Consider ChatGPT as a virtual assistant, dedicated to streamlining your workflow and taking your architecture to the next level.

An Augmented Architect?

Gone are the days when IT architects had to rely solely on their own expertise and intuition to make critical design decisions. Today, we live in an era where technology is constantly evolving. It is crucial for IT professionals to stay ahead of the curve, through technology and sector monitoring, networking, and engaging in the Community of Practices and ChatGPT represents another valuable opportunity for IT architects to become “Augmented Architects“.

But what exactly is ChatGPT, and how can it help IT architects do their jobs better? Put simply, ChatGPT is a state-of-the-art AI language model that can generate human-like text based on a given prompt. For IT architects, this means that they can leverage ChatGPT’s advanced capabilities to streamline their work, improve their accuracy, and ultimately enhance the outcomes of their projects.

How does ChatGPT enhance the work of IT architects? Let’s examine three examples to understand their impact.

1. Architecture design assistance

IT architects constantly face complex design problems that require swift and efficient solutions.

ChatGPT can assist in the design process by providing suggestions for architecture patterns, best practices, and trade-off analysis:

• Pattern suggestions: ChatGPT can provide suggestions for commonly used architecture patterns that are relevant to the specific problem the architect is trying to solve. This can save architects time and effort in researching and identifying suitable patterns.

• Best practice recommendations: ChatGPT can recommend best practices based on previous experience and industry standards. This can help ensure that the design is scalable, secure, and efficient.

• Trade-off analysis: ChatGPT can provide a trade-off analysis to help architects weigh the pros and cons of different design options. This can help architects make informed decisions and choose the best course of action.

Here is a concrete example of a trade-off analysis, conducted with ChatGPT:

“Imagine an IT architect is tasked with designing a new e-commerce platform for a company. What are the possible architecture scenarios, considering factors such as cost, complexity, scalability, and performance?”

After several deep dives and interactions with ChatGPT, here was its answer:

“When it comes to designing a new e-commerce platform, IT architects have to weigh several factors such as cost, complexity, scalability, and performance. Two common architecture scenarios to consider are composable commerce architecture based on microservices and monolithic architecture. Let’s explore the trade-off analysis between these two options:

Option 1: Composable Commerce Architecture based on Microservices

In this approach, the e-commerce website is built using microservices, each responsible for a specific function such as product catalog, shopping cart, payment processing, etc. This approach offers greater flexibility and scalability, as new microservices can be added or removed as needed. However, it also involves a higher level of complexity, as each microservice must be managed and maintained separately.

Option 2: Monolithic Architecture

In this approach, the e-commerce website is built as a single, integrated application. This approach is simpler to manage and maintain but may be less flexible and scalable than the composable commerce architecture.

IT architects can benefit from ChatGPT’s fast and effective support in the design process, freeing up time and resources to delve into scenarios and concentrate on strategic matters.

2. Component selection

The selection of the right components for an IT architecture can often be a time-consuming and challenging task. With ChatGPT’s ability to analyze large quantities of data and provide informed recommendations, IT architects can more easily select the most suitable components for their projects, saving time and effort in the process.

I asked ChatGPT:

“Let’s consider a scenario where an IT architect is tasked with searching for the right front-end framework for a company website. Can you recommend a set of solutions to consider, with their related advantages and disadvantages?”

ChatGPT responded with:

1. React has a relatively low learning curve, but it may require more resources compared to the other two options. Additionally, it benefits from a thriving community that offers extensive support and resources.
2. Vue.js is known for its simplicity and flexibility, making it a popular choice for building user interfaces. However, it may come with a slightly steeper learning curve compared to React.
3. Angular offers a comprehensive solution for building web applications and has many features and tools out of the box. It also has a steeper learning curve compared to React and Vue.js, but once you understand the framework, it can be a powerful tool for building complex web applications.”

Such answers from ChatGPT can serve as a starting point for architects who are unfamiliar with the topic, or as a way to accelerate the work of those who are already knowledgeable in the field.

3. Technical documentation

Technical documentation is a critical but often time-consuming aspect of an IT architect’s work.

ChatGPT can offer valuable support by streamlining the documentation process through its ability to process large amounts of information and understand technical language.

With ChatGPT’s help, architects can more easily summarize and structure technical data, allowing them to allocate more time to higher-value activities.

As an illustration: you will find a  summary, generated by ChatGPT, at the very end of this article.

Here’s another concrete example of how ChatGPT can assist in technical documentation:

Let’s say we need to document the architecture of a new cloud-based application. The architect can provide ChatGPT with the necessary technical specifications, design documents, and any other relevant information. ChatGPT can then assist in generating a high-level summary of the architecture, outlining the main components, relationships, and dependencies.

