IT is still neglected in the sustainability agenda

At the same time, as another study by Capgemini shows, IT still plays no or only a minor role in the sustainability strategies of most companies (“Sustainable IT: Why it’s time for a Green Revolution for your organization’s IT”). Although half have their own sustainability strategy, only one in five companies includes IT in their sustainability agenda.

IT itself causes a CO₂ footprint but can also make a profitable contribution to CO₂ savings. However, only 43% of executives know the carbon footprint of their corporate IT and only 18% have defined a comprehensive strategy with clearly defined goals and timelines.

CO₂ footprint reductions of IT can be achieved primarily by optimizing and streamlining IT landscapes and architectures and require a corporate social responsibility (CSR) strategy that addresses the levers for reducing environmental impacts in the various business areas.

Introducing a framework for sustainable IT implementation

We recommend the following process to implement a sustainable IT strategy with clear and measurable goals, and defined milestones which also indicate necessary adjustments to the organization, processes, and culture.

1. Sustainable IT Employees
   Promote a sustainability culture that includes cooperation with cloud Hyperscalers like AWS, Microsoft, or Google. Adapt teams and corporate culture accordingly to achieve and accelerate sustainability goals: everyone must take responsibility for the environmental cost of cloud use.
2. Sustainable IT Strategy
   Define a corporate sustainability strategy including your vision of sustainable IT, embedded into a broader approach to CSR, and conduct a baseline assessment of the IT’s overall environmental footprint and sustainability maturity. For implementation, establishing a Sustainable IT Lead helps to control activities by KPIs and sets up the necessary governance.
3. Digitalization for Sustainable Business
   Analyzing the value chain and target areas of the current business will evaluate the measures and focus areas to assess improvements in the value chain, e.g., through disintermediation. Another way to achieve added value in the business is through a significant consolidation of extensive application landscapes with targeted shutdown of applications.
4. Assess and Calculate
   Based on data-driven business and IT assessments, a digital twin of the IT and application landscape helps to determine current CO2 emissions and simulate CO2 reduction potential. Decision criteria, defined in a joint approach, help to identify the potential for improvements within the IT landscapes and architectures. The effect of measures can be extrapolated and simulated. Combined with an on-premises facility (DC) sustainability assessment, consider the leverage of the power usage effectiveness (PUE) of cloud data centers by cloud transformations.
5. Design and Plan
   Architecture is the key for data center setup, landing zones, network, and communication. Building a sustainable IT platform will allow significant efficiency gains by moving workloads and storage to cloud, which consumes less energy.
   By using the cloud and automating the scaling of necessary hardware resources, large energy savings can be achieved. Cloud-based application landscapes achieve compute resource utilization rates of more than 60%, whereas classic on-premises data centers typically achieve utilization rates of less than 20%.
   While cloud computing – compared to the usage of traditional data centers – can play a significant role in reducing the use of energy, it causes extra energy consumption for required networking and communication. A sustainable architecture will thus strive to reduce network transfers and use efficient data transfer mechanisms, including the deployment of edge computing. That overall planning approach will be the foundation for a holistic planning based on portfolio analysis with focus on sustainable modernization paths.
6. Development and Green Coding
   In addition to switching to green electricity, data center emissions can also be significantly reduced by modernizing the workload through improved software architecture as the design of the software architecture impacts required hardware sizing and electrical energy consumption. Sustainable application development and application transformation with a focus on green coding thus needs to be a part of your approach to sustainable IT. Some companies have already saved 50% of the energy demand of optimized applications and reduced CO2 emissions accordingly.
7. Operate
   DevOps can contribute to a better energy management if DevOps processes and automation are used for continuous optimization. Establish site reliability engineering to create efficient, scalable, and highly reliable software systems through automation and continuous integration and delivery. Use modern DevOps tools and technologies, automated CI/CD pipelines, and tests to detect defects early in the process before they hit production. Use monitoring systems and act on the alerts before they turn into incidents. In this way, you reduce the waste of resources that can be used more effectively for other efforts.
8. Re-Calculate and FinOps
   Use calculators for on-premises and cloud-based infrastructure to determine the carbon footprint of your IT and establish greenhouse gas emission dashboards. Make sustainability part of FinOps to achieve maximum business value within the financial and environmental management of IT/cloud systems by shaping the use of resources.

Customer experience and engagement have been among the most critical business priorities for many years and in “anytime, anywhere” economies they directly impact the growth and profitability of media and entertainment businesses.

Our research shows that emotions play a far greater role in creating true brand loyalty than current approaches recognize. Building meaningful, deep-rooted loyalty requires driving emotional engagement and embracing a dynamic program of customer experience and journey management to deliver personalized content across all channels while anticipating each customer’s behavior and recommending content based on their preferences.

