies to test their innovations against regulation and policies and give them greater incentives to innovate more quickly and responsibly. In addition, regulatory sandboxes can help policymakers and regulators understand the implications of various policy and regulatory choices, keep up to date with the latest innovations and technologies, and more broadly enhance trust across the
data (sharing) ecosystem. While regulatory sandboxes have been successfully launched in a number of key markets such as the UK and Singapore, more is needed to further promote the development and use of regulatory sandboxes for privacy and data around the world.
“Regulatory sandboxes offer an opportunity to accelerate responsible innovation by providing a safe environment to test new ideas and technologies, and build bridges between industry and regulators”
Caroline Louveaux Chief Privacy Officer, Mastercard
Technology is transforming the financial services industry at the speed of light, and "fintech" innovations offer immense value to consumers and businesses alike. One element fueling this innovation is increased access to account level financial data. Along with the wealth of data generated daily, an individual’s financial data footprint is growing equally. The emergence of open banking is empowering consumers to have greater access, more control and benefit from their financial data. Simultaneously this introduces the need for standards and safeguards that protect all parties across the financial system, most notably the consumer.
Such innovation places consumers in control of their financial universe by providing access to personal financial data and facilitating the permissioning of that data to a wide variety of applications and services. However, advancing consumer empowerment and unleashing the potential of tapping into one's own data is wholly predicated on the consumer's right to own, access, and easily make use of their data. Consumer-permissioned data in the financial services space is data created by consumer financial activity (such as banking, payroll, tax, insurance, and wealth/investment) and authorized or permissioned by the consumer for use in their chosen applications or services. This marks a tidal shift in the
financial services landscape. Historically, financial data access and its use was cumbersome as it resided on monthly paper or PDF-based account statements or pay slips. However, over the past several years, technology has made it possible for consumers to better understand their financial situation through personal financial management and budgeting tools that automatically pull their data from their financial accounts. Consent management, as described in this article, is at the heart of this discussion.
a Mastercard company, is an open banking platform that empowers consumers to permission their financial data to improve their financial health. This is possible through data connections to financial institutions. Financial services centered on consumer-permissioned data have the power to provide choice, as well as increase financial inclusion and wellness for consumers, while at the same time promoting competition among companies. However, as with any innovation, it is imperative to build with an eye focused on goals and outcomes. No one group can determine the way forward alone. All players in this evolving ecosystem must all work together to align product innovation with consumer protection.
In sum, industry leaders and regulators should collaborate to create a safe and secure ecosystem where market participants can bring new products and services forward while respecting existing rules. Individuals also need to trust that their personal information is being handled responsibly. Investing in superior privacy and security measures drives every participant in the financial data ecosystem to deliver their very best and provides consumers with the privacy and security they deserve. Ultimately, the consumer’s right and ability to unleash their financial data to their benefit will profoundly impact financial literacy, planning, and peace of mind.
For a while now, large organizations have been leveraging their business data to improve efficiency, gain market insight, increase profitability, and enhance customer experiences. With open banking and the innovations built on top of it, individuals and families are also now starting to realize the value of their financial data in making wise financial decisions, accessing credit, and enhancing their overall financial health.
and the future for financial data
Read full report
More recent innovations enable consumers to permission the use of their financial data to streamline the loan application process, access new loan types or better loan terms, and even improve their credit scores. Strengthening connections between consumers and third-party financial resources results in a more inclusive financial system. These opportunities provide individuals and families across the socioeconomic spectrum access to financial tools once reserved for a limited few. Whether it's personal financial management tools, the ability to proactively contribute data to credit scoring, or participating in peer-to-peer payment platforms, access to personal financial data is changing lives. For example, Finicity,
The Finicity Perspective: The empowered consumer and the future for financial data
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For more information on these topics or other digital topics of interest, please contact Mastercard Foundry at: foundry@mastercard.com or your Mastercard account representative.
Silicon Valley is currently awash with metaverse fever, and if we are to believe the hype, the holy grail of a virtual world appears to be within our reach. Yet, people crave real-world, lifelike experiences in the virtual world too. As this 3D immersive environment comes into view, developers are working around the clock so that technologies can better approximate real-life experiences. This trend applies to sound too. The excitement in the metaverse is driving a concerted effort to enable interactive acoustics to
The Importance of sound The pandemic and global lockdowns have utterly changed our sound patterns, especially in larger cities. Sounds of nature replaced the sound of traffic - people heard sounds they had never heard before. A new sonic-scape changed the ambiance of cities as people became more aware of the impact of sound on their mood and behavior. Sight and sound are known as the two higher senses and the basis of emotions. Yet, it is sound that
more explicitly conveys feeling. Using the expressive qualities of sound, we can impact and alter the subtleties of emotion to create a more powerful connection than visual processing alone. Take, for example, the sounds of a sporting event. As professional sports have slowly returned to empty stadiums, it’s more apparent that the crowd's noise enhances sporting events both for the viewers at home and the players in the stadium. Walking into a stadium without the usual fans cheering makes for a sterile atmosphere, which filters onto the playing field. As a result, recent games lacked the intensity of previous seasons. We could perceive a vital link between athletes’ concentration in subdued sonic situations and their responses within a packed stadium of noisy fans.