This summary can serve as a useful starting point for the architect, who can then use it to generate diagrams (ChatGPT cannot) or create a more detailed technical document. In this way, ChatGPT can help IT architects save time and effort in writing technical documentation while ensuring that the information is accurate and well-structured.

This allows architects to focus on more value-added tasks, such as identifying and resolving technical issues or exploring new technologies to drive innovation.

Limitations

While the benefits of ChatGPT for IT architects are clear, it is important to recognize that the technology is not flawless. ChatGPT’s performance is limited by the data it has been trained on. In my testing, I also encountered some errors in the context that required restarting the interaction with ChatGPT.

ChatGPT now officially warns you that your messages may be read by others (including the previously mentioned issues with context), so we need to be careful not to disclose any confidential information.

Additionally, as with any AI technology, it is crucial to critically assess the validity and relevance of the information produced by ChatGPT. There may be instances where its suggestions are partially, or even entirely, inaccurate. The way you formulate your questions can mitigate this risk. In a nutshell, ChatGPT is not a substitute for experts, but it can support them if used effectively!

What about sustainability?

The use of large language models such as ChatGPT poses a challenge in terms of sustainability, because the training and deploying of these models require a significant amount of computational resources and energy, resulting in a large carbon footprint.

Not only does the training process consume vast amounts of energy, but the deployment and ongoing use of these models in production also consume significant resources. Given the environmental impact of using large language models, it is imperative to explore and adopt more sustainable approaches for AI development and deployment.

Implemented practices, such as using renewable energy sources and optimizing the use of resources during training and deployment are necessary, and there is still much to be done in this area. It is crucial for the AI industry to focus on sustainability and reducing the carbon footprint of AI systems.

My conclusions

ChatGPT can become a valuable asset for IT architects in their daily work, offering numerous benefits.

Whether it be assisting with architecture design, component selection, or technical documentation generation, ChatGPT has the potential to transform the way IT architects work.

It can ultimately increase their productivity, efficiency, and effectiveness.

I’m convinced that technologies such as ChatGPT have the potential to serve as virtual assistants for not only architects but also many other professionals in various industries.

So why not try ChatGPT, and experience the benefits for yourself?

Summary (generated by ChatGPT)

This article, co-authored by Pascal Espinouse and ChatGPT, explores the potential of using advanced AI language technology, ChatGPT, to boost the productivity and success of IT architects. ChatGPT can act as a virtual assistant and provide support in areas such as architecture design, component selection, and technical documentation.

Throughout the article, real-life examples are presented to illustrate the impact ChatGPT can have in these areas. While the benefits are clear, it is important to acknowledge the limitations of using ChatGPT and emphasize the need for critical evaluation and decision-making by the IT architects.

Ultimately, the article concludes by stating that ChatGPT is a valuable tool for IT architects to become “Augmented Architects”, but also for many other professionals looking to work more efficiently and effectively.

With digital literacy comes a need for the data skills that will ensure the EU’s Digital Decade goal of ‘a digitally skilled population and highly skilled digital professionals’ comes to fruition. And among the EU’s objectives through to 2030 are to give small businesses and industry access to data, as well as the ability for all parties to compete in the digital work on fair terms. This suggests open data – but what is open data and why are skills in it so important?

The open data skills gap

Open data is data that anyone can access, use and share, free of charge – subject, at most, to the requirement to attribute. Governments, businesses and individuals can use open data to bring about social, governmental, economic and environmental benefits. For example, open data can help to track the waiting time in the ER of an Italian hospital, monitor the air quality in a French city, support the construction of a public procurement dashboard for Greek small- and medium-sized enterprises (SMEs), and build an application that provides migrants in Germany with key information about the country. Some of these and more cases can be found in the data.europa.eu’s high-level Open Data Maturity (ODM) Report 2022, which gathers insights into the state of open data in 35 European countries.

Given the great potential of open data – reflected yearly in the ODM publication – it is no surprise that more and more national governments and municipalities are publishing parts of their datasets on their national and local (open data) portals and encouraging its (re)use by citizens and businesses. Yet, the open data value chain – from the identification of datasets to the publication and reuse for creating impact – is long and requires both skilled data providers and re(users).

2023: The European Year of (open data) Skills?

Will 2023 be a turning point for open data skills in Europe? After all, it is the European Year of Skills. Yet, according to the ODM Report 2022, all EU27 Member States could do more when it comes to open data skills and training across the public sector.

