In any given product, up to 80% of its lifetime emissions will be decided in design[i]. Design choices affect the materials used, in-use emissions, and end-of-life processing. With growing pressure from customers, shareholders, and regulators to be more sustainable – engineering businesses understandably want to make more sustainable design decisions.

One can point to thousands of potentially sustainable design decisions. Airbus is exploring replacing composite materials with bio-based composites[ii]. BMW is considering what materials it can reuse from its cars at their end of life[iii]. Automotive and aerospace companies are looking at sustainable fuels, from biofuels, to syngas, to hydrogen.

These are all exciting possibilities. But the question is not ‘should we make our products greener?’. We should. The question is, ‘in a world of millions of exciting possibilities, and limited time and resources, what is the best combination of decisions to cost-effectively meet sustainability goals whilst maintaining technical performance?’.

Making emissions reductions decisions in a messy world

Most sustainability decisions involve trade-offs. New materials and fuels have knock-on effects for other elements of design, supply chains, and end-of-life disposal. Switching inputs from virgin materials to waste streams is good, all else being equal, but not if shipping them to your factory creates more emissions than mining them locally.

And all this needs to be considered in the context of practicalities such as cost and ensuring sustainable replacements perform well enough. There is no point making a sustainable product that cannot be manufactured, reduces safety or efficiency, or makes your product so expensive that you lose customers to an unsustainable alternative.

Sometimes a seemingly good decision is not as good when you’ve worked through the lifecycle, whilst those that didn’t seem immediately obvious can have an outsized effect. Sometimes leaders’ personal passions jump to the front of the queue when better ideas are out there. And sometimes, your instinct is spot on.

It is impossible to know what the most sustainable design decisions are without good data and models.

Modelling the future

Sustainable design models are still quite immature, but they are coming. Most companies do Lifecycle Assessments which involve gathering data on their products, to calculate emissions. Most have digital design tools that allow them to model different design decisions and PLM systems that let them track products across their lifecycle.

But connecting these worlds is challenging. These systems lack real-time product and supply chain emissions data. The models within them are usually physics-based or hypothetical projections based on industry standards, rather than on what is actually happening in the real world. They can give approximations, eg the carbon savings of swapping one material for another based on industry standard calculations. But they cannot show the real impact right across your product’s lifecycle.

Ultimately what design engineers want – and what leading corporate innovators are now moving towards – is real-time systems-level models. These would use sensors, tracking, and reporting mechanisms to collect real-world data – eg on materials tracking, supplier energy use, product in-use emissions and so on, which all feed directly into the model. So when a hypothetical change is made, designers can see how that ripples through the system across all lifecycle stages

The idea is something akin to self-service financial reports – where financial teams can play with projections to understand the business impact of different future scenarios in just a few clicks.

Such a ‘model-of-models’ might have a dashboard where users click on a part of the product, then use drop-down menus to switch inputs between, say, primary steel, secondary steel, new biomaterials or innovative manufacturing technologies. The user interface would then show them what impact that has on everything from total emissions across the supply chain and product life, to technical elements (weight, aerodynamics, etc), and manufacturing requirements (eg what new machines would be needed).

That would let users experiment with new design ideas in silico and make decisions about which emissions reduction initiatives are most worth pursuing from a cost-benefit perspective.

No one is doing this at this level yet. But many are heading in that direction.

A model of models

Such real-time data is increasingly possible to capture through connected sensors. Setting up such data capture systems remains a mammoth task, but companies are heading in this direction as they already understand the benefit resulting in gathering and using this information.

Once the data is captured, the big challenge is turning it into meaningful insights.

Solving this means not just deploying technologies, but linking up data and models of multiple complex systems.

So, for a plane, we may have a model – or a digital twin – of the aircraft itself, with a list of every material and process needed to manufacture the plane, its components and sub-components. When you change a part in the user interface, the overarching model pulls data from various sub-system models.

It would look at the part’s supply chain model of costs and emissions, which is being updated in real-time by the suppliers – and runs the change to see what effect it has. It does the same for other models along the value chain – customers, service engineers, recycling companies.

The updated emissions values would then feed into your own company emissions model (e.g. how would the new material directly impact your energy use?); and to a model of the plane’s in-use emissions (how would the lightweight material change fuel consumption?).

So when you switch one input in the frontend, you get an updated total score for environmental impact and cost.

The journey to joined-up product modelling

Many companies are already on this journey. As we discuss in a previous article, work on gathering data for life cycle assessment (LCA) will provide the foundation of these models. Many are investing in data collection and Green PLM tools across their business.

As these mature and data collection improves, they can be adapted and combined to provide detailed models which inform strategic decisions about both product design and reinventing production processes for the circular economy.

But to do this, some complex challenges need to be overcome across the organisation, in terms of data collection, management, and curation, as well as IT architecture. These challenges and their solutions are explored in our next article.

[i]https://www.capgemini.com/insights/research-library/sustainable-product-design/ (citing https://joint-research-centre.ec.europa.eu/scientific-activities-z/sustainable-product-policy_en)

[ii] https://www.airbus.com/en/newsroom/stories/2021-04-this-new-class-of-materials-could-transform-aircraft-design

[iii] https://www.press.bmwgroup.com/global/article/detail/T0341253EN/the-bmw-i-vision-circular?language=en

We believe quantum technology could be a crucial tool for creating a step change in our approach to sustainability. However, we must set the right expectations for the role quantum can play.

In 2015, the United Nations (UN) and its member states defined a collection of 17 global goals designed to be a “blueprint for achieving a better and more sustainable future for all.” These Sustainable Development Goals (SDGs) address global challenges, including poverty, inequality, climate change, environmental degradation, peace, and justice.

Seven years later and the recent COP 27 conference in Egypt has highlighted once again the urgent need to act on these goals. The world faces a confluence of ever-increasing pressures, from continued global warming and increasing carbon emissions, to fears over rising poverty and inequality due to macro-economic and geopolitical tensions. Unless we act effectively, our attempts to achieve these SDGs could be thwarted.

We believe that one of the solutions is quantum technology, which offers a range of levers to help societies act on sustainability. Right now, it’s crucial to emphasize the watchword is potential. We are a long way from meeting net zero ambitions or achieving our SDGs through the application of quantum.

Yet we also believe it’s crucial to explore every avenue. Quantum is one long-term route to change – and we should start investigating this technology at the earliest opportunity.