So, why do a majority of companies have lower-than-average customer engagement scores and struggle to deliver a consistent experience across content, services, products, and the entire ecosystem?

Neither customer experience nor customer engagement can be improved without a solid foundation of data related to the consumer (transactional, behavioral, and others). The more you know about your customers, the better the experience you can provide to them. Getting a deep understanding of any audience, including the audiences of TV shows, events, and celebrities, through their behaviors, interests, and affinity patterns is critical for reducing subscriber churn and increasing brand loyalty.

It’s not a lack of data that’s the problem. Rather, huge volumes of data are not in one place and organizations struggle to connect it all to provide a single, real-time view of the customer.

Some organizations are getting it right. They’re building customer intimacy on a single source of unified, trusted data. And they’re striding ahead of the competition.

On the ad sales side of things, media houses and publishers need to realign how to predict and sell content based on customers’ interests, while the advertisers reassess the best way to optimize their ad spend. Customer data is going to be pivotal for audience engagement, content monetization, and advertising revenues

One thing is clear: while identifying a customer and related data is a key step, applying this data and deducing actions to enhance customer experience, personalize content, and segmenting the audiences for marketing based on this data is equally or more important.

Done right, personalization goes beyond any single technology to focus on contextually relevant experiences that boost content monetization and advertising revenues.

In the past few years, organizations have introduced various initiatives to overcome this challenge by launching 360-degree single views of customers, based on data lakes. While these initiatives have brought some amount of success, they have not solved the holy grail of customer engagement across channels and departments. The challenge is the ever-growing data around customers, with the proliferation of channels clubbed with regulatory changes on third-party tracking cookies, device IDs, etc. These evolutions are pushing organizations to look at a more sustainable solution that would keep customer-related data unified to enable personalized customer experience, with flexibility to evolve with changing industry dynamics.

An overarching challenge is data privacy and consumer trust

Ethical data management is the cornerstone on which customer trust and loyalty are built, and customers expect brands to embrace ethical AI as part of this. Data use is also governed by increased global data regulation, which requires mature privacy, quality, and lifecycle management, while honoring the preferences and permissions set by your customers.

The customer (and related data challenge)

Customer data is the most scattered and complicated to manage and activate. Here are six of the challenges that must be addressed to transform customer data into sharp and relevant insights.

• How to match and unify individual customer data and insights, even with inconsistent identities
• How to handle massive real-time data ingestion, transformation, and storage
• How to create a unified customer profile with customer data and enterprise data
• How to give real-time easy access to data for business personas
• How to activate this data to drive and personalize advertising at the individual customer level – and this is the most vital requirement.

Make the move towards real-time, personalized customer experience

In today’s data-rich consumer landscape, the value of customer-data platforms (CDPs) is undeniable. Globally, businesses want to invest in these systems but don’t know where to start.

Designed to create actionable data by taking outputs (segments, audiences, etc.) that are pushed into the organization’s interaction platforms, a CDP is a set of data products built on top of enterprise AI and data platforms. It can give you the answers you’re looking for. Who are your customers? Where do they live? How old are they? How often do they transact with you, when, and why? In essence, a CDP ingests, organizes, cleans, enriches, and connects customer data into unified and trusted customer segments.

Advertisers will have access to focused audience and household targets and the ability to better reach their intended audience based on demographic and behavioral information collected from customer’s  first- and second-party digital platforms and linear TV and then enriching this with third-party data providers to gather more intelligence about audience to better segment for ad sales and content engagement. This is contextual data that can be leveraged across marketing channels.

The next challenge is how best to derive actions for your customers based on that contextual insight.

Engagement should ideally be in real-time because your potential customer will not wait minutes until you find the right message. The insight provided by your CDP enables you to tailor the next interaction or moment your brand shares with the customer, whether that’s initiated through a pull or a push from the brand. To optimize your customer data, it must be stitched together across devices or channels where the visitor currently interacts. When you know which device and channel your customer is using, you can create a more engaging experience.

The marketing side of the business will have the ability to connect to many downstream and upstream systems and provide demographic, behavioral, and geo-based based information for the ad sales and marketing team’s business-activation use cases.

The objective is to find the right balance between customer and business expectations from media publishers and advertisers all along the customer lifecycle, then deliver value at speed for everyone. How? By focusing on the moments that matter, nudging customers through a more engaging experience and a stronger value exchange in which they share more about themselves and what they value, and for the business to experiment, test, and learn. Finally, all this must happen with the consent of your customer and in compliance with data regulations.

Attraction: Metaverse Brand

Gen Z ers are gradually overtaking the workforce. These young people are in high demand as they are perceived to be more hardworking, which is an asset to employers. However, their unique characteristics make employers concerned about recruiting, retaining and training them.