So, what are EU countries doing to equip public sector bodies and citizens with the skills they need to collect, share, process, publish, and reuse open data? The state of play is described in the ODM Report 2022, which was commissioned by the Publications Office of the European Union and the European Commission (EC) in the realm of data.europa.eu, the official portal for European open data. Coordinated by Capgemini Invent, the report discusses the findings of the annual open data benchmarking exercise conducted across European countries (EU27+). In it, we discover that open data is a cornerstone of the EC’s policy for shaping Europe’s digital future.

However, despite data proliferating, governments and public agencies still lack the full complement of skills needed to translate open data into government policy and better citizen outcomes. In fact, the ODM Report reveals an absence of adequate data skills and literacy among civil servants. In particular, we observe a need for upskilling in aspects such as data literacy in general, understanding different licenses, knowledge of the different languages (e.g., DCAT-AP), and quantitative research on the economic impact. data.europa.eu also points to a need for greater skills in the technical, quality, legal (e.g., CC licenses) and governance aspects of making data openly available.

This is not the first time that limited skillsets have been cited as a barrier to digital and data-enabled advancement. For example, in a recent Capgemini Research Institute report, 54% of public sector organizations cite cultural challenges that include a lack of talent and skillsets as one of the barriers holding back the deployment of public sector data ecosystems.

How public sector data providers are addressing open data skills

Positively, moves are already underway to address this situation across the public sector. Almost all (26 of the EU27) Member States report that they have set up training activities to refine the data skills of civil servants working with open data.

The ODM Report identifies several best practices being deployed:

• Establish formal qualifications: 19 of the EU27 indicate that their training activities offer a formally recognized certification. For example, as part of a national digital competencies program, the Portuguese government has launched an initiative for the digital qualification of public servants. This aims to promote the coherent execution of digital and open data policies with short-term courses on several ICT domains, including open data, provided by the national institute for competencies in public administration.
• Collaborate with academic institutions: A program made available only for Public Sector Liaison Officers in Cyprus is delivered by the country’s Open Data Team in collaboration with the Open University of Cyprus. Participants receive a formal certification, which can then be submitted to the Public Service Commission. The program includes modules on data audits and selection of data for publication, preparing data for publication (formats), licensing, data and metadata quality, publication processes, and releasing data APIs.
• Invest in training: In Luxembourg, regular training sessions designed to inform and support data providers cover topics such as transparency, interoperability, open data, and data management. Elsewhere, the Polish National Open Data Portal (dane.gov.pl) includes multimedia training on open data and preparation of data in open formats. This features tutorials on how to make data available on the portal, and how to add data(sets) and resources.
• Set national policy: Some countries have incorporated skills into their national data policies. In France, for example, every ministry is expected to draw up a roadmap for data, algorithms, and source coded strategy, for which a list of skills needed will be required.

Increasing open data literacy among data (re)users

Beyond public sector data providers, there is, of course, a need to ensure the citizens, businesses and institutions who use and reuse the data gain benefit from it. Indeed, the future envisioned by the EC for Europe will have data as a foundational building block. The EC’s policies (and rationale of the annual Open Data Maturity assessment) aim to prepare EU Member States for this future with a focus on generating value for the economy and society through the reuse of public sector information – in other words: open data.

Some countries are already advancing their open data maturity by stimulating data literacy among (potential) users. The Open Data Maturity (ODM) Report 2022 points to events, such as hackathons, conferences or open data days, designed to raise awareness about open data and increase data literacy beyond public sector bodies. Focused on open data, these events are held annually at national, regional, and local level across the EU. According to the ODM 2022 assessment, 52% of EU Member States organize more than nine such events every year.

As well as upskilling today’s workforce, the employees of tomorrow will also need open data skills. The Czechitas organization in the Czech Republic, for example, runs the Digital Academy project, which, among other things, focuses on data analysis using open data. Czechitas is a non-profit organization that fosters the IT skilling of women and their subsequent integration into the IT labor market. This tackles two barriers to open data maturity, that of a lack of STEM education and the limited opportunities for women in the world of IT.

Accelerating open data learning – why now?

It is hard to envisage a world in which books, articles, and magazines aren’t accessible to everyone. And in today’s digital economy, the EC is determined that a lack of data literacy should be equally hard to envisage. Why? Because it wants the EU to be a leader in a data-driven society.

This ambition demands open data maturity. Without it, the EU risks trailing behind in the wake of those countries and regions already advancing their open data maturity, with the potential to impact economic prosperity, competitiveness, innovation, digital inclusion, healthcare outcomes, and evidence-based policy making.

