Growing demand for network data

CSPs are facing challenges on many fronts – rolling out new network technologies such as FTTH (Fiber to the Home) and 5G, improving customer satisfaction, while at the same time trying to reduce operating costs. The one link between these disparate goals is the need for network data. The variety of these data and their volume (often 10’s or 100’s of TB per day), make them valuable to many operational processes, and enable CSPs to:

• build personal, one-to-one interactions with the customer, relying on a deep understanding of her behavior and experience;
• improve service assurance, gaining insight into the quality of service that customers experience, without the need for surveys or complaints;
• Help Engineering make smart network investments through insights into the nature and quality of services, value of customers, bandwidth, and latency appetite.

In these cases and many others, readily available network data would help a lot. The question is: what’s the best way for CSPs to gather, collate and use their network data?

An outdated dilemma: buying a platform or building your own?

To most CSPs this is nothing new; indeed, they’ve been amongst the first adventurers in the deployment of big data. But up until recently they’ve been choosing between two strategies, neither of which fully meets their needs:

1. Buying an off-the-shelf, proprietary solution. On the plus side, this option is pre-built with integrated telecom expertise. Unfortunately, data and derived insights are too often confined within the limits of a proprietary environment – tied to a specific data model, which is owned by the vendor and not the CSP. This makes it difficult or impossible to use your data in new ways, which may not have been considered when the platform was designed. It can also be very difficult to find the expertise necessary to manage these platforms, further limiting the potential uses of valuable data.
2. Building one’s own big data platform with open-source technologies. This has proven quite effective at capturing massive amounts of data, but it demands significant resources. It’s difficult to evolve; it relies on dedicated expertise, and like the off-the-shelf option, it’s difficult to scale. It ties up valuable resources which you would prefer to allocate on solving business problems!

All in all, both options have proven to be more expensive than initially expected, especially in the long run. They both lack flexibility and fail to exploit the full potential of data across the many dimensions of the organization.

Why cloud-native technologies are taking the lead

CSPs have already embraced cloud-native technologies to support their data transformations, with the first initiatives focusing on the corporate and customer domains. More recently, many have embraced the cloud for their network data initiatives, and for good reasons:

• AI – artificial intelligence is becoming a ubiquitous tool, using data to improve operational processes as well as quality of service and relationships with clients. To implement and operationalize AI, CSPs and their data scientists need a wide choice of advanced tools and techniques, plus access to large datasets and large computational power at an economical cost when needed for training models. Cloud-native data platforms deliver these advanced AI capabilities, and with a much lower price tag than most in-house solutions;
• Value – open source and cloud provide a wide range of other advanced capabilities as well, in a way that’s easy to use and highly cost-effective. Under constant pressure to improve their operations and invest wisely, CSPs can rely on cloud to make sure every penny counts;
• Ownership – many CSPs have recently announced plans to become more “software-oriented” – in a sense more like the big Internet companies – developing software on their own, within their own control. They realize the criticality of their data and associated software, which manages, extracts value from and activates those data. It benefits CSPs greatly to own their code and make it a core asset. Cloud aids massively in the creation of agile software solutions, and in later adaptations and scaling.
• Uniqueness – behind a façade of high normalization, networks and service platforms actually differ substantially from CSP to CSP, due to the histories of the particular organizations, the choices of architecture, and the combination of vendors and technologies. Thus, any off-the-shelf solution requires significant amounts of adaptation to meet the individual needs of a given CSP (due to the innate limitations of its design). Cloud-native data platforms make it possible to collect, process and massage data according to the unique needs of each operator. One size does not fit all when managing complex networks and extracting information out of them!

In a word, cloud offers agility. It makes it possible to experiment, adjust, pivot, personalize and scale with a freedom that’s simply not practical with off-the-shelf or in-house platforms. And with the increasingly central role of data, this freedom helps to enable Agility and DevOps throughout your organization.

Functions expected from the Network Data Platform

Cloud-native data technologies are making the dream of network data democratization come true, while helping CSPs advance many of the challenges they’re facing, including the three mentioned at the outset. How?

• One-to-one interactions – a cloud data platform renders network and service usage data more accessible – breaking down silos while ensuring security and privacy. This makes it possible to share the data necessary to drive the personalized, one-to-one customer interactions CSPs strive for.
• Service assurance automation – a combined edge / cloud data platform collects CPE (Customer Premises Equipment) and network telemetry data efficiently – filtering, aggregating and correlating to obtain real-time insights into the operation of services. CSPs can thus spot potential problems early and identify root causes.
• Smart investments – a cloud data platform collects high volumes of data on network, usage and quality of service (typically from cell towers and transmission equipment), aggregates them and applies advanced analytics and machine learning to anticipate future consumption patterns, identifies revenue opportunities, and prepares optimal investment allocation.

Capgemini as partner for building cloud network data platforms

At Capgemini we have experience deploying network data platforms for our clients, starting with on-premises big data platforms and then migrating them to the cloud, or starting with a cloud-native use case and expanding. We expect to see more CSPs taking advantage of the possibilities of cloud network data platforms to unleash the power of their data, remain in control, extract value and become data masters in combining network and customer data. Contact us to learn more.

Interested in cloud network data platforms?

Read about Capgemini’s Cloud Platform

TelcoInsights is a series of posts about the latest trends and opportunities in the telecommunications industry – powered by a community of global industry experts and thought leaders.

Yannick Martel

Group offer lead for Data & AI in the Telecom industry

The automobile industry follows a periodic maintenance model for servicing cars defined by the OEM based on travel distance or time. During the inspection, certain critical parts are examined visually by the OEM service center’s maintenance team to ensure they are functioning as designed. Sometimes, the team will identify car parts that need replacing to avoid major breakdowns. However, one downside of periodic maintenance is that it can lead to higher service costs as technically functional parts are replaced to improve performance even though they are functioning.

A better way to manage car servicing is proactive maintenance that utilizes intelligence embedded in the car to monitor and assess the working condition of parts. The car collects sensor data at regular intervals and sends the relevant data to the service center. The maintenance team uses the data to predict failures in advance and provides quick service as needed to meet the car owner’s service level agreement.