Our company recently hosted a Metaverse Career Fair for students of five universities in the U.S. The event provided 2D access to an arena where avatars could explore job opportunities and interact with different elements on the platform. The “Hired in the Metaverse” event, hosted by Hirect, allowed candidates to experience the event in 3D by leveraging Oculus Quest 2 headsets.

Another avenue for leveraging the virtual world is career sites and job descriptions (JDs). The JDs have evolved from text-based documents to picture-based ones to videos. The next logical step in this evolution is 3D experiences that comprehensively showcase the “day in the life” of a typical employee.

Recruitment: Effective Alternative

The metaverse provides an efficient solution to bring the best of both physical and virtual approaches together. Interviews conducted on the metaverse enable recruiters to accurately assess the behavior of a candidate while saving a significant proportion of the cost associated with a normal recruitment process.

Samsung uses Gather Town. Indian startup Incluszon claims to be building a platform that enables interviewers to gauge the candidate’s confidence levels. Microsoft’s Mesh platform will overlay 2D videos with 3D rooms. The platform is expected to evolve from avatar-based interviews to VR/AR/MR-enabled ones with real faces in virtual settings.

A major impediment to this model, however, is the availability of hardware at the candidate site. One possible solution is to leverage the age-old concept of interview centers spread across a particular region, the only difference being that instead of a phone/desktop these will be equipped with metaverse parlors.

Onboarding: Welcoming, The New Way

Arguably, the most mundane part of any onboarding process is the scores of PDFs the new joiner must go through to get accustomed to the ways of the organization. This results in people skipping this strenuous act altogether or grudging their way through it—both defeating the purpose of onboarding.

If these PDFs were to be replaced with experiences where employees can walk around their future office, meet and greet their colleagues, and undergo interactive orientation classes, the results would be immediately perceivable.

Hyundai Mobis leveraged ZEPETO to design its new employee experience in the metaverse. The company paired this with an “untact online trip” where new employees chose from a host of European cities and enjoyed a two-hour online trip via YouTube Live. While not strictly related to the metaverse, the employee feedback was significantly positive, suggesting a possible application of metaverse in this realm. The growing trend of using third-party platforms suggests the need to clearly set boundaries in how employee data is captured, processed and saved on the metaverse.

Development: Play To Learn

The metaverse provides an opportunity for financial institutions to upskill/cross-skill their workforce through simulations in the virtual world. Partnering with specialists in “metaverse learning,” companies can introduce elements of gamification that engage the employee better, thereby reducing the time required to learn/develop new skills. Digital coaches, powered by AI/ML, can be used to assist and guide employees through the process, helping save human effort.

There is extensive research to support the claim that virtual world training, as opposed to classroom-based training, has significant advantages (learning by doing, safe place for differently abled employees, visual stimulation, etc.). Another advantage of the metaverse in learning and development is that it essentially works as a social platform. Employees can engage in simulations as a group and interact with one another during the training. This enables them to stay connected and build interpersonal relationships through personalized avatars.

Retention: Engagement Like Never Before

Keeping the employees engaged has been a challenge for companies during the pandemic. The metaverse can be an effective solution to this problem. Meta launched Horizon Workrooms in August last year as a collaboration platform. The platform enables employees to work together using avatars. It works both on VR as well as on the web and boasts features like mixed-reality desk and keyboard tracking, hand tracking, remote desktop streaming, video conferencing integration and spatial audio.

AI-powered avatars can be programmed to take up repetitive tasks, freeing up human effort for the more complex tasks. Such avatars can mimic human behavior with great precision. Another important avenue for metaverse application is rewards and recognition. Non-fungible tokens (NFTs) can be a great avenue for employers to recognize employee performance. Showcasing NFTs through employee marketplaces and monetizing can go a long way to drive employee retention.

Closing Remarks

While opportunities are aplenty, it is important that we do not put the cart in front of the horse. Although we have not delved into them in detail, there are numerous challenges that the metaverse as a technology must overcome before its scaled adoption can be realized across the use cases mentioned above. These relate to hardware requirements, data privacy, regulation and general reluctance in working with a new technology. Hence, it is critical that metaverse use cases are tackled in a phase-by-phase manner.

This article was first published on Forbes.com

eWallets’ market introduction

Forgot to bring your wallet to the supermarket? Since the introduction of the eWallets as payment method, this does not have to be a problem anymore. All BigTechs (Google, Apple, Amazon, Facebook, Tencent and Alibaba – also known as GAAFTA) have been developing payment services over the last few years, enabling payments with smartphones or smartwatches via eWallets.