Currently, however, it is clear from the ODM 2022 Report that more needs to be done to strengthen the skills of participants in the open data value chain. Even the most mature EU countries in open data (categorized as trend-setters) need to up their game when it comes to accelerating skills and training, with a recommendation for them to: ‘Work with training institutions on providing advanced open data courses and training’.

Rethinking how we build skills

Giulia Carsaniga, lead author for the ODM Report 2022, comments: “A Europe fit for the digital age is one of the European Commission’s overarching priorities. Skills will form the bedrock of this. The European Year of Skills is surely the time to rethink our approach to open data maturity and how we build the skills to accelerate it.”

The ODM Report encourages countries and data providers to do more for their national teams and for individuals, with a set of extremely useful recommendations for countries at varying levels of maturity. The data.europa.eu academy is also a good starting point for public sector open data users as they accelerate their open data learning.

This is why data sharing is becoming an important part of the conversation as governments accelerate their digital transformations to meet the evolving needs of their citizens. The benefits of digital ecosystems and data spaces – secure infrastructure where sensitive information can be pooled and shared while preserving privacy – are coming into focus for leaders with a strong digital vision.

And yet, serious obstacles are slowing progress toward realizing the full potential of these new paradigms. In a new Capgemini Research Institute report, -“Connecting the dots: Data sharing in the public sector,” more than half of public organizations surveyed cite trust, technology, and culture as the main factors holding them back from large-scale deployment of data ecosystems. Indeed, technology tops that list of concerns with 39% of surveyed organizations saying they lack the infrastructure to collect and identify data, 50% stating they can’t store and process it, and 52% are struggling to embrace modern tools like cloud and AI.

It’s easy to be frustrated by the slow pace of change. Only 10% of the public sector organizations we surveyed have deployed data ecosystems, while another 17% are in the early deployment phase. The remaining 73% are still planning or piloting initiatives.

While, these hurdles may seem high, they are not insurmountable. In speaking to these public digital officers, we came away encouraged, even inspired. Our survey found that public sector leaders believe data ecosystems could enable close 10% improvement on average in the use of government funds and resources. These public leaders are rising to the moment by finding creative and innovative ways to build data platforms that one day could revolutionize their roles in the daily lives of their people.

They understand that building mature data-sharing ecosystems is a long-term journey. Breaking that journey down into definable steps can make it less intimidating, and the goals more achievable.

In this report, we recommend a roadmap based on four key actions that public sector organizations should take to begin building support for their own data sharing strategies:

1. Identify the use cases, data sources, and participants necessary for an ecosystem: By clarifying the reason for developing a data ecosystem, uses cases will emerge that fit these goals. That, in turn will help identify and prioritize the internal and external data sources required, including citizen-generated data. Finally, determine who the participants in the data ecosystem should be, the value the ecosystem can deliver for each of them, and potential incentives to encourage data sharing.
   
2. Develop the infrastructure for interoperability and collaborative work with data: The digital infrastructure should include data exchange platforms, decentralized data management architectures such as data mesh, and a cloud-based foundation. There is a large choice of data exchange platforms that can enable sharing. Couple this with decentralized data that allows for federated ownership and governance policies that are easy to define, and the elements are in place to ensure security and compliance in a cloud environment.
   
3. Establish trust across the full spectrum of data-sharing practices, from governance and organizational set-ups to technology: Trust is one of the biggest barriers to data sharing and must be a priority. To build governance mechanisms that drive transparency and accountability, public organizations should leverage advancements in privacy-enhancing technologies (PETs). These include technologies such as differential privacy, federated learning, and homomorphic encryption that safeguard data privacy.
   
4. Develop skills and capabilities for a data-driven culture: This starts with holistic training to provide employees with skills in AI, data, and privacy management. Our survey found that only 55% of organizations have trained employees on the ethical use of citizen data. All employees must recognize their obligations and opportunity to participate in shaping data services rather than seeing data as a niche issue for the IT or data teams. Clear communication of the mission-driven goals can be an important motivator. There is also a robust playbook for building data culture, including sandboxes, innovation labs, hackathons, data academies, and academic collaboration, where appointing a Chief Data Officer can accelerate driving data strategy and coordinating initiatives with other stakeholders.

The data road ahead

At Capgemini, we see four key benefits of collaborative data ecosystems: citizen engagement, cost reduction, insight multiplication, and interoperability that leads to process efficiency. But we are not there yet. Governments have not answered the technological, human, and governance challenges necessary for laying the data sharing groundwork.