The transition to proactive maintenance is part of the automotive industry’s digital transformation journey. Cars now have cellular connectivity (e.g., 4G and 5G), enabling them to share telematics data with the service provider over the internet for real-time monitoring and to schedule a service. The shared data from the car include speed, idling time, harsh acceleration, harsh braking, fuel consumption, mileage per gallon/liter, coolant temperature, level of coolant, maximum speed, engine oil level, fuel level, and distance traveled. (See Figure 1.)

The data collected can trigger an emergency or routine service. In addition, managing the runtime/real-time data can lower the cost of the traditional pre-set service. For example, if a specific part needs immediate replacement, it can be addressed before failure. The alternative is to risk a breakdown that would be significantly more expensive for the car owner.

Telematics data captured from the car network
Source: Capgemini Engineering

Real-time monitoring continuously assesses the connected car’s health and predicts faults and component failure. The data collected from the car can be used to create personalized plans for vacation car travel, improve driving enjoyment, ensure safety and reliability, and reduce the chance of a significant breakdown during vacation. The car collects sensor data using a controller area network (CAN) installed in the car network that sends data to the service center to decide whether to replace parts and predict the improved performance.

The biggest challenge of monitoring real-time car data is data management. Data generation from various sensors include cameras, Lidar, radar, ultrasonic sensors, and the Global Navigation Satellite System, which can reach 25 GB per hour or higher.[1]  Therefore, analyzing the data in real-time requires a mechanism to analyze and manage the data at the network’s edge – in the car’s e-cockpit platform – because 25 GB per hour cannot be transmitted for analysis over the mobile network operator’s 3G or 4G LTE cellular networks to the OEM’s datacenter in the cloud.

The car data monitoring system (CDMS) performs data collection, processing, and analytics inside the car network at the edge. After processing, filtering, and analyzing the data at the edge, only essential data is transmitted securely to the OEM datacenter to avoid hackers tampering with it. (See Figure 2.) Always-on connectivity is required to avoid any interruption when transferring the data.

Using CDMS in the connected-car ecosystem is a growing trend for monitoring the wear and tear of car parts when in drive mode based on data collected from various sensors. The service center uses the information to predict any upcoming maintenance needs, which helps prevent drivers from getting stuck on the side of the road facing significant repair costs. Establishing secure, always-on connectivity with the service center and sharing critical data can potentially enrich the car’s performance in the long run and avoid major breakdowns.

The benefits of CDMS in the car’s e-cockpit platform include:

• Lower cost of maintaining, repairing, and inspecting the car ecosystem
• Less downtime
• Better performance
• Improved reliability, availability, and maintainability
• Delivering intelligence by evaluating the relevant data and using it to achieve longer service life and lower service costs

The CDMS enables the service center to monitor the car’s condition remotely. When servicing the car, staff can account for how the car is driven to estimate the car’s lifespan. The car telematics data is collected, processed, and analyzed to predict possible failures, and the data is used to inform the type of maintenance work the car needs. If an error is found, service staff can take action to delay or prevent failure.

The CDMS can diagnose faults before they become critical and predict the life of the components to ensure operational effectiveness, thereby reducing maintenance overhead. The CDMS addresses this challenge by measuring different parameters and providing support for proactive analysis and other statistics to display the present status of car components by evaluating the car’s current health and the driver’s safety data. (See Figure 3.)

There are five key elements required for the CDMS to handle the data in the e-cockpit platform:

1. Data collection and processing
2. Data monitoring and management
3. Data traffic optimization (compression)
4. Secure data transmission
5. Seamless connectivity

The connected car market is emerging and can significantly boost revenues for OEMs, Tier-1 suppliers, and service providers in the coming years. Four technologies will enable tomorrow’s cars: connected, autonomous, shared, and electric. If the car breaks down or suffers equipment failures while in use, it can transmit its telematics data securely to a roadside assistance technician who can quickly trace the problem, fix it, and get the driver on their way.

Telematics will be a vital component of the car ecosystem in the coming years. For instance, it will provide immediate service information to the driver by constantly monitoring the car’s parameters. The CDMS framework embedded in the e-cockpit platform will collect and processes data received from the CAN bus, enabling seamless switching over a 4G or 5G cellular network. With fast and stable access to the internet, the driver can access the menu of services offered by the OEM service center.

With advanced technology in the connected car ecosystem, the OEM service center will monitor the car’s parts while driving and determine the exact time for the next inspection and replacement of critical components based on their actual condition. Also, the CDMS framework will monitor the car’s systems and the owner’s driving habits to suggest ways to improve situational awareness displayed on the e-cockpit dashboard. In addition to analyzing the data, the CDMS will manage the car’s data security, data compression, and high-speed data mobility (i.e., seamless connectivity with persistence) to improve the end-user experience addressing all network-related challenges.

[1] Simon Wright, “Autonomous cars generate more than 300 TB of data per year,” Jul 2, 2021, Tuxera https://www.tuxera.com/blog/autonomous-cars-300-tb-of-data-per-year

For an in-depth analysis, download the white paper “OPTIMIZE VEHICLE SERVICE WITH EDGE-BASED TELEMATICS“.

Author: Vijay Anand, Senior Director, Technology, and Chief IoT Architect, Capgemini Engineering

Vijay plays a strategic leadership role in building connected IoT solutions in many market segments, including consumer and industrial IoT. He has over 25 years of experience and has published 19 research papers, including IEEE award-winning articles. He is currently pursuing a Ph.D. at the Crescent Institute of Science and Technology, India.

The world of retail is another case in point. Pre-pandemic, we were seeing significant growth in e-commerce sales: in 2019, they grew 20.2% year-on-year to reach $3.35 trillion. But in 2020, under lockdown, that growth accelerated breathtakingly – by 27.6%, to $4.28 trillion.

eMarketer, “Worldwide ecommerce will approach $5 trillion this year,” January 2021: quoted in “A New Playbook for Chief Marketing Officers,” published by the Capgemini Research Institute

The parallel between the two struck me recently. The traditional motto of the Olympic Games is “citius, altius, forties” – “faster, higher, stronger.” Retail organizations in general, and their chief marketing officers (CMOs) in particular, are experiencing broader, more sustained, and more rapid growth than ever before – and the same is true of CMOs’ roles.