All of them did this by acquisitions and setting up partnerships. Acquisitions were mainly aimed at getting the required payment technology capabilities in-house, while partnerships paved the way for quickly increasing market reach. Partnering with banks is still needed to connect the customer’s eWallet to a credit or debit card. Facebook was less successful in partnering with banks, and instead they invested in their own blockchain crypto technology to operate on Facebook’s Messenger platform.

Benefits for BigTechs

But what are the BigTechs’ strategies behind these moves? Looking at their business model, creating digital platforms, providing payment solutions is a logical next step in keeping the customer with them in another phase of the customer journey. With that, gains can be made at the expense of traditional banks:

• By taking a cut of consumer fees that banks charge as users embrace BigTech functionalities
• By shifting merchant fees away from banks
• By attracting consumer deposits (as we have seen happening in Chna)

Next to that, BigTechs enhance their existing business models with payment services by:

• Improving accuracy of personalised advertising by using payment data (e.g. Google), which can increase advertisement sales. As the head of Alipay Europe said “we are not a payment ecosystem, but a marketing ecosystem”
• Develop new functionalities, such as semantic search, by using payment data. With this a user can for instance get an overview of money spent on pizzas or groceries, which could attract more customers
• Offering a seamless payment method can increase hardware sales and brand awareness (e.g. Apple)

The current BigTech payment method still relies on bank’s credit and debit cards solutions, which made customer adoption easy. But eWallets have potential to evolve. The next step could be to develop account-to-account transfer-based solutions, e.g. enabled by PSD2 PIS and Instant Payments. As an even more bold step, BigTechs could develop proprietary e-money, cryptocurrency and/or payment account solutions. This would give them full control: no involvement of banks would be needed at all.

It’s not too late for banks yet

Is payments therefore another service where incumbent banks are losing their customers to new players in the market? BigTechs have a huge customer base ranging into the billions, so the potential threat is huge. Adoption rates of eWallets are by far largest in Chinese BigTech platforms (WeChat Pay of Tencent: 81%, Alipay: 69%) while US BigTechs have only 1 to 9% of payment service users. The US market is maturing, while in Europe there is still room to grow.

This means that there is still time for European banks to react. But how? It’s needed for banks to take an offensive stance going forward. They can build further on their reputation as a trusted and secure partner for their clients. Banks should leverage their customer knowledge and (enriched) data to develop financial information solutions addressing customer pain points. It’s also necessary for them to drive the scale and efficiency required to compete with Fintechs and BigTechs on a European or global scale.

At the same time, the regulatory developments within the industry need to be watched. Banks need to keep a close eye on the area of data reciprocity– regulation might one day level the playing field where currently banks are required to open up under PSD2, whilst other industries do not (yet) face similar obligations. At the same time competition law may further challenge the size and dominant positions of BigTech. Protectionism of vital payment infrastructures might slow foreign entrants.

My team and I are delighted that Snowflake has honored Capgemini with a special Media Competency Badge for accelerating innovation via the Snowflake Media Data Cloud. This is an important recognition of how we’re helping publishers, advertisers, and other parties prosper in an increasingly complex media landscape. 

The sector faces numerous challenges, but three data-specific ones stand out. 

To start, the way we consume media has grown more complex, making it more difficult to determine how brands should reach their target customers via advertising. For example, we no longer gather in the living room at a specific time to watch a program on TV. Instead, different household members engage with different media – at any time, in any place, and via a range of devices. This raises the technological barrier and complexity to achieve the insights that advertisers and partners require. 

Evolving regulatory environments compound the challenge. Laws protecting consumer privacy and governing data use are growing stronger. For example, when the California Privacy Rights Act comes into effect on January 1, 2023, it will prevent not only the selling but also the sharing of data. This will make it even more challenging for the industry to effectively identify target markets for advertisers. 

And, finally, that’s a problem because advertisers are increasingly demanding better proof that media buys are delivering the desired return on investment. Brands need to connect the dots between running an ad and generating sales – and that requires mixing data from several sources. 

These three factors combined create a huge challenge for publishers and media buyers: How can they identify their advertisers’ target markets, deliver the desired advertising, and track their success, without running afoul of strict regulatory environments? 

To address this, Capgemini has been working closely with Snowflake to design and develop data clean room solutions with end-to-end analytics capabilities. Snowflake enables marketers, publishers, and data and ad technology businesses to unlock their data for identity, insights, activation, and measurement across the advertising ecosystem. Capgemini’s clean room allows these stakeholders to perform essential first-party data matching, create audience insights, and activate and measure campaigns. It does this without exchanging data between the stakeholders, ensuring these activities remain compliant with all applicable privacy laws. 