Why emerging technologies can be part of the solution

Quantum is the latest in a long line of emerging technologies that will change how we interact with the world, and how it interacts with us. Just like cloud and big data before it – and alongside artificial intelligence, blockchain, and the metaverse during the next decade – quantum technology is likely to have a transformative impact on society.

But before we talk about how, it’s crucial to recognize that society’s application of technology also needs to change. For all the millions of people that have been empowered by easy access to information on computing devices, there remain large swathes of the global population that are unconnected and unaffected by the information revolution.

What’s more, technology remains one of the big contributors to global emissions. The scope 1, 2, and 3 emissions generated by the IT industry are responsible for about 4% of CO² emissions globally – one and a half times more than those generated by the aviation industry. Worse still, this level of emissions is projected to grow steadily in forthcoming years.

We must do all we can to slow this trend. If we are to deliver sustainable development, then we must think much more carefully about the technologies we use and the impact they have on society and the environment. When we introduce quantum technology, the goal must be to deliver a positive result for sustainable development, where the key is mindset.

How quantum technologies can help us deliver on our SDGs

Quantum comes in three main forms: quantum computing, which promises exponential increases in processing power at scale; quantum communication and security, which offers new mechanisms for data protection and secured communication; and quantum sensing, which involves a class of sensors that promises higher levels of measurement sensitivity.

These technologies are at varying levels of maturity, but they’re also moving out of the laboratory and into real-world applications. This applicability to the UN SDGs is stronger in some goals than others, and we believe quantum could deliver a big impact in several key areas and the related industry sectors. Here are some use case examples and potential applications:

• Affordable and clean energy – Simulation using quantum algorithms for research and development in battery, solar, and nuclear power; optimization of power grid and energy operations to improve performance; and quantum-based machine learning in energy management, power generation, and predictive maintenance.

• Good health and well-being – Improving the process of in silico drug discovery for the design and development of new drugs; optimize chemical and biological processes; enhance automation of pathology and imaging analysis as part of the diagnostics process.

• Industry, innovation, and infrastructure – Optimization of transport, utility, and telecommunications systems; real-time optimization of production flows, demand forecasting, and supplier risk modelling; and using quantum simulation to create stronger concrete, find efficient catalysts for manufacturing, and boost fluid dynamics.

• Sustainable cities and communities – Optimizing traffic routes, public transport, and resource usage in smart cities; using algorithms to build efficient buildings and simulate healthier environments; and applying machine-learning capabilities to equipment maintenance, intelligent surveillance, and crowd management.

Conclusion: A plea for consideration

Other potential applications include using quantum computing to improve the process of fertilizer production, applying quantum simulation to advance research into water filtration, and using quantum technologies to monitor environmental impact and decarbonize air.

Let’s be clear: we are a long way from finding a quantum solution to the world’s biggest sustainability challenges. However, we also believe it’s crucial to start planning for a quantum advantage now and to think about funding mechanisms for explorations.

There is already significant industry and government interest in research and development. The Capgemini Research Institute reveals that almost a quarter (23%) of organizations plan to leverage quantum technologies within the next five years. And Horizon Europe, the EU research and innovation program, is funding quantum research in air foil aerodynamics, battery and fuel cell design, and space mission optimization, that could have a longer term impact on sustainability.  At Capgemini, our dedicated lab, a team of global quantum experts, is also exploring the potential impact of this emerging technology.

These kinds of initiatives are crucial. Indiscreet use of technology has sometimes been a hindrance to sustainability, especially when it comes to carbon emissions. We can’t allow this to continue. We all have a role to play, and we should explore every potential avenue for change, including the promise of quantum.

So, rather than a “call to action,” consider this as a “plea for consideration.” Join us as we help you find out more about the fast-evolving area of quantum technology and its potential impact on delivering sustainable development.

To find out more read our point of view on Sustainable development and quantum technologies.

In this post, we would like to focus on black fleets, which account for a significant proportion of nearly two-thirds of fleet users that enjoy a high degree of freedom in their choice of vehicle. A decisive persona in this context is the so-called User Chooser. A User Chooser is an employee with a car allowance that grants the choice of different brands and models within the limits of a company’s specific car policy.

The User Chooser: Valuable but unknown

Within the framework of the respective company’s car policy, this customer has permission to select and configure the car to be ordered – often within a leasing model with two-to-four-year terms. Accordingly, User Choosers represent a very attractive customer segment. This is primarily because of the short leasing cycles, elevated purchasing power, and a high degree of freedom in choosing and configuring the desired vehicle. The high level of freedom challenges car manufacturers, but also offers great potential to reach and bind these customers.

Looking ahead, the analysis and inclusion of User Choosers into an OEM’s CRM strategy can have a significant impact on the achievement of corporate sales revenue goals. Therefore, User Choosers must be considered during the entire journey. OEMs should try as hard as possible to detect their needs and wishes to use the potential and buying power of this segment.

While OEMs have made great progress in achieving the often-claimed 360-degree view on private customers, the User Chooser remains relatively unknown and is therefore poorly taken into consideration for CRM activities. To generate the needed information on User Choosers and integrate it into CRM activities in a targeted manner, we at Capgemini Invent postulate the following hypothesis: It is essential for OEMs In the future to define and execute B2B-specific CRM that targets the individual user behind the buying center. This is done by collecting users’ personal data and connecting it to the surrounding organization.

In this blog post, we will first outline how OEMs can unlock the B2B User Chooser potential and thereby enable concrete CRM use cases to exploit the potential.

Unlock the B2B potential – Identification, Elaboration, and Execution

1. Data generation – Define the needed information sources

The biggest challenge is the User Chooser identification. To overcome this, personal and professional information must be combined:

• Self-shared information by the User Chooser via “My Car” apps, such as myAudi, Mercedes me or MyBMW
• Web-based information given during offer requests and vehicle configurations
• Smart fleet data through connected fleet management systems, as outlined in our blog post, CRM for fleet customers
• Collected information through physical touchpoints, such as workshop visits and showroom events

Generate User Chooser insights through the “My Car” application and web touchpoints

Audi, BMW, Mercedes, and other car manufacturers aim to have more than 80% of their customers using “My Car” applications, such as Mercedes me or My BMW. By linking the vehicle to a customer profile, the customer shares information with the manufacturer – only with corresponding consent of course. As soon as User Choosers connect a vehicle that belongs to a company with their profile, the unknown users behind a leasing contract with a certain company become known customers. As a result, they become a customer that can be targeted with CRM activities. Making customers link their profile to a vehicle is for many reasons a key challenge for today’s OEM sales and marketing departments. It becomes even more important when looking at it from the perspective of making unknow B2B decision makers known.