Their function is expanding beyond brand-building and scheduled promotions to include a wide range of other activities, including data analysis, mar-tech deployment, business strategy, business growth, supply chain integration for fulfilment, and customer experience. Faster, higher, stronger demands, indeed.

A strategic partner to driving business growth

It’s a trend that’s corroborated by a recent report from the Capgemini Research Institute (CRI). “A New Playbook for Chief Marketing Officers” tells us that over half (57%) of marketers agree that their C-suite executives now see marketing not as a cost center, but as a strategic partner in driving business growth.

Given the increasing breadth of their responsibilities, you might think that CMOs are seeking greater support from external marketing specialists. That’s not completely the case. While the CRI report does show that most marketers are saying their teams work in partnership with agencies for activities such as branding and marketing strategy and digital marketing, it also reveals that in the next two to three years, 43% of them plan to bring this work in-house.

In short, marketing has become central to the success of the enterprise – and, given the growing importance of e-commerce and the need for marketers to understand how customers interact with brands and companies (and to know when and where to engage with them), it increasingly needs to respond in real time.

Real-time, data-driven marketing

Real-time marketing enables marketers to collect relevant customer data, make quick decisions along the customer journey, be more proactive in engaging customers, support customized content, and enhance the e-commerce experience. It depends not just on gathering data, but on interpreting it and acting upon it quickly. Data-driven marketers process, analyze, and leverage data to fine-tune their campaigns, their content, and their other marketing outputs. By taking a data-driven approach, they also gain deeper understanding of consumers and trends, and target customers with personalized and relevant offers and services.

Analysis in the CRI report shows that fewer than half of marketers can turn their data to good advantage. More of them would like to be able to develop data-driven go-to-market strategies.

Opportunity for progress

There is an opportunity here to make real progress. To take it, CMOs need access to a data platform that provides a unified view of the customer. They also need AI tools and skills to automate their customer segmentation and grouping. The best way they can do all this is to bring together the people and the processes in their organizations, removing obstacles, and creating what we at Capgemini call the Frictionless Enterprise.

Capgemini’s own Connected Marketing Operations offer provides one such solution. It acts as a central hub, and aggregates knowledge, so organizations can see both the bigger and the smaller picture; so they can extend their channel reach, and increase the effectiveness of campaigns; and so they can improve their operational efficiency at the same time.

Integrated – and smart

Just before the Tokyo Olympics last July, the International Olympic Committee added a word to that famous motto. Translated from Latin, it now reads: “Faster, higher, stronger – together.” In today’s fast-paced, rapidly growing marketing environment, that sense of togetherness is just as important. When everything comes together in a single enterprise, with a common platform sharing data that can be interpreted and actioned in real-time – that’s when the magic can happen.

In the second article in this short series, we’ll be looking more closely at what data-driven marketing looks like, and what it can achieve.

To learn more about how Capgemini’s Connected Marketing Operations unlocks enhanced brand value and revenue impact through frictionless, digitally-augmented marketing operations, contact: abha.singh@capgemini.com

Read the full CRI “A New Playbook for Chief Marketing Officers” report to learn why CMOs should enable real-time marketing to drive sustainable growth.

Pulled in two directions

Over the next five years, energy efficiency will become a core focus in the telco industry, as two sets of forces pull in opposite directions. First, energy needs will rise. A phenomenal rise of data and energy-hungry tech will continue to drive energy usage, which already constitutes 20 – 40% of telco OPEX. Second, industries around the world are beginning to respond in earnest to calls for more sustainable practices, which are coming not just from governments and the public, but increasingly from enterprise clients. It’s no wonder that upwards of one-third of operators have committed to net-zero goals. Telcos that fail in this challenge will soon find themselves operating with higher costs, degraded reputations and fewer clients. But the opportunity for those who successfully increase their energy efficiency will be profound. With enviable cost savings and well-earned ESG status, these telcos will operate with a powerful competitive advantage.

Exponentially increasing data needs

Data has been on the rise for years, and will continue accelerating exponentially in the near future. Technologies such as holograms, video-led digital experiences and new, digitally-generated realities such as the metaverse will require data on a scale that dwarfs anything we’ve seen. Even basic services will become increasingly data-heavy. Worldwide, data usage of average mobile subscribers is expected to increase from the current average of 11.4GB/month up to 34 GB/month by 2025 and 53 GB/month by 2027. Although 5G is proportionally more energy efficient than 4G, that difference will be swamped by the amount of data surging through networks. Additionally, 5G sites require more energy than their 4G equivalents, and 5G depends upon large numbers of closely-linked data stations. Today telcos are consuming between 2 and 3% of the world’s energy. Consumption will rise over the coming years; there’s little doubt of that. Telcos would be wise to put their energy efficiency strategy into action before this rise in data and energy becomes unmanageable.

And many are doing just that. A recent GSMA survey of mobile operators found that 92% rated energy efficiency and sustainability as very or extremely important. But how to make that change is another matter. New science-based target initiatives (SBTi) standards limit offsetting carbon to just 5%. To move beyond that requires the analysis of large amounts of data to identify critical network weaknesses, but for most operators it is precisely these capabilities that are incomplete. In fact, many active and passive network equipment elements are not currently set up to measure energy consumption, let alone optimize it.

Where to start

The number one place to look for improvements is in the networks. Network energy usage swallows up  a huge amount of a mobile operators’ OPEX, and 70-90 % of that energy is consumed by RAN. These massive networks were built to maximize connectivity, not to minimize energy usage. And that means plenty of opportunity to go back in and make improvements. 