While our data clean-room solution is important, Snowflake’s award also recognizes Capgemini’s ability to establish the right environment around the solution to help our joint clients achieve successful outcomes. We are leveraging both our technical expertise and our sector expertise to deepen the value of the clean room. 

The issues are significant, which is why we are sharing our thought leadership with stakeholders through venues such the Snowflake Media Data Cloud Summit held earlier this year. All sessions from that event are available to view on-demand and I encourage anyone in the media, advertising, and entertainment industry to watch them. 

My colleagues and I plan to conduct more roundtable discussions and, when we do, I hope you will attend. If you have any questions or comments, I would be happy to discuss them with you. 

Some innovations spring from a moment of inspiration, most are the result an ongoing commitment to people, process and technology. The real genius of people like Steve Jobs or Reed Hastings is not that they had good ideas, but they created environments where such ideas could become reality.

A company moving from physical products to intelligent ones needs to nurture a portfolio of innovations. There is no one-size-fits-all, but there are lessons from companies that do this well, and those that don’t.

The traps that stop you innovating

Before looking at best practice, consider the traps that block innovation. These need to be resolved before you start.

The biggest risk is a culture where staff and leaders are entrenched in their thinking. They see the status quo working, and want to keep going. They are either uninterested in change, or apply old thinking to new problems.

Microsoft provide a salutary lesson, and an optimistic one. As the world’s largest software company, it was well placed to dominate mobile. But it couldn’t get out of old thinking. It built its smartphone operating system based on its PC expertise, whilst Apple built the iPhone around user need. Microsoft failed here. But then, when many had written it off, it changed leadership, strategy and culture and began innovating with speed and agility – launching a string of sucessful cloud products and services. Now it is back on top.

Create a culture of innovation

For the most part, the best corporate innovations are those that support the existing offer, whilst opening new revenue streams.

A good example of getting this balance right is Coca Cola’s Freestyle custom drink maker. It’s a digital offer that supports the existing product line to creates richer customer experiences. It also gathers data on customer preferences that supports traditional R&D. Phillips Hue smart light bulbs are another example.

These types of innovations come when companies create innovation spaces that are distinct from the core business, but maintain overlaps. The core of this is the right people.

You will of course need people to manage your whole portfolio. But any individual idea chosen to take forward will its own team. Turning an idea into a profitable revenue stream needs a business case, enabling technology, and user experience. Any team must have right mix – strategists, engineers, creatives, designers, and managers – to bring all of these together.

This probably means new talent; people who bring new ways of thinking and understand how new technologies such as 5G, AI, or VR can help reinvent or digitise products.

But it also means looking at your existing staff – or inviting them to submit ideas – and bringing ambitious people across into innovation teams. They know the existing products, how they are used, what is missing. Some aviation businesses, for example, are sitting on a goldmine of jet engine data – they will need existing staff who understand what they have, and data and AI experts to understand what can be achieved.

This overlap also keeps communication channels open, so innovation is not seen as ‘other’. This is critical when you hit on a sucessful innovation and need the core business to start selling it. Good innovations fail when they are forced on a business that is not ready for them.

The innovation process

People need to be backed with processes.

Innovative companies create bench time for teams to come up with lots of ideas, as well as mechanisms to capture wider ideas from across the businesses.

For those worth pursuing, the usual first step is to setup a small team of 2-5 people to develop it into a real concept: research the need, create a prototype, validate, and write a business plan.

If it shows promise, setup an internal venture to progress it towards commercialisation. It’s important to set milestones and realistic time boxes. Creativity takes time, but constraints force people to come up with an answer. And ideas that don’t work should be culled quickly. Aim to succeed, but fail fast.

A portfolio of up to 10 projects is usually good for a mid-sized company, but pick a number that gives you several shots at success but feels manageable.

As ideas become viable products, make sure the core business has the tools, technologies, and skills to sell, support and update it.

One example we worked on is VRCare, a distraction therapy for pain relief. This started when a team member came across research on distraction therapy for burns. VR seemed to offer an opportunity. The team came up with a proposal, presented it to the company, and were given a small budget, people, and eight weeks. They built a low-cost prototype device capable of operating in a wet environment for burn debridements, a VR game, and ran a small study with a hospital that showed a drop in pain.  The complete product is now open source and available to everyone.

These portfolios need to be tracked, following the curves of time and investment, progress and revenue. There is no fixed formula for when to allocate more resources, and when to pull out, but informed decisions need to be made based on data and judgement.

Move fast is a safe environment

Disruptive innovation is not a moment of genius, but the result of a well-managed portfolio of ideas, with rigorous processes that allow them to progress within a stable business environment. That needs dedicated teams with the right mix of skills to understand user needs, deploy technologies, and build a business case. It needs processes to spot bad ideas and invest in good ones. And it needs open lines of communication between business units that allow the best ideas to eventually migrate smoothly to the core business. That is what has distinguished the great corporate innovators of the last 20 years.