Therefore, using “My Car” applications should be further incentivized and enhanced by corporate contexts to extend the set of information beyond private matters.

Web interactions are another source of information. These can also be used to enrich the data generated by the “My Car” applications. OEM homepages offer useful points of contact. Information can be generated and integrated into the CRM – system during vehicle configuration or when requesting a personal offer. Other touchpoints include channels such as social media and communities.

The objective is to generate a holistic picture and to collect personal information (e.g., age, address, and hobbies across all sources).

Integrate Smart Fleet Data

Integrating smart fleet data, as outlined in our first B2B blog post, can enhance the needed information landscape. The respective data can provide insight into the financial situation as well as the potential vehicle preferences of the User Chooser. 

In the first place, smart fleet data capturing the leasing contract provides insight regarding financial framework conditions. For example, the selected vehicle segment can already provide indications of the User Chooser’s current job position and seniority level.

In addition, the leasing contract and the restrictions on the car policy imposed by the company provide guidelines for OEMs when preparing offers for User Choosers. Specifically, this involves a degree of freedom in segment selection, OEM of choice, and the specified budget. The result is a concrete picture of the potential vehicles and services that can be offered to the customer.

Lastly, the fleet composition provides further guidelines to target User Choosers more precisely. The cross-section of the fleet composition, combined with job information, provides evidence on the single customer and draws conclusions on the User Chooser.

Request and integrate information during physical touchpoints

Physical touchpoints with User Choosers also offer great potential to close the information gaps. This can range from the initial handover at a dealership to workshop visits and driving events. The added value is gained through the insight into the geographic circumstances and the preferred dealer and potentially allocates to the current company the User Chooser is working for.

For instance, dealer and workshop visits can be used to narrow down the location and the User Chooser’s radius of movement. In this way, the personal profile of the customer can be enriched, but also point-of-interest can be considered for further cross- and upsell. To address customers in a targeted and customer-centric manner, it is essential to know their preferences. This already starts with my preferred dealer. In the future, configured vehicles within retention offers can be suggested for a test drive at the dealer of choice. For this reason, the physical touchpoints must be integrated into the CRM ecosystem established and used by an OEM.

2. Identification and elaboration – data connect, predictive analytics, and customized next-best offerings

Besides the generation of data, the effective combination of the collected information as well as the derivation of insights and next-best offers represents a major challenge. However, it is necessary in order to make the unknown known and unlock the potential of the User-Chooser-specific CRM. To overcome the challenge, we recommend the following steps.  

First, the data generated across all the above-mentioned data touchpoints must be stored centrally and connected. In the process, personal information, vehicle information, and contractual information are merged in order to generate an ideally complete User-Chooser profile.

Second, by evaluating the generated profiles and matching them with potential vehicles and services, targeted next-best offers can be integrated into CRM nurturing activities. It, therefore, requires the adaption of software able to perform predictive analytics functionalities. This is necessary to handle the amount of data and to derive specific use cases, ideally integrated into one CRM software.

3. Execution – create differentiating experiences for the User Chooser

Once the personal and corporate information of the User Chooser has been collected and evaluated, the next step is to transform the findings into the right approaches. In the following section, the focus is on two specific use cases – firstly, with the aim of triggering customer retention to increase customer loyalty; and secondly, to leverage cross-selling during the leasing period.

Customized next-best retention offer

The optimized next-best retention offer captures customer, vehicle, and company information to provide an attractive repurchase offer and thus extend the leasing cycle. In this way, OEMs can tie customers to their own brand and increase loyalty. The retention offer makes use of the previously defined steps on data collection and predictive modeling that calculates the right time to present an offer for the next vehicle. This is tailored to the customers’ needs and fleet-relevant specifications. Certain aspects, such as the car policy and the degree of freedom in the selection options, determine the framework conditions. This is especially true for User Choosers, for whom the experience can be enhanced when providing offers for pre-configured vehicles. The pre-configuration already includes certain configuration options and connected service packages that can be traced back to the User Chooser.

When such personal information as age, address, marital status, and income of the User Chooser is available, it can be complemented by additional data generated through digital interactions. For example, social media activity can be used to determine whether or not the customer is interested in outdoor activities like skiing. Such insights can be validated by the range of movement of the respective User Chooser. In this context, booked workshop dates, hotel stays, and other physical touchpoints also provide valuable insights. The case becomes even more precise when the vehicle is not only used by the User Chooser, but also by other family members. These interactions (e.g., through additional phone-to-vehicle connections) provide further clues when designing the optimized retention offer.

Now, smart fleet data can be used to determine the vehicle preferences of customers with similar job titles, income, and seniority levels. With this information, a concrete picture emerges. If this information is complemented with the My Car ID log-in, the vehicle can be assigned even more clearly. For instance, let’s assume the User Chooser has borrowed an SUV from a colleague to go on a weekend trip with the family. In the last step, the scope of action is restricted by the company car policy. The result could be, for example, an SUV offer that fits the user’s hobbies and family situation. Finally, our unknown customer becomes a known User Chooser. In the next step, the local preferences can again be included to offer a possible test drive at the preferred dealer to complete the journey.

This offer does not only refer to the subsequent period after the leasing cycle. In this way, certain vehicles from similar car segments can already be offered during the cycle. For example, an offer can be made during a customer’s workshop visit, as a replacement vehicle, to test possible interest and matching for the subsequent offer.

The advantage here is the optimization of cumbersome vehicle configurations, considering the trend is towards vehicles configured in just a few clicks.  

Need-based cross-selling offerings

Further benefits of the integration of personal and professional data are achieved through the creation of need-based cross-selling offers within the leasing cycle. This potential can be exploited by targeted nurturing with customized offers.

Based on the generated fleet insights as well as personal preferences, customer needs can be identified in a targeted manner. The development of product usage is moving away from traditional ownership and toward need-based use. In this context, intelligent products, such as Functions-on-Demand and subscription models, are moving into the foreground. This offers great potential for OEMs and for their corporate customers. At the same time, some adjustments to the CRM alignment are necessary.