Maria Bäcklund-Hassel, a Senior Advisor and International Coordinator at Sweden’s eHealth Agency, explained the multitude of reasons that her country is struggling to embrace data sharing. Even so, she is optimistic that the rise of healthtech startups, the lessons from Covid, and the European Health Data Spaces (EHDS) proposal are going to break the logjam and create consensus for overcoming those issues. “The COVID-19 pandemic has been a great initiator of momentum around data sharing,” she said. “How great would it have been if we could have shared information then? To see the level of vaccination, and how the virus was spreading throughout Europe – this type of data is critical for us, it’s not just nice to have.”

It’s easy to see how such leaders can get sidetracked and demoralized. Public data leaders must confront data fragmentation, antiquated policies, complex rulemaking, conservative practices, and turf wars between different levels of governments. And yet, in the interviews for this report, we found a growing range of examples of leaders who were undaunted by these problems and were finding creative ways to blaze data sharing trails.

Consider, for instance, Europe. The European Commission has launched “DS4Skills”, a project focused on developing a data space for education and skills as part of a broader effort to create common European data spaces to accelerate the EU’s digital transformation. The Gaia-X initiative in Europe was created to develop a European cloud infrastructure based on the principles of cloud sovereignty and has spawned such experiments as Agdatahub, a data ecosystem to facilitate agricultural data sharing to promote sustainability and innovation. And the EU-funded project “MyHealth-MyData (MHMD),” is demonstrating the power of homomorphic encryption to facilitate the sharing of sensitive health data.

In Spain, Chief Data Officer Alberto Palomo-Lozano said his agency has a mandate to explore “potential ecosystems which can facilitate greater insights by combining this data with cloud technology.” He’s been collaborating with the nation’s CIO to develop the necessary governance and technology systems. As an early step, they’ve released a MyCitizen tool that let’s citizens browse information government agencies have collected on them. “One hope with this project is to facilitate a greater sense of data ownership by citizens and be able to engage them even more so in these processes,” he said.

Meanwhile, three Spanish hospitals launched a collaborative initiative using federated learning to increase the speed and accuracy of COVID-19 screening. Dr. Javier Blázquez, Head of the Radiology Department at Hospital Ramón y Cajal – one of the participating hospitals – said: “Federated learning allows us to improve our diagnostic reliability without disrupting data privacy, and since the experience of a hospital is shared among several others, the results improve a lot with respect to those obtained separately.”

These efforts clearly show, the benefits of collaborating with data. The report’s four action items chart a path for public data leaders ready to steer their organizations toward a data-sharing future. The time to start is now. It’s a matter of having the right vision and starting to plan to take those first steps.

Overview

The Multiple Virtual Storage (MVS) file system is the most commonly used file system for the IBM Z family of mainframes. However, MVS is not a hierarchical file system like today’s typical file systems such as Windows, Unix, macOS, and others. Most of the next-generation IT recruits are not used to working in the MVS environment. Other than MVS, IBM Z also has the Unix file system, which is hardly used compared to MVS. However, next-generation IT professionals are very familiar with Unix.

With z/OS2.5, IBM introduces a new file system type that will render traditional MVS data sets accessible by the z/OS UNIX namespace. As a result, Z2.5 will enable z/OS UNIX applications, tools, and utilities transparent access to data in these data sets securely and consistently.

IBM is calling this file system type Data Set File System (DSFS). It will map MVS data sets on a Unix namespace. IBM’s idea of accessing MVS data sets from the Unix address space makes MVS data sets visible and easily accessible to and from Unix commands, applications, tools, etc.

A turning point for IBM mainframes

Since the 1970s, businesses running on IBM Z mainframes have generally stored their data and application code on an MVS file system. As a result, it is one of the oldest and most widely used file systems for large and mid-sized businesses worldwide.

However, training recruits on MVS is a significant challenge for businesses running on IBM Z. Today, most universities and colleges don’t educate students on MVS file systems. It would take years of training for the current generation to make themselves familiar with MVS internals. Thus, there is a significant gap between the supply and demand of MVS-trained instructors in the market.

Suppose your new software recruits are not required to understand the MVS file system. Instead, they just need to work on the familiar Unix Bash shell environment to access your existing databases and codebases. That would be amazing, right?!

With z/OS 2.5, businesses don’t need to train recruits on MVS. They can even do lateral hiring based just on Unix skills. Most importantly, companies will not have to migrate their databases/codebases from mainframes because recruits lack the MVS skillset. Also, there will be no need to code separate code logic for accessing data from MVS and Unix file systems, which reduces program size and complexity. These are the kinds of benefits that DSFS can bring to mainframe-based businesses.

Figure 1 captures the technical details describing the use of DSFS to access MVS datasets on a Unix namespace.