How we can help

We can support your innovation teams through expertise in product design, user experience, new technologies (including AI, VR, 5G), product validation, and by providing an outside perspective to help frame the business case for new innovations.

Intelligent products and services face a trade-off between capability and privacy.

To be truly intelligent, these offers need user data on a vast scale, to build and train sophisticated AI.

Health devices capture data on heart rate, diet, genetics, or medical history. Smart energy devices collect data about in-home activities. Assisted driving systems need data on where you are, how you drive, and whether you have (inadvertently of course) exceeded legal speed limits.

This data can be sensitive.

To protect this data, it is encrypted. The problem comes when it needs to be decrypted to train the models that underpin intelligent products. This creates the possibility that private data is revealed to people working on the model, and the possibility for unencrypted data to be lost or stolen.

Keeping data safe is all the more important in the age of AI. With today’s mathematical capabilities, even anonymized datasets can be reverse engineered to identify individuals and draw inferences about their private lives (as Netflix once found).

The data sharing trade-off: more predictive power = more privacy risk

As data and AI skills permeate organisations, it becomes advantageous to share data more widely. The more data that experts can access – and the greater the diversity of people with access to data – the more value data can potentially bring.

But we may not want to risk too many people seeing personally identifiable data, or we may not have permission to share it (old clinical trial results may only have consent to be shared with the research team).

We may also want to share it beyond our organisation. Medical companies may want pool data on disease responses, or utilities companies on energy usage patterns, so they can all develop more predictive tools. But they want to do this without compromising customer’s privacy, or giving away IP.

This trade-off is often presented as ‘we can do data science or retain data privacy, but not both’. But this is a scale rather than a binary choice. Small, focused teams can work safely on unencrypted data. The more that data is shared, the greater the potential benefit, and the greater the risk.

The new privacy-preserving tools of the trade

Rules around protecting privacy are strict, and users will get upset if you abuse their trust.

Deepmind fell foul of this in 2017. It’s Streams app, using NHS data, predicted risk of acute kidney injury. But in building the app, un-anonymised medical records of 1.6 million patients were illegally shared with Deepmind. This life saving tool was eventually discontinued because privacy had not been adequately considered when using personal data to build an intelligent AI product.

• Federated Learning: This allows a model to be trained on data held across lots of different devices or servers. So, the model learns without ever taking the data off that device or making copies of it. It can be thought of as ‘sharing the model, not the data’, creating a global model which learns from the local ones.

• Secure Multiparty Computation: This enables multiple parties to work on data they don’t want to share with each other. It shares encrypted data between an agreed set of people within a network and allows them to work on a dataset made up of all party’s private data, without ever seeing the raw data.

• Homomorphic encryption: This allow data to be processed while encrypted. For example, it would make it possible to find data on people with arthritis from a wearables data set, run calculations on it, and create a useful model based on group-level insights without ever decrypting personal records. Homomorphic Encryption is gaining popularity and it is hoped that one day almost all computation will be done on encrypted data.

• Trusted Execution Environments: These is a hardware feature that create secure area on a device which can execute certain approved functions in isolation; our smart phones use these for biometric authentication. These could be set up for running AI models on private data without anyone having access to that data.

• Differential privacy: Even if modelers do not see the raw data,it may still be possible for bad actors to reverse engineer the model’s outputs to reveal personal identities. Differential privacy helps overcome this (and also helps maintain anonymity more  generally) It adds random noise to the data, which corrupt the datapoints, but preserve properties of the overall data set. Because the modeler knows the type of randomness, they can still construct an accurate group-level picture that is reliably predictive. But anyone who steals the data has no idea whether any individual data record is accurate.

What do privacy preserving technologies look like in practice?

These are not just academic concepts, these technologies are starting to be used seriously in real-world applications.

MELLODDY is a consortium of life sciences companies using federated learning to share drug discovery data. By accessing each other’s data, all participants can boost predictive performance of drug discovery models, helping them identify compounds for drug development. It uses a central platform containing machine learning algorithms and incorporating a privacy management system for data sharing.

The latest US census was released with differential privacy, in order to protect individuals from being identified while making aggregated population data available. And the UN PETS (privacy-enhancing technologies) Lab is testing a range of the above technologies to enable national statistics offices, researchers and companies to collaborate on shared data.

Making privacy-preserving technologies work

Nonetheless, the path is not entirely smooth. Privacy preserving technologies come with trade-offs. Where modelers don’t see the data, they need to send models back to the data owner to run them, slowing the process. Techniques like homomorphic encryption are computationally intensive. By obscuring data with differential privacy, you lose accuracy in some use cases.