Some conclusions can be drawn when a customer has the technical prerequisites for autonomous driving of L2. The hardware can potentially be activated by targeted nurturing if the offer is attractive enough and the customer’s interest can be tracked precisely. In the process, the profile of the User Chooser is reused and complemented based on smart fleet data in order to derive predictive offerings. For example, if the autonomous driving feature is used by colleagues with similar characteristics, such as job title, department, and willingness to travel, this might be an indication to consider an offer. If the respective vehicle of the User Chooser is frequently used for long-distance trips, the predictive assumptions can be validated. Based on this information, a possible interest in autonomous driving can be derived. In the next step, a special offer is to be made at attractive conditions. The offer can be directly placed via in-car notifications with a potential over-the-air activation to guarantee the most convenient experience. Further goodies, such as business packages to work in the vehicle or a massage functionality, can complement the offering. Thus, we can optimally exploit the cross-selling potential and promote customer loyalty.

By combining intelligent next-best offers and proactive recommendations, User-Chooser-specific CRM can be taken to the next level. To start in time, the following basic building blocks should be considered, all of which are discussed in the following chapter.

Laying the foundation early is key to success:

To build groundbreaking User-Chooser use cases, we recommend that you start putting these basic building blocks in place today:

CRM SERVICE HUB: Establish a CRM Service Hub, as outlined in the first blog series, to combine and process data centrally and be able to apply smart analytics to personalize communications and offerings.

B2B AND B2C DATA INTEGRATION: Integrate the private and corporate data of User Choosers, specifically their profiles and overall online behavior.

PREDICTIVE ANALYTICS:  Apply predictive analytics to target User Choosers with the right offerings at the right time based on predictive retention models in order to leverage cross- and upsell potentials.

CONSUMERIZED B2B JOURNEYS: Personalized journeys, ranging from community-building activities to offer structure.

What is your view on the relevance? Which priorities and additional use cases do you see? We look forward to hearing your thoughts!

This blog has been co-authored by Thomas Ulbrich, Christopher Rose, Alexander Stotz, and Kamil Kilic. Please get in touch if you have questions or need further information. We look forward to exchanging ideas on this particularly current topic.

Tech-enabled learning enables 24/7 learning

A chief advantage of tech-enabled learning is that it’s always on tap: people can add to and consolidate their skills in the flow of their everyday work, but also at any other time that suits them. Learning experience platforms (LXPs) enable them to personalize and add to their knowledge in small increments: not everything needs to be learned in long lessons or in broad courses.

Sometimes, people simply want to find out how to fill one particular knowledge gap. For instance, “I get this issue all the time. If the customer purchase order was raised in Country A, but it was in the currency of Country B, and we provided goods and services to Country C, is VAT applicable, and at what rate?”

In fact, LXPs enable people to explore by category or content, just as they might search at home by title or by genre on a streaming platform when they’re looking for something to watch in the evenings. “I have two or three half-hour slots this week, and I’m looking to advance my understanding of XYZ. What have you got for me, LXP?”

Just as a streaming entertainment platform can track previous preferences and make recommendations, “integrated talent management” technology can use search histories, people’s job profiles, and career progressions or interests to suggest learning paths, competency-based learning, and job-aligned learning. The technology’s in-built intelligence means individual competencies can be curated at scale, so individual employees can improve their skills in a way that delivers value not just to them but to the business.

Leveraging design to drive people-centric learning

Learning Design, which needs to take precedence in today’s times, is an upgraded version of Instructional Design. This structural foundation supports learning experiences while considering the complex interactions between the instructor, learner, and the platform.

Tech-enabled learning systems consider the qualities of a learner – their prior knowledge, existing skillsets, and a multitude of other factors – and modify the course or learning processes that can adapt to the unique needs, circumstances, and goals of the individual. This, is in turn, helps the learner guide the direction or outcome of their training through data-driven platforms.

What’s more, learning can be embedded into the platforms on which employees operate, so the systems can train them to be better on the job, adapting intelligently to their rate of progress and also to their preferred learning approaches. You no longer need to “sit next to Nellie” when Nellie just went digital. Approaches like this are transforming the face of learning – especially when employees need to keep pace with technologies that are evolving and/or being superseded all the time.

Intelligent, people-centric learning driven by innovation

I mentioned that intelligent learning systems can adapt to the needs of individual people. It’s a trend that’s crossed over from sales and marketing. Just as businesses are increasingly developing customer personas and customizing their products and services to meet the expectations of those groups, so organizations are defining learning personas for their employees, putting learners at the center, and tailoring what and how they need to acquire knowledge to their own circumstances and preferences.

Digital interactive practice sessions, podcasts, multiple-choice tests, written submissions, videos, virtual reality, and extended reality role-playing are just a few of the possibilities. Whatever the case, the aim is to make learning impactful relevant and accessible for everyone.

As with so much else in the digital age, the content itself is key – but the format can make or break its prospects of success. So, too, can the ecosystem that underpins it all: organizations need to build and sustain an infrastructure that enables them to keep pace not just with emerging technologies but also with changing business practices and people’s evolving needs.

To use a favorite line of ours here at Capgemini, a smart and flexible learning infrastructure can help get the future your business wants – and that your employees want, too.

To learn how Capgemini’s Intelligent Learning Operations can drive a personalized, frictionless, and continuous learning journey across your talent management cycle, contact: sarita.fernandes@capgemini.com

Competing in the recent BMW Group Quantum Computing Challenge gave us the chance to delve into this conundrum – and start to light the way for manufacturers who need to know when quantum technology will overtake classical approaches in terms of performance and cost.

Our conceptual work on this evaluative road-mapping framework came during the second round of the BMW Group’s global crowd-sourced innovation initiative. I’m a quantum physicist here at CC and joined forces with colleagues from the wider Capgemini Group as part of Capgemini’s Quantum Lab to tackle the organisers’ use case challenge on Machine Learning (ML) for Automated Quality Assessment. You can read more about the main thrust of our activities, which focused on a holistic approach to developing a quantum ML algorithm.

After successfully qualifying as a finalist – a heartening achievement given the fact that the challenge attracted around 70 submissions from around the world – our team decided to invest time in pushing the boundaries of our thinking. Specifically, we explored ways to assess the ML model’s viability and scalability, with a view to forging a roadmap for quantum technology adoption by the BMW Group.

We compared various algorithms to establish the size of quantum platform that would be needed to outperform a classical solution. The team also drew on its broad expertise in AI, ML and quantum engineering to assess the feasibility of transferring classical ML approaches and tools to quantum platforms.

Transferability between classical and quantum platforms

I’ll dig a little deeper into the detail in a moment, but essentially our key finding was that the approaches and tools were indeed transferable between quantum and classical platforms. The next step was to develop a framework for testing and comparing the two, to establish the size of platform needed for one to outperform the other. The idea is that by mapping this with the development plans of companies, it would become possible to predict the time when quantum displaces classical.