Figure 1: How to use DSFS to access MVS datasets on a Unix namespace
Source: Capgemini Engineering

Figure 2 below shows how MVS datasets, residing on a direct-access storage device (DASD), can be accessed on the Unix namespace using DSFS. MVS dataset AAAA.BBBB.CCCC can be accessed as /dsfs/txt/aaaa/bbbb.cccc on Unix. Similarly, PPP.QQQ.RRR can be accessed as /dsfs/txt/ppp/qqq.rrr.

Figure 2: Data Set File System mapping
Source: Capgemini Engineering

DSFS brings value to the mainframe world in five important ways:

• A mapping mechanism to access MVS-cataloged (DASD) datasets from Unix namespace
• Reduction in the gap between demand and supply for MVS resources
• Reduction in cost and time in training resources on MVS
• Reduction in program code as single logic will be required to access both MVS datasets and Unix files
• Increase the confidence of businesses in the IBM mainframe. Yes, mainframes are adapting to your business needs and mainframes can modernize and change to help solve your problems.

Long gone are the days when an organization’s sustainability ranking was only judged by the impact of its own actions. Organizations that cannot provide relevant Scope 3 data in public reporting face the likelihood of stakeholders criticizing them for providing an incomplete picture of their total environmental impact. A company’s Scope 3 emissions account for 65 to 95 percent of the total CO2 footprint, but the findings from Capgemini Research Institute’s latest Data for Net Zero research show that only 22 percent of companies are measuring Scope 3 data today. This means that a majority of companies overlook most of their carbon footprints and simultaneously ignore the business value potential from emissions data.

The complexity of Scope 3

A company’s carbon footprint consists of Scope 1, 2, and 3 greenhouse gas emissions. Scope 3 emissions result from activities that are not directly owned or controlled by the company, including those coming from suppliers, distributors, partners, and customers. They are divided into 15 categories.

Measuring and reducing emissions and focus on shifts, but some valid Scope 3 measures are:

• Avoid unnecessary business travel and inefficient modes of transport

• Switch to lower-emission fuels and vehicles

• Manufacture more energy-efficient products

• Design durable, recyclable products.

Although sharing Scope 3 emission information has been optional for most businesses so far, both US and EU regulators are now pushing for increased transparency, and companies will increasingly face new regulatory requirements. Furthermore, companies are also realizing the business potential that sustainable value chains, operations, and products can provide. Findings from the Capgemini Research Institute show that 85 percent of organizations recognize the business value of emissions data, but it remains inadequately used: 45 percent of the companies use the data only for reporting.

“Without accurate data and the skills to use it, you don’t only struggle to report the right numbers, but it also becomes impossible to make predictions for the future and steer your business towards net-zero.”

The Scope 3 dilemma is all about solving a big data challenge

Scope 3 emissions come from external parties, so collecting accurate and reliable data can be particularly challenging. Without accurate data and the skills to use it, organizations not only struggle to report the right numbers but also to make predictions for the future and steer the business toward net-zero targets. So, Scope 3 is a big data challenge: reliable emissions data is needed for both asking the right questions and providing the right answers. But how to get started? Here are three data-foundation strategies that get things moving in the Scope 3 space.

Emissions data collection: start with identifying hotspots

When a company has defined net-zero targets, it usually has already created an understanding of the most carbon-intensive parts of the value chain. For example, to a manufacturing company, such a hotspot might be logistics. A data-collection strategy for Scope 3 is a plan aligned with the sustainability strategy. It describes what data is needed, from where to collect it, and what to measure. With Scope 3, organizations should always aim at getting the source data if available. In practice, this means reaching out to suppliers and partners and asking for emissions data allocated to the products and services purchased. In some cases, when the actual data cannot be accessed, another way to estimate the activity data should be agreed.

As with all data these days, a smart approach should be used. Hence, instead of collecting it all, focus on what data is really needed to understand where the organization is, and make predictions for the future.

Establish a shared single source of emissions data

A hub to ingest and store data in one place is next. The data will come in different formats and structures, and a common emissions data model will help make sense of it. Major cloud-platform providers such as Microsoft, Snowflake, and AWS have already developed accelerators in their stacks to support Scope 3 emissions data collection, modelling, and analytics. One advantage of these cloud platforms is that they tend to have great data collection and sharing capabilities for data outside the organization. In the end, Scope 3 is all about sharing across the data ecosystem. Once the data is in one place, it is possible to use this single source of truth for multiple purposes, such as carbon accounting, reporting, or analytics.