Some telcos have begun to find ways to cut the emissions from their networks. Our team recently worked together with one industry leader on a project to increase spectral efficiency. This project has demonstrated that through AI based RAN optimization, there is strong potential to  reduce the number of sites and lower emissions. Other telco players have found  success implementing remote sites driven by self-sufficient renewable energy sources – solar, wind and hydrogen. Vodafone, for example, has been launching eco towers across the UK, and Telia has launched a self-sufficient tower in the scenic Trollstigen region to address the coverage need in the remote but trafficked area. 

The transition from 4G to 5G also provides an interesting opportunity to optimize for energy saving. Many operators are in the midst of sunsetting their 2G and 3G networks, which is beneficial from an energy efficiency perspective, especially when equipment can be reused or recycled. A few additional steps operators might take include:

• Modernizing the network across all network nodes (especially transport, data-center and RAN) 
• Implementing energy measurement and saving features, such as AI powered MIMO sleep mode,  and in turn also improve network performance 
• Selecting virtualization-based architecture across network architecture and virtualized-RAN architecture 
• Utilizing the AI/ML concepts and O-RAN based architecture to improve network efficiency 

Nokia and Telefónica have made great strides in the last category, and are working together to build green 5G networks, as well as developing smart energy network infrastructure and AI / ML technologies to improve sustainability and performance. Despite traffic tripling since 2015, Telefonica su

cceeded in reducing their energy consumption last year by 1%. Ericsson has also had successful trials on the application of reinforcement learning (a type of machine learning) to remote electrical tilt of antennas, resulting in a 20% decrease in downlink (DL) transmission power, without affecting performance. And the trend is spreading. Research finds that fully half of CSPs expect to save 10 – 20% in energy costs in the coming years – that’s 50% of CSPs that will be operating with significantly lower costs, and with a powerful sales advantage. 

The triple bottom line

Tackling inefficiency in networks is a crucial step, and many telcos already have a strategy in place. Where they face difficulty is primarily in tracking and measuring their progress along their net zero roadmaps. In addition to reducing their own carbon footprints, CSPs need to develop greener services and products in order to reduce their scope-3 emissions. These scope-3 emissions are looming large on the horizon – accounting for around 75% of the carbon footprint of a typical telco, according to some estimates. They will require impressive data capabilities and new levels of industry collaboration. Let’s not fool ourselves. Reaching net zero will not be an easy task, for mobile operators, or other companies, governments or private individuals. Lasting change depends on a mindset change in all layers of the organization. The benefits are a triple bottom line  – bringing value to telecom operators, customers and our shared environment.

The fact is, what you can’t measure, you can’t manage. That’s where Capgemini’s capabilities are playing a crucial role in CSPs’ journey to net zero. To learn more about the ways we can help you leverage your data and equipment to reduce emissions, contact us below. With the power of intelligent platforms, the future of telecommunications will be efficient, sustainable and bright.

#TelcoInsights is a series of posts about the latest trends and opportunities in the telecommunications industry – powered by a community of global industry experts and thought leaders.

As governments scramble to respond to the enormous challenges facing societies, economies and our planet, speed and agility are now essential attributes for public authorities. During the pandemic, national treasuries often had to set aside traditional structures and processes in order to release the huge sums of money so urgently needed to maintain social cohesion.

In turn, many tax and customs authorities are transforming too, embracing new and innovative ways to keep essential tax revenues flowing, that respond to changes in society, and the financial imperatives of the health and climate emergencies, while maintaining security and compliance.

Digital technologies, data and cloud are providing the transformational tools required. Automation and AI are replacing manual processes, producing more agile, service-driven organizations, able to meet customer demands for convenience, speed, and ease of use. Data and analytics are informing decision making and financial planning, as well as nudging citizens towards the right behaviors, while helping with their entitlements and obligations. Skilled tax professionals are becoming active change agents, creating more flexible and technology-enabled tax regimes that help drive key social, economic, and environmental policies.

1. Building trust and security will help transform tax authorities’ place in the economy and society

As tax authorities continue their fightback against cyber criminals by bolstering their defenses with increasingly robust and sophisticated cybersecurity measures, not only are they protecting vital national resources and infrastructure, but they are also building that priceless commodity – trust.

Trust is a critical component in the ongoing evolution of tax authorities, from enforcers to business enablers and active participants for good, providing the resources that deliver governments’ key social, economic, and environmental policies.

Trust can be truly transformational in the tax world. When people trust their tax authority, they are more likely to pay their taxes in full and on time. When citizens feel that their tax system is fair, secure, transparent, and operating in the best interests of society, they are more likely to share their data; more likely to adopt digital processes and modern payment mechanisms; and more likely to use technologies such as cognitive care when they need support.

In these circumstances businesses are more likely to see tax authorities as potential partners, participants in rich data ecosystems, collaborating and sharing information on their tax affairs while bringing benefits to society by tracking ethical practices such as the living wage or adherence to modern slavery legislation. This is a powerful reach far beyond that tax authority’s traditional role.

As these new relationships – and the trust that sits at their core – become established and grow, so that spirit of collaboration can extend throughout economies and societies, driving sustainable economic growth, supporting businesses, and achieving social responsibility goals.

Enhanced cybersecurity has also enabled tax authorities to successfully embrace hybrid working during COVID-19, at a time of unique risk and vulnerability, with criminals eager to exploit any loopholes as public sector organizations scrambled to formulate their responses to the pandemic. This must remain a focus as criminals become more inventive in their exploitation of weaknesses, with great emphasis on supporting and protecting users in their critical tasks through education, new processes and technology enablers.

2. User-centric products and services, combined with technology, will drive participation

Today’s customers, whether consuming services from their mobile phone company, clothing retailer or tax authority, expect a fast, frictionless, and personalized multi-modal digital experience, informed by an understanding of life events and, in the case of their own tax situation, precise information about tax obligations and entitlements.

In 2022, the drive for hyper-personalization will accelerate, with tax authorities adopting best practices from across the economy to apply user-centricity to all stages of the customer journey, to increase trust, confidence and compliance with tax laws and obligations, while also reducing the need for costly agents and accountants.