There wasn’t an opportunity during the time limitations of the challenge to develop the concept in more detail, but I certainly believe that it represents a routeway to road-mapping for any manufacturing company contemplating the adoption of quantum technology.

For the more technically minded, here’s a closer look at the metric we used. Capacity measures the ability of an ML model to approximate functions. For instance, fitting a straight line to a set of points is not able to model non-linear relationships between variables. A higher order polynomial, such as a quadratic function, can capture a greater range of functions, and therefore has higher capacity. Models with high capacity are better at learning complex features within a data set, allowing them to better differentiate between categories such as fractured versus normal car parts on a production line. However there are drawbacks as we outline below.

It was by benchmarking classical and quantum algorithms, using the same metrics, that enabled our approach to indicate that the quantum version would outperform its classical counterpart when run on systems of an equivalent size. Quantum computers are still small, but engineering breakthroughs are rapidly pushing the boundaries of what’s possible. It’s reasonable to predict that it’s only a matter of time before quantum algorithms outperform today’s classical computers.

Our submission to the challenge only considered one aspect of the proposed algorithm. A complete analysis would assess the trade-offs between a variety of factors such as cost, expected quantum computing development timelines and other ML measures. It is by identifying trends in algorithm performance as a function of computing power and cost that would enable companies to identify when they should plan to incorporate a given quantum algorithm into their processes.

It was a real thrill to take the lead on these ideas, which sprang from the BMW Group’s request for team Capgemini to consider scaling and algorithm comparison from a theoretical perspective during the second phase of the challenge. And it was great to bring in my CC colleagues Joseph Tedds and Jacob Swain to add their insights and expertise to the submission. As I mentioned in my previous blog on the BMW Group challenge, there’s plenty more content to catch up on from Capgemini’s perspective. READ HERE  Meanwhile, please email me if you have any questions or observations on the topics I’ve been exploring.

Author – Edmund Owen, with contributions from the Quantum team including Julian van Velzen, Christian Metzl, Barry Reese, Joseph Tedds and Jacob Swain 

Demystifying three common myths around sustainable IT

But before embarking on their sustainable IT transformation journey, IT leaders must be careful to avoid common myths surrounding this topic. One common myth is measuring the CO_2e emissions of IT only during its use or run phase, while manufacturing also has a significant carbon footprint. Since user devices, networking, and data center equipment constitute a large share of enterprise IT’s carbon footprint, any sustainable IT transformation strategy will need to consider the impact of manufacturing too. And to close the loop it is important to have an end-of-life strategy for all IT devices and equipment. Global IT leaders must acknowledge that there could be a wide variance in CO_2e emissions depending on many factors like location etc and you cannot fit one sustainability strategy to all.

A second common myth is to treat cloud as a panacea for all of IT’s CO_2e emissions. Although moving applications to the cloud has a potential to reduce IT’s carbon footprint, it also has a scope 3 impact based on the hyperscaler’s operational carbon footprint like electricity. Electricity generation needs energy that varies across countries and impacts its CO_2e emissions commonly referred to as carbon intensity[1]. Hence, a simple move to cloud when the data center is in a country with higher carbon intensity might increase the CO_2e emissions. There is no doubt that hyperscalers have been trying to optimize their carbon footprint and reporting, but today we still lack a clear view of carbon footprint on public cloud and especially the manufacturing CO_2e emissions of servers etc. As Gartner® says, “Sustainability metrics and workload placement tools are still immature and not always transparent, making it difficult for organizations to fully and accurately assess true sustainability impacts of their cloud usage today”[2].

Also while discussing cloud in the context of sustainability, I would advise more caution as it can have an infinite effect. I have seen many clients add much more into their cloud than they need, which in turn can increase the total CO_2e emissions even though the emissions per workload on cloud may be lower. Hence, IT leaders should always have sobriety as a guiding principle to shape their sustainable IT strategy and transformation roadmap.

A third myth I come across often is limiting ecodesign to only code optimization. In reality it also includes user devices, infrastructure, network on which the software is running and foremost it is about efficiently addressing the business needs.

Sustainability is more than climate change

I certainly believe that we need to do all we can to reduce CO_2e emissions to the minimum before looking at offsetting, and also ensure compliance with regulations on sustainability reporting. But let’s also be aware that sustainability is a broader topic covering ecological degradation and depletion of abiotic resources like precious metals. It is important for any long-term sustainable IT vision to also consider its impact on other areas of sustainability beyond CO_2e emissions.

We see a strong demand for sustainable IT!

We see a clear urgency in the market to act on this. Most clients want us to help them build awareness on this topic internally, baseline their IT carbon footprint, and set-up governance to track their CO_2e emissions.  Industries where IT is a major contributor to their CO_2e emissions are ahead in the curve compared to others, but even here sustainable IT is sometimes seen as a quick win while they transform their core business model. While cost was and continues to be a priority for CIOs, going forward we might see sustainability metrics like units of electricity also being used to measure the sustainability ROI from IT transformations. Would you agree? I would love to hear how you demystify sustainability myths for your teams and build a vision for your sustainable IT transformation.

For more than 11years I am leading Capgemini’s unique eAPM capability around the key principle of helping our clients make fact-based decisions on their IT transformation journey. Most recently, we launched its sustainable IT module leveraging our proprietary eAPM studio powered by an AI engine. Using this we can model a 360° view of enterprise IT’s carbon footprint at an application level and identify key emission hotspots. With our unique benchmarks we are then able to recommend actionable levers that can accelerate CO_2e emissions reduction. We are on a continuous innovation journey in this space along with our clients who are keen to accelerate their sustainable IT transformation journey. Would you like to embark on this journey with us? I would like to learn more about your sustainable IT plans. Please reach me at philippe.roques@capgemini.com or connect with me on LinkedIn.

Visit our website to learn more about how we help our clients’ sustainable IT journey with eAPM. I would like to thank my colleagues Claire Egu & Joy Bhattacharjee for their valuable contributions to this article.

_{[1] https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement
[2] https://www.unep.org/resources/emissions-gap-report-2021
[3] https://ourworldindata.org/grapher/carbon-intensity-electricity
® Gartner Press Release, Gartner Predicts Hyperscalers’ Carbon Emissions Will Drive Cloud Purchase Decisions by 2025, January 24, 2022}

Responsible for 37% of CO2 emissions, the automotive sector is at the center of the global decarbonization agenda. That’s why, according to the Capgemini Research Institute, 65% of organizations have a comprehensive sustainability strategy, with electrification as the preferred approach. However, this solution increasingly exposes automakers to sourcing risks due to intensified battery production demands.