Let data lead the people

Calculating and reporting Scope 3 emissions can still be considered a quite straightforward part of the puzzle. When an organization starts building analytical models, what-if scenarios, or utilizing advanced AI – for example with digital twins – it gets tougher. The tricky part is how to make people trust the data and make decisions based on it. The data-powered sustainability game is, in the end about people, culture, and the right skills. As an example, in the manufacturing industry, R&D and procurement teams are at the heart of change. These teams decide what kind of products to make, materials and processes to use, and from where to buy. When these teams have knowledge of the net-zero targets, understand their role in reaching them, and have access to data, tools, and insights that lead the way, change can happen.

Emissions data literacy is a quality that involves people, tools, and processes. Similarly, to financial data literacy, emissions data and related skills are essential tools for tomorrow’s leaders. It is evident that one of the toughest elements in the race to net-zero lies in Scope 3. Despite this big data challenge, here’s the good news: by adopting a data-powered approach – starting with three foundational strategies – a business can take a leap towards a net-zero future, building data-management capabilities and data mastery along the way.

INNOVATION TAKEAWAYS

BE SMART WITH DATA COLLECTION

For Scope 3, identify hotspots, collect what is really needed, and aim always for the critical data.

ESTABLISH A SINGLE SOURCE OF TRUTH

Collect and store this data in a central place and manage it on top of a common sustainability data model.

SUPPORT AND EDUCATE

Equip teams with the skills and tools to understand and use emissions data for business decisions, investing in a net-zero future.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 5 features 19 such articles crafted by leading Capgemini and partner experts, about looking beyond the usual surroundings and be inspired by new ways to elevate data & AI. Explore the articles on serendipity, data like poker, circular economy, or data mesh. In addition, several articles are in collaboration with key technology partners such AWS, Denodo, Databricks and Dataiku. Find all previous Waves here.

We all know the transience of hype and how it can sometimes lead organizations to take the wrong turn or over-commit to new technology that doesn’t ultimately pan out. But for all the futuristic ideas surrounding these buzzwords, the tech is already here, and it’s exponentially developing into hitherto unseen horizons – think virtual smelling (more on that later).

The technology to simulate anything and everything a person can imagine in real life is now emerging. And the potential applications are inexhaustible. But Innovation is a means, not an end. The challenge is relevancy: how can this be applied now to benefit organizations?

In this blog, we’ll see how immersive experiences and NFTs can be utilized in commerce. Gently – but boldly – transition companies to higher consumer engagement.

Esteemed colleagues and expert speakers at the NRF 2023 have drawn an electrifying picture of what the future holds. Revealing some of the secrets behind these technologies and how they can be implemented to drive commerce into an exciting future.

What are immersive experiences?

Immersive experiences place the user in an environment that diverges from daily life. The applications are stunning. They could be anything from the more distinct type, like VR, to exploring the map of an otherworldly video game, or even something like an exclusive event at a remote location in real life.

The term itself refers to the psychological involvement of the experience rather than a specific set of external catalysts. (Immersive experience does not equal wearing virtual reality headsets.)

This is a broad definition, but it is essential to understanding the scope of what immersive can do – and the crux lies in the psychological aspect, which is what the consumer wants and values. As has been known for a while, consumers – the millennial demographic and the new Gen A even more so – are showing more interest in experiences rather than things, which provides a solid foundation for building a new strategy around product development and especially CX.

Where are we now with the tech? Some startups have managed to simulate touch and smell in a virtual environment. This is the latest and most encompassing simulation technology for the metaverse and virtual experiences to be released later this year. This opens up a new world of marketing opportunities in retail to extend e-commerce further than it’s ever reached.

NTFs – below the tip of the iceberg

We are in the age of Web 3.0, the new era of the internet with evolving decentralized blockchain technologies, including cryptocurrencies and non-fungible tokens, or NFTs. The popularity of NFTs surged and reached media outlets with stories of people paying outrageous amounts of money to own unique artwork – perhaps the first thought that comes to people’s minds when hearing the term. But what are they, really?

Blockchain technology enables a “transparent” and non-reproducible chain of blocks of information, with encryption linking each one to the previous with a timestamp and transaction data.

This distributed ledger, as opposed to a centralized database, removes the risk factor of a single point of failure. If part of the system suffers damage, data still exists in an identical form in another part of the system. This solid method of guaranteeing that the blocks are non-fungible and cannot be replaced/tampered with is the basis of cryptocurrencies and NFTs.

Once someone is recorded as being the owner of an NFT, this ownership is authenticated and certified by the blockchain. This authenticity can lend itself to the incorporation of exclusivity and uniqueness in products and rewards from brands.