Digitally native customers will adopt self-sovereign data practices, ensuring that the data that tax authorities hold on them and their businesses is accurate, while also deciding who else they wish to share it with. This will give rise to new forms of data sharing and consent across geographical boundaries, facilitating ease of movement, and also improving overall tax compliance by making pre-populated tax returns and payments an easy process. In 2021 the UK’s HMRC launched the world’s first public sector Open Banking payment initiation system, enabling payments to be made directly from bank payment accounts to payee bank accounts, without the use of cards.

Meanwhile, advances in mobile technology, 5G and edge computing will enable more media and AI-enabled applications for tax administration to become available, serving the needs of all taxpayers, but in particular younger people for whom smart devices are instinctive and the default choice. By providing a feature-rich user experience, new taxpayers can be better informed about the role of tax in society and be confident to manage their tax affairs and share their data from the palm of their hand.

3. Data sharing and data sovereignty will deliver choice and control

Real-time data will drive tax obligations and welfare entitlement at the point of the transaction, driven by closer integration with customers’ third-party applications, and voluntary compliance through integration with their banking and platform lives.

The importance of real-time data will be amplified across Europe as e-invoicing and VAT standards are mandated, enabling both more accurate data capture and AI-driven repayments, based upon risk and provenance. This will promote stronger economic activity with greatly reduced friction.

Combining rich data from Open Banking, payment and other third-party data will allow AI and pattern recognition to enable early identification of business vulnerabilities, allowing customers to declare their risks, seek support and prevent unrecoverable business debt and individual hardship. Early warnings will enable tax authorities to make better decisions about compliance and debt management interventions as early as possible.

Meanwhile the use of common data spaces and ecosystems, driven by standards in Open Finance, will allow tax-related data to be shared, with consent, to recover tax in a more transparent and frictionless manner. There will also be an ongoing focus on closed ecosystems sharing critical, cross-border financial information to close gaps in financial crime and tax evasion.

4. Demographic shifts will produce growth in indirect taxes – and automation and AI

Demographic studies reveal growing social and economic challenges facing industrialized nations, caused by rapidly aging populations. The UN predicts that those over 65 years of age will double from 727 million in 2020 to more than 1.5 billion by 2050.

Among the many consequences of this trend are a reduction in the working-age population, rising healthcare and pension costs, and increased demand in the economy for products and services for older citizens. As a result, 2022 will see governments, through their tax authorities, continuing the trend towards more indirect tax regimes, where citizens will pay for the things they use and the assets they own, rather than contributing to national budgets through income or business taxes.

At the same time, similar effects are being experienced by tax authorities themselves as older, skilled and experienced tax professionals retire, with lower numbers of experts available to replace them.

Here, 2022 will see further extensions in hybrid, more sustainable working models and intelligent industry techniques, using data to allocate tasks to the most appropriate resources, deploying automation, AI and collaboration tools to enhance productivity, reduce errors and enable smaller teams to work on higher value tasks.

5. Tax will be increasingly used to drive consumer behavior

Humanity must achieve the most fundamental change in its behavior, in the shortest period of time in its history, if Net Zero 2050 is to become a reality.  Although by common consent we’re starting to fall behind in the race to Net Zero, even at this late stage, all is not lost.

By redoubling our efforts and taking fast, effective and coordinated action, the line on the graph can still be reset to the required trajectory, towards the 2030 targets that we must hit to achieve 2050.

To achieve the mass consumer participation that brings Net Zero into range, more and more products and services produced by sustainable means must be affordable, easy to access and simple to use, for the overwhelming majority of consumers.  Currently, uncompetitive prices, lack of availability and perceived complexity are still pushing too many consumers in the direction of high-carbon, unsustainable solutions.

In 2022, tax authorities will have an increasingly important role to play in enabling more and more consumers to contribute to the global effort, deploying a variety of tax policies to encourage citizens to make the vital personal changes in lifestyle and purchasing decisions – electric cars versus petrol or diesel power for example – that are essential if we are to deliver a brighter future for all.

Further reading

For information about Capgemini’s tax and customs services, visit here.

The news that hybrid working is the future of work surely comes as music to the ears of many. For office-based workers, it means a flexible start each morning, or the opportunity to make the most of ‘digital nomad’ visas. Organizations too have benefited, with as many as 70% of them seeing improvements to productivity, reduction in facility management costs. More importantly, hybrid working bodes well for diversity and inclusion.

This rapid shift to hybrid working means employees want to be able to work seamlessly across locations, devices, and on the move. Organizations now need to re-look at their digital operations model and address technical and operational debt accrued over the years. With the fundamental infrastructure in place, it is imperative organizations take the next steps to deliver the Total Employee Experience.

In 2022, there are two aspects to hybrid workplace leadership: optimizing the base and preparing for transformation. Here we look at key trends that we expect to accelerate this year.

Narrowing the ‘Digital Dexterity’ Gap

Digital dexterity denotes the ability of an employee to take full advantage of the technology at hand. In plain language, can I use the tools I’ve been given to their full capability? Or is it proving a challenge? The answer to these questions is a bit tricky as we have four generations in the workplace – Baby Boomers and Generations X, Y, and Z – with differing technology skillsets. Failure to manage this disparity and maximize capabilities directly impacts productivity- and with more connected technologies entering the workplace this year, issues will only snowball if left unaddressed.

Narrowing the gap starts with training. Employing external trainers or utilizing in-house IT teams to run employees through the ‘whats’ and ‘hows’ might sound obvious, but few organizations have done it. Productivity tools like Microsoft Teams are more than messaging services – they are platforms for a more efficient, collaborative way of working. Optimizing familiar programs is essential; introducing productivity augmenting capabilities with automation and emerging technologies is the next step.

Patching Vulnerabilities

With the introduction of IoT connected workplaces and with many employees working from home, data ecosystems are getting larger and more complex. Both multiply the entry points for cyber attackers, making robust cybersecurity more important than ever.