What’s at stake for the automotive industry? 

To meet societal and environmental needs, anticipate legislation, and reduce exposure to resource scarcity and sourcing difficulties, manufacturers need to scale up their efforts. Increasingly they’re arriving at the same solution: introducing circularity into their models. OEMs’ objective should be to embrace the circular economy to minimize their raw materials footprint, keeping vehicles and components at their highest value for as long as possible (repair, maintain, reuse) and reintroducing them into new product lifecycles when they reach the end of use (repurpose, remanufacture, recycle). Implementing these new business models with a holistic approach will help companies to address four main challenges.

1. Build business resiliency with circular products that deliver end-to-end value

Automotive players’ obligation to “greenify” their activities through electrification has made their procurement activities increasingly complex. Mining, material processing, and battery production are conducted by very few countries (notably China), with production and supply capacities that fall well short of today’s demand. In addition, automotive is not the only sector that needs such raw materials as lithium, cobalt, copper, or nickel – a fact that adds even more stress to sourcing.

This situation has resulted in significant price increases: between the start of 2021 and May 2022, lithium prices rose by a factor of more than seven and cobalt prices more than doubled. Limiting disruption in global supply chains implies deploying circular economy concepts to reuse and recycle as high a proportion of these rare elements as possible in the production loop. Circularity can help absorb a significant proportion of metals sourcing costs (by 15% for copper and 102% for nickel in 2021, by 50% for both lithium and cobalt and 80% for copper by 2030). Automotive companies need to work toward reusing resources and components through repurposing or recycling and find ways to extend EV lifespan through recovery operations (repair, refurbish, retrofit, remanufactured). Only then will circularity take its position at the heart of the business strategy for long-term resilience.

The cornerstone of a circular economy strategy is sustainable product design. The goal is to design vehicles that are fit for purpose through sufficiency, digitalization, durability, modularity, recoverability, or recyclability. Another key piece of the puzzle will be biotechnology – the next frontier for automotive players in material selection and waste management processes, bringing novel enzymes, bio-based materials, and new, tunable products.

Biotechnology can help tackle issues at the two extremes of the lifecycle, overcoming resource scarcity by inventing alternative materials, and dealing with pollution (from plastics in particular) through solutions such as biodegradable materials and chemical recycling. This model change will introduce the shift from a volume business to one focused on value, with technology helping to support this transition to sustainability. 

For instance, BMW is making a €30bn investment in R&D by 2025 to extend its leadership in resource efficiency from production to the entire vehicle lifecycle, thanks to its I-Vision concept car. I-Vision is a design based on circular economy principles, with the goal of recovering 100% of materials.

For another example, the Hydrovolt project, Europe’s largest electric vehicle battery recycling plant, is the result of a partnership between Northvolt and Hydro. The plant will have the capacity to process 12,000 tons of batteries a year via a fully automated process recovering up to 95% of battery materials (2030 target: 50% recycled materials used in battery production).

2. Achieve long-term economic value through services that help optimize and preserve the value

To meet long-term sustainability targets, $50bn of investment over the next five years is required, in addition to the current investment in EVs, autonomous vehicles, and digital mobility services. Besides the need to comply with legal and regulatory requirements, expectations about the profitability of sustainable business models are high.

Achieving circularity implies becoming “asset managers” rather than traditional vehicle sellers, to make the most of each product put on the market. Consequently, automakers are urged to transform their ecosystems and massively develop the after-use segments, so that they keep fleets/vehicles/components at the highest value for the longest period (focusing on long-term usage at the expense of selling large volumes of goods). Better consumer desirability and increased lifespan of products result in a more profitable Total Cost of Ownership (TCO) per item.

Recent announcements from Renault and Stellantis reveal that both companies have created dedicated subsidiaries or business units to deliver their circular economy ambition. Through a holistic approach that permits the implementation of revalorization services (retrofitting, repurposing, remanufacturing, recycling…), each company estimates that it can generate around €2bn in revenues by 2030 thanks to the circular economy.

Mobility services have long been in the spotlight because of their ability to increase revenue per passenger seat and optimize overall vehicle utilization (e.g., Mobility-as-a-Service, Car-as-a-Service). Similar initiatives can be applied at the component and battery levels to address the electrification challenge. For instance, the Chinese automotive company NIO meets EVs’ need for electricity not by selling batteries but instead by leasing them and replacing them when empty (Battery-as-a-Service). In other words, instead of the customer waiting for the battery to charge, a full battery is swapped in and NIO charges the empty one.

3. Extend traditional operating models through broader and more local partnership ecosystems

The recovery mechanisms of the circular economy will imply major changes to the supply chain and operations. The challenge is to develop manufacturing chains that can integrate used parts/products and waste in the same way as they need to integrate them into the production process itself, but also complementary capabilities that facilitate vehicle disassembly and recovery. That is the intent of Renault’s Re Factory, the first factory in Europe dedicated to circular economy and mobility. This project engages a broad ecosystem to offer four main activities (retrofit, re-energy, re-cycle, re-start). It aims to retrofit more than 45,000 vehicles each year by 2023, reduce turnaround time for second-hand vehicles from entry into stock to resale from 21 to 8 days, and repair 20,000 electrical batteries per year by 2030. 

Decentralization is key, with local collection points, repair centers, and recycling facilities to decrease transport and supply costs, accelerate operations, and avoid dependencies. It also requires new relationships with partners or competitors – for example, to standardize parts, share assets, or merge flows, optimizing operations and reducing costs. The company is part of a new ecosystem, favouring local or regional partners such as Renault for the Mobilize Share program, which relies on more than 400 garages in France to provide maintenance or repair services.

Furthermore, given the complexity of such operations, product design needs to integrate modularity features to facilitate durability, reusability, repairability, and recyclability to preserve value over multiple lives (product design can drive up to 80% of a product’s environmental impact over its lifecycle). Standardization is a key driver to facilitate the recovery of parts and process automation and to integrate operations across the industry for each vehicle and component category (with partners or even direct competitors).

Finally, anticipating such profound changes means investing significantly in new capabilities and competencies. Attracting and training the right talent will be crucial: not just developers, tech experts, and data scientists, but also software engineers, mechanical engineers, electrochemists, and battery experts. Because the circular economy requires an understanding that goes beyond the traditional borders of our industry, investment will be needed in additional areas: notably in telecoms, mobility, and energy expertise.