How NFTs can be used? Exclusivity and a demo

Consumers like exclusivity, especially with luxury items and experiences. For example, there is an exclusive dining club in New York that is the first one of its kind – you can only enter it if you own an NFT for it (and further applications could be made to have tiered experiences within the restaurant, like additional NFTs allowing patrons to have premium dining experiences). This sort of use of NFTs can elevate luxury products, experiences, and rewards to new heights that surpass the mass market – a digital environment, tied to real life, that enables people to have the most exclusive possible access to what companies have to offer.

To showcase how this can be used, Capgemini has created a demo: someone buys a luxury bag. The journey for the customer starts with a QR code attached to the bag and when they scan this code, their ownership of the bag gets registered on the blockchain. Then, the customer receives an email inviting them to an exclusive event, like Fashion Week. This bag’s digital twin is now associated with an NFT. The customer can attend the event while others can’t – creating exclusivity – and they become a part of that community. This is the power of combining online-offline experiences.

Other online-offline applications that already exist include:

• Real-world shoes that, in the digital world, enable you to fly in a game
• A real-world car that can go faster in a racing game
• A ticket is being minted as an NFT to attend a conference

The same principle can be applied in countless ways.

Consumer naysayers versus the new generation of commerce

Sixty-five per cent of consumers have said they would prefer to have real-life, physical experiences than immersive ones, and many people consider the metaverse to be a bad thing for society. But many of us remember a time when the same was said about Web 2.0, what we’ve had for nearly two decades now – the internet as it exists today. Socializing as a kid would involve going to the mall or maybe chatting online with friends. Kids today instead might immerse themselves collectively in Minecraft or Roblox, entire digital worlds where they can create anything they want.

Like the reticence many in the industry had about the idea of online commerce back in the infancy of Web 2.0, it is expected that the same would be thought about where we are now, but the applications of Web 3.0 in the new age of digital commerce are already here. The younger generations especially are primed for the new experiences that this technology has to offer.

Moreover, according to the newest 2023 Capgemini Research Institute report, 58% of consumers – of several thousand across 11 countries and a thousand organizations – believe that immersive experiences will influence their next purchase. 77% believe that immersive experiences will influence how they engage with a brand after purchasing, using, and servicing their product. And in customer service, 77% also believed that immersive experiences will create loyalty. The key will be designing an omnichannel strategy with outstanding CX, based on robust research into customers’ expectations and use.

They don’t need to know

This sounds insidious, but the idea is that the consumer doesn’t need to be familiar with or understand the technology itself to find value in it. This is a simple concept that is implicit in the marketing of most products – most people don’t expect to know about the chips and processors in their electronics, for example. A mere 20% of US online adults said they were familiar with the metaverse, and only 20% have heard of NFTs but 31% do not understand what they are (including Gen Z). But the psychological aspect of immersive experiences is the actual intrinsic value that the consumer will find through the exclusivity of the event or experience tied to the product.

The novelty of Web 3.0 technologies might tempt companies to label their immersive experiences with the associated buzzwords. Companies that use NFTs and immersive experiences in their products and services do not necessarily need to have the term “NFT” attached to anything. In essence, the idea is to combine the technology with good CX research to produce the best experiences possible for the consumer and market them in the correct way for a frictionless experience. Nothing is gained by alienating customers with unnecessary jargon.

What are the limitations?

The hardware is not yet as fast as the software. We now have 5G, are exploring 6G, have WiFi 6 on the horizon, and are beginning to explore 7G, but these are not quite where they need to be yet. The technology and infrastructure are there and improving, but the current state of the hardware is a limiting factor.

For example, virtual try-on – the ability for consumers to virtually try on products using their devices via augmented reality and layering visuals – is still not quite right and doesn’t sufficiently simulate the look of an item of clothing or shoes on the person. But it will be soon. (In the case of some clothing, such as sunglasses, it’s already there.)

The hardware limitations can be a thorn in our side, preventing us from moving faster, but the existing technology has reached the stage that, if used in the right way, it can launch organizations further into the new wave of commerce.

How can I get in with the new frontier of commerce?

Immersive experiences are undoubtedly the new frontier and future of commerce. At Capgemini, we urge clients to start their journey toward these new vistas – and it’s better to begin now than later. With so much on the horizon, you need a good place to start and to select the right tools and strategies for your organization.

To see what else happened at NRF 2023, check out our event page. You can also get in touch with our team to find out more about immersive experiences and book a demo.

If you’re interested in discovering what Capgemini can do to kickstart your journey into the future, visit our Retail and Consumer Products pages.