Broadcasters also realized that something was missing from the usual experience. Soon, they began to simulate crowd noise to build a more vibrant atmosphere for viewers. Surprisingly, they turned to the world of gaming for their solution. A videogame publisher supplied the crowd noise for the Premier Leagues' stadium soundtrack from the FIFA 20 console game. Using a system called “Atmospheric Audio, " they provided 13 hours of game sounds, made from 1,300 individual assets to the English
Premier League and the Spanish La Liga for live broadcasts. They created audio samples for specific moments, such as fouls when goal scoring. The producer watching the match then inserts the exact sample into the audio mix in real-time to better convey how people would react if watching live. There are ‘home’ and ‘away’ versions for the different teams in each match to create an even more immersive sonic scape. The away team is panned off to the side to mimic the typical placement of away fans in one section of the stadium. The sound of home fans is presented more loudly for realism. Interestingly, this feature was also broadcast through the public address systems in the stadiums for the players’ benefit. While this proved effective, it led to an 'audio-visual disconnect,' resulting from the crowd noise while viewers saw rows of empty seats. The inclusion of seat covers and billboards in the stadiums later addressed this.
Videoconferencing fatigue is an audio problem These new advances in acoustics may also transform teleconferencing. As we attend more video conferences every day, we are all experiencing call fatigue. After a call with multiple participants, we feel drained because the audio mixing and voices can significantly overload the brain, constantly assigning the sounds to the visuals. In a real-world conversation, the brain can localize sound sources in the environment, match the visual and audio sources, and create a ‘directional scene.’ Using spatial audio in virtual meetings creates a natural acoustic scene, saves our brains unnecessary background cycles, and reduces the cognitive load - making meetings more efficient and less stressful.
The differences we see across video conferencing platforms are visual and constrained to the front-end experience. We will soon see a revolution in collaborative media driven by sonics. We will move away from a mono-sounding world and into a 3D spatial world. Due to significant sonic upgrades to video calls and center stage cameras, voices will be locked into the direction of the device screen, so walking away or towards the device will automatically and subtly add the sound localization using machine learning. An audio-chat social network has recently announced spatial audio support on certain devices. This change will allow them to build a sound stage akin to an actual room. Instead of entering an audio-chat room and getting a mono, flat sound experience, they will use 3D audio to place people around the ‘room’ to allow for a ‘café’ feel to the occasion. As the digital worlds bleed into the physical world, we will rely on spatial audio to enable more natural communication, provide a sense of presence, and reduce cognitive overload in visual environments. Virtual Reality is not just a visual experience, it is multi-sensory, and sound is a critical component in creating realistic simulated environments. Spatial audio will allow programmers to develop immersive content where sounds can come from any direction. From the sea splashing at your feet to the wind blowing around you to hearing voices behind you, audio that responds in real-time using head-tracking will become a potent source of engagement in the future.
Into the Metaverse Augmented technology will give us different ways to perceive the world that will go way beyond sight, sound, touch, taste, and smell. As we transition into the metaverse era, the use of spatial audio, in particular, will help us achieve a profound sense of presence. Realistic soundscapes will transport listeners to faraway places, evoking powerful emotions. Spatial sound will bring 3D depth to digital experiences from all around us. We will feel we are ‘there’ wherever there may be. Innovations in spatial sound represent the most significant disruption in acoustics since the shift from the silent movie era to the talkies. With the upcoming acoustic revolution, we may unlock the promise of the metaverse.
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Spatial Sound
Stereo Sound
create more emotional, atmospheric, and dramatic experiences. As a result, the latest developments in acoustic technologies can place interactive dynamic sound all around you - a breakthrough known as ‘spatial audio.’ This goes well beyond improving the way you binge watch your favorite series. Spatial audio gives you a sense of space beyond conventional stereo, allowing the user to pinpoint where a sound is coming from, whether above, below, or a full 360 degrees around you. While stereo enables you to hear things in front and to the left and right, you don’t get a sense of surround, nor from sounds above or below you. With the introduction of a new 3rd acoustic dimension, adding depth, you get a sense of the exact location of sound sources.
Listen to some examples of spatial audio below Please use spatially enabled earphones such as AirPod Pros for the best experience.
the revolution will be amplified
Why is spatial audio so important? As our new world embraces remote working, spatial audio will facilitate better and more realistic virtual communication. Spatial audio will become even more key as we start working and collaborating in 3D worlds. Using spatial audio in 3D worlds allows us to use our natural senses to navigate and interact without cognitive reasoning. Instead of using visual communication as the primary sensory mode, we use sonics to reduce visual overload. Natural conversations allow us to work and engage with each other for extended periods and add a deeper emotional connection.
Spatial audio-enabled virtual meeting platforms are the future of video conferencing and collaboration. Distributing each person's voice in a 3D audio space makes conversations and discussions feel more lifelike but, most importantly, creates a different and more engaging atmosphere. It enables the angle of the sound source to shift as your head moves and increases immersion by further mimicking the real-world audio by adding levels and distance. To further mimic a real conversation and increase sound localization, we can also use head tracking to allow head movements and sync audio levels.
As individuals generate more significant amounts of data, insight-driven organizations are creating digital customer profiles that are rapidly extending outside of straightforward buying preferences and into the realm of understanding human behavior. While companies make a concerted push to hyper-personalize their products and services, they are increasingly looking to virtual reality experiences for consumers. As this unfolds, the role of sound provides a more immersive experience and can drive emotional engagement.