And of course, capabilities within sustainability also require an understanding of the interconnectedness between all the components. For example, General Motors is investing $71m in a new campus to support emerging business opportunities, attract world-class talent, and achieve the company’s sustainability goals.

4. Embrace data management and traceability to empower corporate governance

The introduction of new digital and software applications into manufacturing plants and vehicles has unleashed the incredible potential for data collection and processing at every stage of the lifecycle. Companies that break down product lifecycle siloes, merging insights from PLM and LCA tools, will be the ones that achieve end-to-end traceability despite the large number of stakeholders involved in the automotive value chain.

Data can be collected and used at different levels across operations to scale up collection processes for closed-loop supply chains, improve performance tracking, enhance customer services, predict maintenance, or even help forecast future consumer demand. Real-time data collected during usage is also invaluable for anticipating maintenance needs (predictive maintenance) and product obsolescence. Connectivity and data will enable “servitization” – the switch from products to products + services – to achieve its promise to deliver superior value, for longer. 

Some players, such as Stellantis, are already seizing this opportunity by developing referencing, traceability, and accessibility activities so that returned spare parts can be used to renovate vehicles. As a result, it is possible to save up to 80% on new raw materials, and reduce energy consumption by 50% in the production of refurbished engines. Meanwhile, Volvo is implementing global traceability of cobalt used in its batteries by applying Circulor’s blockchain technology.

The shift to a circular model, including the management of new ecosystems, is complex, and therefore requires strong data governance. With new practices – including transparent access to necessary data by all the members of the value chain – it becomes possible to break down internal siloes across BUs as well as those involving external partners. Monitoring the suitable KPIs at the right level not only supports reporting to investors and authorities but also facilitates decision-making. That’s because this type of monitoring makes it possible to measure short-term transformation progress with circular initiatives against the long-term vision. This is how we will accelerate the transition to the sustainability era.

To explore the width & breadth of our work in the automotive sector visit us.

Bolder, more transparent pledges

Warnings resound as COP27 continues: urgent climate action is needed now, and it’s vital corporate pledges are ambitious enough. We have all seen that corporate net zero pledges have increased rapidly in recent years. However, a challenge persists. Some pledges aren’t comparable because of inconsistent definitions of what “net zero” actually means. For example, the scope of emissions included in targets differs between pledges, as does how deeply and quickly a business must decarbonize before offsetting its residual emissions.

Reaching net zero should not be seen as a ‘competitive advantage’, rather it should be a shared challenge. In reality, no one company can reach net zero in isolation. We will need cross-sector action – for example: changes in mobility infrastructure and a shift to a circular economy. Achieving net zero therefore requires a unified vision of what we’re trying to achieve. Critically, it requires a vision that’s truly ambitious enough to support the Paris Agreement goals to limit global warming.

The Science Based Target initiative (SBTi), a global standard setting organization, set out to create clarity. Throughout 2020 and 2021, Capgemini and many other businesses, academics and non-governmental organizations fed into SBTi’s development of a new standard that establishes clear guidance on when a company can claim to be net zero – and on what is required to get there.

The result was the world’s first framework for setting corporate targets aligned with what scientists say is required to limit global warming to 1.5°C. The Net-Zero Standard is endorsed by NGOs, academics and others, including by CDP, UN Global Compact, the World Resources Institute and WWF.

What the Standard requires

The framework’s requirements are highly ambitious. They require significant collaboration across companies’ entire value chains to achieve the radical transformation needed. Here’s a summary of the key features of the new Standard:

1. 90-95% decarbonization. Organizations are required to reduce carbon emissions by at least 90% across their own operations and value chain. This includes emissions produced by their own processes (scope 1), purchased electricity and heat (scope 2), and emissions generated by suppliers and end-users (scope 3).

2. Near- and long-term targets. The Standard sets two new ways of describing science-based targets: near- and long-term. Near-term targets help spur action for significantly reducing emissions in the short-term (5-15 years), while long-term targets convey the overall destination of a businesses’ decarbonization journey and must be achieved no later than 2050.

3. Carbon removals. These can only to be used when a company has met its long-term reduction target to mitigate for the final 5-10% of emissions – and no claims of net zero can be made until this point.

In addition, SBTi recommends that companies make investments outside their science-based targets to help mitigate climate change elsewhere and increase the likelihood that the global community stays within a 1.5˚C carbon budget. These cannot be a substitute for the rapid and deep reduction of a company’s own value chain emissions.

Doing what science demands

As a company that has been committed to the climate-safe Paris Pathway for many years, we reviewed our net zero targets once the Standard was published, to ensure that we remained aligned with the latest scientific understanding.

We elevated our targets in two main ways. First, we significantly increased the ambition of our original net zero commitment: we increased our target to a 90% reduction in carbon emissions across scopes 1, 2 and 3 by 2040 (compared to a 2019 baseline), before we’ll neutralize the final 10% of residual emissions through high-quality carbon removal solutions to bring us to net zero. This updated target commits us to eliminate most of our carbon footprint.

Second, we quickened the pace of our decarbonization by raising the ambition of some of our near-term 2030 goals. In addition, by revising the baselines of our near-term goals from 2015 to 2019, this has increased our ambition further. SBTi validated our targets as aligned with its Standard in July 2022.

Recognizing the urgency to achieve global net zero emissions, we are also taking action beyond the decarbonization of our own operations to keep global average temperature increases below 1.5 °C by investing in carbon avoidance and removal solutions (including high quality carbon credits). Our investment will be aligned to our own footprint, neutralizing our operational carbon emissions by 2025 and our supply chain emissions by 2030.

Turning commitment to actions

Achieving a 90% carbon reduction means radical changes across our business, requiring us to look beyond efficiencies and implement a complete shift in how we operate. Capgemini was one of the first businesses in our sector to set SBTi-validated targets. Our first targets set in 2016 were reached by January 2020, ten years earlier than targeted and ahead of COVID lockdowns. Since then, we’ve rolled out our ten-point global transition plan, which guides our action.

As a technology company, we’ll keep taking a lead on the issues relevant to our sector. Our dedicated sustainable IT steering group continues work to improve the efficiency of our IT devices, enable lower carbon delivery, and minimize electronic waste.

Our global real estate team is reducing the environmental impact of our offices and data centers. For example, we recently launched our Energy Command Center (ECC). The ECC uses digitalization alongside data collection and prediction to manage energy performance across our campuses in India. It’s already reduced energy consumption by an estimated 20%, and offers a model for improved energy management for use more widely across our offices. We also remain committed to switching to 100% renewable electricity globally by 2025.