No technique is a silver bullet. Preserving privacy will need layers of these technologies and careful thought to the right balance for your use case.

And, as with all data projects, good models need good underlying data. For privacy preserving technologies to work, the data owner needs to apply good data management practices. Since some modelers won’t be able to see the data, it is all the more important that it is curated so as to handle anonymous queries.

Finally, it is critical to note that privacy preserving technologies should not be an add on but a fundamental part of design. Any process that needs to share private data should take a privacy-first approach. Start by thinking about the privacy implications of the data behind the product, and bake in the right tools from the start so that you can get the insight you need whilst preserving user’s privacy.

When deployed from the start – with the right bedrock of data management, and agreements – privacy preserving technologies can help convince customers to share data, and to navigate the trade-off between respecting privacy and maximizing access to useful data.

The point of all of this is to do more with the data we collect. Broader, deeper and more representative data allows us to build more accurate, generalizable and useful models that underpin intelligent and personalised products and services. Doing this will be hugely valuable, but doing it means protecting and respecting the privacy of those who share their data with us.

How we can help

We have experience using privacy-enhancing technologies, including differential privacy, federated learning, and homomorphic encryption – all technologies that are hard to implement. We have a close eye on future developments, so we are ready to deploy advances as they arrive.

To help address the challenges discussed in this article, we can help deploy these in ways that allow private data to be safely shared within or beyond your organisation, to allow advanced modelling to be performed whilst preserving privacy. We can also help ensure the underpinning data sources and data management practices are of high quality, make sharing your data using privacy preserving technologies viable.

Capgemini Government Solutions (CGS) participated in full force at the first stop on the Salesforce World Tour in Washington DC. Salesforce brought together thought leaders and innovators to highlight how they have grown into a platform which is fully equipped to serve the government in a wide variety of contexts. World Tour DC was held in the Walter E Washington Convention Center decorated to the nines in Salesforce’s campground aesthetic. With many of our Capgemini colleagues based in the DMV, including our newest colleagues from our acquisition of VariQ, CGS had a tremendous showing at the World Tour. Many of us were reuniting for the first time since the start of the pandemic. The leaders of our Software as a Service Capability, including Fiza Petro and Paul Brown, flew in for the event!

Since 2007, Capgemini has been a global strategic consulting partner with Salesforce. We bring a wealth of Salesforce application development and operations & maintenance experience in the Federal and commercial sectors, backed by our large, dedicated Salesforce and MuleSoft practice. By bringing proven Federal, large-scale, complex case management applications delivery expertise on the Salesforce platform, we offer the most effective and lowest-risk solution to our federal clients. At the World Tour’s first stop in DC, it was great to see presentations and demonstrations of Salesforce features that we work with every day. Particularly, Salesforce’s Customer 360 which was emphasized throughout the event. Customer 360, as the name suggests, gives a complete perspective on the people and organizations that matter most to our clients through Salesforce’s vast suite of capabilities. Capgemini is proud to have worked with Salesforce to deliver Customer 360 since they launched the suite in 2020.

In addition to seeing many of our Capgemini colleagues, many of our Salesforce-using clients attended as well, providing a terrific opportunity to build stronger connections. Salesforce brought in representatives from federal agencies including the Internal Revenue Service and the Veterans Affairs Administration as well as officials from state governments across the country. Many senior decision makers from our client agencies attended, providing a great chance to engage and learn about where they are most excited to expand to next.

Even better yet, the fun did not stop once the convention was over! CGS was the premier sponsor at the Public Sector Networking Reception at the Planet Word Museum downtown (if you are local to DC and have not had the chance to go to the museum, we would highly recommend it!). One of our close partners, Carahsoft, hosted the event alongside us and proved to throw a spectacular event. We learned where we overlap with our partners and how we could join forces in the future to deliver exceptional work for the government.

Fiza Petro, who leads our Software as a Service capability, summed the day as “marking the beginning of the ‘new normal’ for conferences and industry events and CGS’s success at World Tour DC showed that we’re excited and we’re ready!”

New priorities are emerging in this quickly changing world. Sustainability now tops the global agenda as consumers and businesses labor to preserve our environment. Meanwhile, technologies like 5G bring an opportunity to amplify our efforts and make a real difference.

The automotive industry makes for an interesting case study of the tremendous potential of 5G. The 5G-enabled car will revolutionize driving by forever changing how consumers interact with their cars. Vehicles will be connected to a truly high-speed and low-latency network, superseding the limitations of 4G and enabling cars to communicate rapidly with surrounding elements such as signs and other vehicles, fostering the conditions for safe autonomy. Software-defined vehicles will drive their own shift in the market, as cars become more like intelligent devices with the same functionalities as smartphones.