With a lens on addressing new vulnerabilities, leaders must evaluate how this will affect the employee experience. To address this, we must step away from bolted-on security towards built-in capabilities while ensuring partners and supply chains are protected. The raised threat level punches above the capabilities of small or in-house security providers, and so leaders must engage with larger, global organizations to manage security. This does not spell the end of in-house security but should accelerate its evolution into a more employee-centric service.

Achieving True Flexibility

We are much closer to understanding what flexibility means to employees. It’s the freedom to work wherever, whenever, and however you can be most productive and attentive to your personal wellbeing. Leaders must make informed decisions about the degree to which this is offered, but what is clear is that flexibility is desired and desirable. A recent study found that flexible working delivered a £37 billion-a-year boost to the UK economy and with 50% more uptake, its value could rise to £55 billion.

The next step is operational flexibility. We are already seeing a shift in how customers engage with services. Agile contracts from niche service providers are becoming more in demand as they enable organizations to respond faster – as opposed to being weighed down by locked in, long-term commitments. The surge of new providers delivering this ‘Netflix’ model will be important in addressing industry-specific needs by offering verticalized solutions.

Augmenting Inclusivity and Sustainability

Gartner Research into hybrid working has found that it can boost inclusion by 24%. The technology keeping us connected has enabled organizations to widen the pool of candidates by extending geographical range. This reflects the importance of delivering a workplace experience that goes beyond simple connection. Increasingly, employees expect consumer-grade experiences that are both personalized and intuitive to their needs.

Technologies such as Virtual Reality and Augmented Reality can be used to enhance training, collaboration, and recruitment. Engaging in simulations of day-to-day interactions, for instance, is an effective way to inculcate a culture of diversity and inclusion.

Digital adoption is and will be fundamental to delivering this. Therefore, it is important to design a workplace that interconnects technology with people and workspaces. If, for example, an employees’ laptop is coming to the end of its lifecycle, new predictive technology should be able to anticipate this and replace it before it malfunctions. The ‘self-healing’ workplace is just one of many ways new technology can be leveraged to make employees feel more valued and engaged. In addition, AI and data driven technologies can automate workloads, freeing up employees’ time to innovate or engage in tasks higher up the value-chain.

As well as inclusivity, we will see sustainability goals accelerated by hybrid working and new technology this year. We at Capgemini have a two-fold ambition of delivering a net zero future to our clients as well as within our group. By leveraging the right workplace technology and tools we seek to help our clients save 10 million carbon tonnes by 2030. As a group, we have an internal target of becoming carbon neutral by 2025 and net zero by 2030. Global carbon emissions dipped by 7% in 2020, attributed to reduction in business travel and lower office energy bills for electricity, heating, and cooling. With the right Digital Workplace tools and platforms, there’s no reason why this trend shouldn’t continue.

Putting Experience in the Driving Seat

At Capgemini, experience drives everything. This might once have been misconstrued as fluffy but the last two years have shown us that connecting employees is far more nuanced than providing essential technology. Experience is a vital measurement of how people interact with what they are working with and how it interacts with them.  We are investing in technologies such as Metaverse, quantum computing, and applied innovation to design a future-ready workplace that delivers frictionless, consumer-grade experience for all. The global pandemic has taught us the importance of resilience and business agility. Therefore, it’s fundamental that we optimize the base and secure the workplace so that we can accelerate on a firm footing. The future workplace is responsive to human emotion, motivates employees to adopt technologies, is inclusive by nature, and foregrounds agility.

Looking to enable hybrid working in your organization? Visit our website to know more about connected employee experience offer or get in touch with our expert

Author

 Alan Connolly
Global Head of Digital Workplace Services, Cloud Infrastructure Services

What does it mean to be a customer-first brand to your consumers? It means making everything feel personal, from the products customers buy to the services they use – and from the way, products are designed to how employees speak to customers. Customer-first makes the pain feel painless, the complex seems simple, and every moment feels intuitively right. When customers feel valued, the brand feels valuable to them.

Knowing what your customers want is the key to successfully becoming a customer-first brand – and the answer is in the data. CMOs are leaning in heavily to maximize these important insights and, while it has never been particularly easy to predict what customers will think and want at any given moment, taking steps to closely evaluate historical patterns and trends makes these predictions far more accurate.

Personalization initiatives have been incorporated into marketing strategies for many years. However, consumers are now demanding nuanced, hyper-personal interactions from their favorite brands. Forced to play a never-ending game of catch-up based on the latest news cycles, restrictions, and publicly available health information, brands are still struggling to keep pace with changing consumer behaviors.

However, a deeper layer of personalization can solve this problem. Here are four emerging marketing trends that can help brands activate a customer-first strategy and deliver a more personalized experience.

Real-time analytics for evolving purchasing patterns

Consumers are disrupting marketing spending and strategies, and brands are responding with real-time analytics to monitor and align with their emerging behaviors. The latest technology platforms can determine automated next-best actions when real-time interactions take places, such as through two-way conversations on social media or other service channels. This level of sophistication requires individualization to optimize the customer journey – but it must be used carefully to ensure customers don’t push back. Marketers who take a thoughtful approach to real-time interactions will break through the noise and win loyal customers with perhaps the most useful benefit they can offer: relevance.

Micro-segmenting at scale

Newly gained advancements in data and marketing tools are enabling brands to embrace micro-segmenting – a trend that identifies very specific persona groups to convert into new customers by delivering hyper-targeted messaging and content at key moments in their shopping journeys. Data-gathering technology helps organizations see the results quickly and easily shift strategies if a certain type of content didn’t resonate with the audience. Social-listening tools are also being utilized to unlock behavior trends and measure brand sentiment. Micro-segmenting is still under-leveraged but is ripe with opportunities to drive conversions and build loyalty among customers who haven’t previously made purchases with a particular brand.

CMOs embracing data and storytelling

CMOs recognize that they must be increasingly nimble with their approaches. That includes how they leverage data to make decisions and support the business – being open to small experiments without abandoning traditional marketing tactics. This fine balance leads to a greater prioritization of knowing their customers by combining data and storytelling. Naturally, CMOs are looking to better understand the most effective approach to connect with customers – such as the right marketing channel, the best time for outreach, the products to promote, etc. But the best data still won’t make a strong impact if the insights aren’t integrated into an effective story or experience that resonates with customers.