As a company present in over 50 countries, travel consistently comprises a large share of Capgemini’s carbon footprint. At the heart of our approach to reduce travel impacts is ensuring that the first question we ask is “do we need to travel?” and, if so, how can we make smarter travel choices? We are also reducing emissions from our employee commuting and business travel. This includes transitioning our global 12,000-car company fleet to electric vehicles by 2030.

Sustainable businesses need sustainable supply chains. We’re working with our suppliers to make their activities more sustainable. We’ve proactively engaged with our top 100 suppliers globally (accounting for around 50% of all our supply chain emissions) and have started developing approaches to systematically embed sustainability into vendor selection.

Finally, we recognize that the biggest impact we can have is through working with clients to manage their sustainability challenges and we continue to work to ensure we help clients in their low carbon transition.

COP27 and beyond

As the world meets for the 27^th UN climate conference, COP27, calls for strengthened pledges and faster action remain top of mind. Examples include the Alliance of CEO Climate Leaders call for the private sector to strengthen net zero approaches by setting meaningful science-based targets, and the UN’s High-Level Expert Group’s recent recommendations for non-state entities’ net zero targets. I welcome this focus – as it is only by setting net zero targets that prioritize deep decarbonization at pace, that we will be able to realize anything like the Paris Agreement.

Everything moves faster

Organizations need to be more agile and reduce decision-making cycle times. To deliver, businesses are enabling workers to make more decisions whenever they are needed. But this requires quick and frictionless access to the right data at the right time. And they also need to trust that the data is current, relevant, and available. Easy, right? No, it’s not. Moving a business forward requires fast access to good data for more applications. But that turns IT and analytics teams into the bottleneck. Let’s face it, the inquiry model – ask for insights, wait weeks for answers – does not support today’s pace of business. That was fine when decisions were made by a few executives at the top of the organization. Today, it’s unacceptable.

Leading companies, however, empower the middle of the organization to speed time to market, push digital acceleration, and maintain a competitive advantage. The traditional inquiry model simply does not provide answers fast enough. If you’re too slow, you lose time, money, customers, market, and maybe your job.

Enter self-service BI. It drives faster decisions, more innovation, lower costs, transformation at scale, and improved quality, safety, and efficiency. But how to get there, and become a self-service data master?

Enabling self-service BI

A more participative self-service BI environment needs to be encouraged. Unfortunately, for many organizations, this change is slowed by internal issues, fiefdoms, and siloed data and systems. But for smart organizations that have sorted out data access and sharing requirements, self-service BI drives data literacy. The goal should be to build a culture where people seek to understand data and its context. Putting data at every business users’ fingertips is the essence of self-service BI. Like the concept of a data mesh, self-service holds that people closest to the data – the data producers – should make the data available. Those who need the data – data consumers – can then access it whenever.

For self-service BI to succeed, however, the entire data value chain may need to be fixed. Data has to be easy to find, understand, access, and use for everyone in the chain: data engineers, analysts, data scientists, and business users. Productivity along the chain can be enhanced with a data catalog, which is a repository of metadata on information sources from across the enterprise, including data sets, business intelligence reports, visualizations, and conversations. It makes the data more accessible and understandable to everyone, especially less-skilled data consumers. It also prevents requests for insights that already exist or questions that have already been asked.

One insurance customer determined that just having a data lake didn’t by itself generate important business insights. It needed to teach people to fish in the data lake. They needed to move from a culture of analysis to a culture of reporting. A key element of doing so was implementing a data catalog. The company is in the process of using catalog to data nearly 12,000 employees so they can answer questions that drive to the best next action. Who are the customers that I should call today? And what tasks should I complete today? With claims employees, they saw increased efficiency with self-service information. And on the business analyst side, they saw 25 percent time-savings due to decreased data inquiries.

A data catalog for trust

Many technologies are needed to deliver self-service BI. First, though, users need to find the data. That data discovery – and understanding the context of the data you do discover – is critical for the typical data consumer. It’s also important to clearly understand when data is not available, and when data is old, incomplete, inaccurate, or otherwise questionable. For this reason, it is not surprising that recent research by Capgemini has found a massive trust gap between the IT-facing arm of organizations and business units. CIO David Seidl says, “as a user, a highly usable data portal or access tool including data discovery and contextualization is critical for more casual, non-power users. I think that’s the real destination of self-service BI in the long term.”

A data catalog does this all by delivering data discovery, contextualization, and user-friendly tools for casual, non-power users. Specifically, a data catalog enables any user, regardless of skill set, to find and understand data via natural language instead of SQL queries. At the same time, a data catalog provides a business glossary to convert technical jargon, obscure field names, or complex database nomenclature into easy-to understand business terms.

• Surfacing learned and collaborative data recommendations
• Flagging potentially sensitive data
• Integrating data quality scoring
• Collecting popularity rankings, user recommendations, and usage recommendations
• Flagging data health and policies to avoid misuse and compliance issues.

A data catalog enables self-service BI, seamless data collaboration, integrated communication, and the sharing of internal expertise, all built on a foundation of trust. This empowers workers to explore data and discover the answers on their own. And, when they can’t, it points them to the resources and people that can help.

This not only empowers the business but also makes them more accountable for driving data-powered decision making.

The next wave of catalog and self service

The next wave should aim to unlock all the enterprise data using AI. This will empower knowledge workers to explore data before a report or analysis exists, and then drill into data and discover the answers on their own. And when they can’t, it points them to the resources and people that can help. This step will sustain knowledge workers in an increasingly hybrid work modality. The combination lets AI analyze data and surface insights while Natural Language Processing (NLP) allows users to build on top of those surfaced insights and ask the next questions. This will give these workers immense power.

INNOVATION TAKEAWAYS

Self-service leads the way

An increasingly distributed and digitally enabled workforce needs self-service BI, rather than solely relying on central services.

Understanding data is key

Those serving themselves need not only access to data, but also to the data about the data (metadata).

The data catalog is foundational

A data catalog provides a trusted, empowering foundation for self-service BI.

AI drives the next generation

Artificial intelligence augments current data-catalog functionalities, making self-service BI even more accessible to more people.

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 4 features 18 such articles crafted by leading Capgemini and partner experts sharing inspiring examples of it – ranging from digital twins in the industrial metaverse, “humble” AI, serendipity in user experiences, all the way up to permacomputing and the battle against data waste.. In addition, several articles are in collaboration with key technology partners such as  Alation, Cognite, Toucan Toco, DataRobot, and The Open Group to reimagine what’s possible.  Find all previous Waves here.