All this evolution is new and exciting, but it is critical sustainability is not undermined in the manufacturing process. Circular-economy practices are no longer optional – they are critical, and many OEMs are awakening to this fact. Consumers are starting to demand sustainable operations from car manufacturers, with 24 percent now researching environmental and sustainability factors before making a purchase.

The circular economy is a model for economic development that benefits our society and environment by decoupling business growth from the consumption of finite resources. Circular practices include buying used products, repairing items instead of replacing them, and recycling whenever possible. Many of us are familiar with these habits in our daily lives.

How does this model look when applied to automotive? Here are the 7Rs of the circular economy under a mobility lens:

• Reduce – reducing the number and volume of resources going into vehicle production
• Reuse – reusing assets from older vehicles in new cars, e.g., tires
• Redesign – designing parts so they can be remanufactured
• Repair –repair of broken or worn-out parts rather than replacement
• Refurbish – restoring parts and systems to their original functionality, e.g., engines
• Return – establishing a recovery plan for different materials to be reused and repurposed for new and used vehicles
• Recycle – recycling metals, plastics, filters, engine oil, and other components to be directed back into the production cycle.

What is compelling about 5G is not just the milestone it marks or the connectivity it enables, but how it can help achieve global sustainability strategies. In the automotive sector, it provides the foundation for a more distributed supply chain with better tracking and tracing through connected sensors. On the roads and the assembly lines, car companies will need to work together with the right partners to extract the valuable data that will guide decisions for operational sustainability. This data can then be studied to determine the environmental impact of processes and to uncover ways of reducing the brand’s carbon footprint.

Drivers will also be able to monitor their own impacts with 5G and connected tracking, giving them the control they increasingly desire and empowering them to take their own preservative actions. It’s worth noting that the 7Rs are not just for manufacturers; consumers have their own part to play in driving what we call circular mobility.

The harmony between sustainability aims and 5G capabilities is the essence of green 5G. Technology will unlock a next-generation driving experience, but green 5G will ensure the value we harness can be sustained and leveraged for building a better future for us all.

Daniel Davenport is a Principal – Automotive Connected Mobility Solutions at Capgemini. He works with a range of global clients to develop connected use cases that drive innovation, enhance the owner experience, and create new revenue streams. He can be reached at daniel.d.davenport@capgemini.com or on LinkedIn

Cars have taken us where we needed to be for over a century. And automobiles themselves have completely transformed since their inception, becoming the safe and convenient form of transport we now rely on. The past few years, however, have accelerated this evolution dramatically and, for the first time, cars will be more than just a means of getting from A to B.

Software-defined vehicles (SDV) are leading the major shift facing the automotive industry. Consumers are increasingly purchasing vehicles optimized with software and data platforms which carry extensive functionalities. Just as the iPhone revolutionized our phones, SDVs will have the same effect, especially as the frameworks mature.

Our vehicles will offer the same personalization features as our devices, starting from a login that might involve a password, voice-activated mechanism, or multifactor identification for security. This will unlock individual preferences to benefit the owner and other drivers who, for instance, might have different mirror settings, preferred routes, and favorite radio stations.

Voice assistants will continue to evolve as a tool for getting more out of the in-car experience. Today you might ask your car to call a friend with a voice command, such as “Call Bob.” But in a software-defined vehicle, you might say, “Check email. Send a reply. Sorry Bob, but I need to push this meeting.” Then you propose a new time, as the car manages your tasks while you focus on the road.

People will soon also trust their cars to do the driving, especially as 5G accelerates the development of safe autonomy. In the meantime, drivers can benefit from the productivity, infotainment, and the conveniences we now associate with smartphones. And this will be delivered with a focus on safety and on not distracting the driver. This will automatically eliminate the danger of drivers picking up a phone to send or view a text.

Another advantage is flexibility. Ford recently announced it will sell Explorers without semiconductor chips, bypassing the chip shortage currently slowing down the industry. Drivers can install them later when they become available. This is a pioneering move in the industry, and it is only possible because of robust software architectures. Consider that, in the near future, you could equip your vehicle with new functionalities over the air, just as you might upgrade a component of your computer. In practice, this may be a transmission enhancer or augmented reality heads-up display.

Software-defined vehicles will transform the driving experience by enabling the personalized interactions drivers want, tailored to their needs, while giving greater management options. SDVs bring a potential that remains largely untapped and begs exploration, creating new opportunities for industry players and daring them to experiment.

Daniel Davenport is a Connected Mobility Solutions Lead at Capgemini. He works with a range of global clients to develop connected use cases that drive innovation, enhance the owner experience, and create new revenue streams. He can be reached at daniel.d.davenport@capgemini.com or on LinkedIn