Data-Driven Customer Experiences

Organizations need to have the right capabilities in place to merge experience data from devices and channels with enterprise data to activate 1-on-1 experiences. Previously, most organizations focused on collecting enterprise data and used it to build their analytics. But to create effective 1-on-1 experiences with customers, they need to collect, store, and process experience data – coming from the customer’s browsing pattern, individual preferences, and time spent. Harmonizing and orchestrating experiences with enterprise data becomes critical in helping to drive consistent experiences across the entire customer journey of content, product, services, and experiences. This allows AI models built with both experience data and enterprise data to enable experiences that make customers feel like a brand truly knows who they are and understands them.  Combine that with a commitment to data privacy and trust, and customer loyalty forms through a seamless experience that’s safe and reliable.

While the past two years have been an enormous challenge for CMOs, the increased inventory of consumer data has created opportunities for never-before-seen levels of personalization and focus, and a customer-first experience that’s being fine-tuned in real-time. Marketers have always been adaptable in the face of change but focusing on micro-segmentation, real-time analytics, and the combination of data-driven customer experiences and storytelling will put them in the best position to succeed in this ever-changing marketplace.

Jerry Kurtz

Expert in Advanced Analytics, BPO Solutions, Digital Transformation, ERP, Internet of Things (IoT), Supply Chain Management

I enjoy working with AI and analytics-driven programs. Delivering benefits at 10 times the cost is a gamechanger. I am happy to deliver results that exceed the expectations of our clients. I collaborate with our partners to solve highly complex business problems.
My team and I work to provide solutions in AI, advanced analytics, data science, machine learning, business intelligence, data transformation, master data management, and data governance. It is vital to keep up with emerging technologies. Only then are you able to take advantage of the new opportunities which emerge every day.
I also have experience in delivering big-data transformations to clients in the domains of internet of things (IoT) analytics, supply chain digitization, and marketing/front-office analytics. Some of my recent work includes:
Launching IoT analytics offerings globally for a Fortune 50 technology/services company
Supporting several IoT-related solution implementations for a number of clients in the manufacturing sector
Before joining Capgemini, I worked for several large and diverse organizations. My experience in management consulting spans 30 years. I spent six years in Business Services, another six years in enterprise resource planning, and two years working for consumer-packaged goods and retail industry leaders.
When I am not working, I enjoy spending time with my wife Amy, and our children, Emily and Adam. I am a big fan of golf and swimming, and I am the lead singer in a band. I also lived and worked for five years in Japan, and love experiencing new cultures.

We go about our everyday lives without fully understanding the systems that keep us safe and secure. That is because safety is vested in software. Gone are the days of knowing how your car works. Modern vehicles require around 100 million lines of software to make them work. Of course, most of the software runs the navigation and entertainment systems and heats the seats. However, some of that software is tasked with keeping us safe, such as making sure the braking and engine management systems are working.

But how can we know for sure that the software is doing its job? The short answer is, we can’t. So we have no choice but to trust the people who designed and built it.

Moving critical functionality from hardware into software is a well-trodden path as complex industries mature. Arguably, the aviation industry was the original pioneer with fly-by-wire systems as far back as the 1960s. It also drove early work in programming language design and international software quality standards.

A rocky road

Of course, the pace of change has accelerated in the last fifty years. Today’s critical software functionality that ensures your safety and security is not just in planes and cars. It is in medical devices, home automation, the electrical grid, gas meters, etc.

The transition to trusted software has not always been smooth. Consider these three examples:

• Between 2010 and 2014, over 700 UK postal service employees were prosecuted, and some went to jail for fraud because the new computer system was adding up the number incorrectly
• Toyota’s well-documented US safety issues with its car braking system in 2014 resulted in a $1.2 billion criminal penalty
• In 2018, the UK National Health Service reported that computer software kills between 100 to 900 people a year in the UK alone

The fact that software plays a crucial role in keeping us safe is not disputed. Yet cases like these give the perception that software cannot be written without bugs. It’s just too hard to make it work all the time. Consumers still get told to “turn it off and on again” to clear the problem. And for companies with software problems, this excuse can be the screen they hide behind.

But it’s a myth. A false perception that needs correcting. For many reasons, the software industry has not led the way in correcting this false perception. Now, as can be seen from the examples above, law courts around the world are challenging best practices in software production.

The way forward

It has been argued that a solution to this problem is to make all source code “open” and thus available for independent scrutiny. Consider the Heartbleed bug in the security library used for online internet transactions. Despite being used by millions of people, the bug went undetected in open software. This shows us that just because something is open, it’s not necessarily bug-free. Software quality is an orthogonal topic to software ownership, visibility, or business model.

The good news is that we know how to use engineering discipline and rigor to write correct software. Software that you can rely on to keep you secure and alive. When automotive companies say it is inevitable that autonomous cars will have many software faults, I point to air travel. Air traffic control, autopilot, automatic landing systems, etc., perform their jobs daily with minimal fuss because the software does exactly what it was designed and written to do.

The Capgemini Engineering approach

Capgemini Engineering has over 35 years of experience building software systems for demanding can’t-fail environments across industries as diverse as air traffic control, aircraft avionics, defense, railways signaling and train control, nuclear and renewable power generation, and banking and finance. The common theme across these sectors is that whatever the function, and whoever the end-user, the software must work first-time every time.

Cross-pollination of good ideas from one industry to another is an integral part of the solution, with aviation leading the way. Using the right processes and tools is equally important. It is essential to train and empower staff to understand the implications of the software they write and their responsibilities.

Into the future

As manufacturers realize the technical complexity of their products has moved from hardware to software, they will need to step up their game. But, for industries with little regulation, consumers will drive accountability through the courts.

Rather than all industries starting from ground zero, manufacturers can look to the aviation industry for guidance. No other industry has the depth and breadth to produce high-quality software. We should not be shy about our achievements. We know that applying engineering discipline to software development produces a reliable product.

The hard reality today is that software is responsible for our safety. So, we need to make sure we build the software correctly. The aerospace industry has a lot of mature processes, tools, and culture that it can share with sectors that are new to the challenge of producing safety-related software.

Capgemini Engineering is proud of its track record in taking these tools and processes from aerospace and adapting them to work in other industries.

Author: Neil White, Director, High Integrity Software, Capgemini Engineering

Neil has over 25 years of experience building software systems for environments that cannot tolerate failure. He has worked in industries as diverse as aviation, power generation, railway infrastructure, defense, and banking security.

A Q&A with Marc de Forsanz, Global Head of Customer First, Insights & Data; Padmashree Shagrithaya, Global Head, Analytics & Data Science; and Naresh Khanduri, Vice President, Digital Customer Experience at Capgemini

A happy customer is a repeat customer – and a repeat customer results in higher revenues, larger profits, and lower costs for customer acquisition and retention. Not only this, but a happy customer also feels safe about transacting with the organization. Such customers deliver greater lifetime value to the enterprise. Every business knows this, but improving customer satisfaction, being relevant/meaningful, aligning to customer values and appealing to their beliefs is an ever-challenging goal.

Marc de Forsanz, Padmashree Shagrithaya, and Naresh Khanduri, are responsible for Capgemini Data-driven CX– an AI-powered offering that helps companies deliver next-level customer experiences. They discuss the need for Chief Data Officers to help organizations manage the customer journey seamlessly and in real-time across a range of channels, and how Data-driven CX builds on the work that many companies have already undertaken with customer-data platforms.

What’s the elevator pitch for Data-driven CX?

de Forsanz: Data-Driven CX enables clients to take full advantage of related customer data (transactional, behavioral, product) from all channels to impact customer acquisition, retention and advocacy (in real-time). In short, it’s an AI-augmented customer-data ecosystem. Customers interact with brands through a variety of channels – including marketing, ecommerce, customer service centers, and in-store visits. Data-driven CX aims to enhance the experience of customers across all channels through data harmonization and activation through AI&ML. Specifically for the Chief Data Officer, it ensures the data is high quality so there’s trust in the data, and that the data is collected, stored, and used in compliance with all relevant privacy laws and regulations.

How much of an issue is this, really?

de Forsanz: Customer data is both strategic for a company (scheduled end of third-party cookie, first party data) and complex to manage (multiple data sources, data quality, PII topics, legal regulations). Whether the subject is initially marketing or broader into areas like E-Commerce, Customer Service, It is essential to partner with a firm who is versed in turning data strategy into a competitive advantage. Capgemini’s own research has shown there’s plenty of room for improvement. To highlight a couple of challenges, 57 percent of marketers we asked admitted they’re missing important data points required to obtain a full view of their customers. And only 45 percent of firms believe they have the data they need to understand the connection between online points of contact and in-store behavior.

How is Data-driven CX different from a customer-data platform?

Shagrithaya: Our data-driven CX shapes the customer experience and drives analytics and engagement. It harmonizes the data about each customer. But it also makes that data contextual. Many of our clients have walked the path of building what’s known as a Customer 360 – but despite this, they find they’re not able to engage with the customer in a meaningful way. The approach, many a times, is to gather as much information about the customer, whether relevant or not and whether that’d be intruding into the privacy or not! Data-driven CX combines knowledge of the customer with the contextual information the company needs to deliver the most satisfying experience through responsible personalization. So, for example, if a customer visits a company’s ecommerce site because they have recently bought something, the context is “Why are they visiting?” Are they looking for a service, or for an add-on for the product, or for a different product entirely? Understanding their intent within context and then servicing that intent is important because it generates a positive outcome for the company, and provides a meaningful experience to the consumer.

How does AI help with this?

Khanduri: AI is all about understanding the customer’s intent. Behavior on the channel should be driven by the insights generated by the AI engine, not by some rule that somebody came up with because “it feels right.” Data-driven CX allows enterprises to examine each customer’s data (with their consent) and learn from it, and then let the AI help with the next interaction with that customer. It allows companies to anticipate their customer’s intent based on data, not gut feelings. The outcome is that the customer has a better experience – they believe that the organization truly understands their needs – and they become more loyal to the brand. That’s the ultimate goal – a loyal customer who advocates for the brand.

And that’s an outcome with many financial advantages for a company…

Khanduri: Exactly. From the perspective of engaging with customers, the biggest investment a company makes is the cost to acquire that customer in the first place. Companies invest in traditional and online advertising, marketing campaigns, search-engine optimization, and so on – all to encourage that customer to make their first visit to a channel. And today, if the chosen channel does not deliver a personalized, unique experience, the company can lose that customer – often, for good. The bottom line is, it’s more cost effective to serve a customer you already have than to acquire a new one – and Data-driven CX is designed to help companies build that loyalty.

Shagrithaya: It can also reduce the total cost of operation for the enterprise. Some companies have implemented a customer-data platform but they do not think through the underlying questions of how to treat data of customers coming in from multiple channels in a consistent and coordinated manner, or how to activate the same through appropriate AI/ML algorithms. By not addressing these questions up front, they would have made the whole process more expensive than it should be. Data-driven CX helps bring those costs under control by providing the CDO with the tools to properly manage all aspects of their company’s data.

How does a company get started on this journey?

de Forsanz: Data Scientists, data analysts and business users need reliable customer related data to build analytics, AI use cases and an optimal Omnichannel experience. The beginning of all stories are the use cases which will drastically increase the performance of a department or the whole enterprise when this vision is shared. I encourage CDOs to identify their pain points with customer data right now. They should identify which departments are using customer data, how they’re using it, what they would like to do with it, and whether they’re achieving those goals.

Khanduri: We try to determine the use cases they want to activate – but also encourage them to adopt a holistic picture of their needs. They can start with a single business case – but at the same time, they should be planning to support other potential cases. If they’re trying to improve a single use case but not planning for others, they will not be able to solve all of their business challenges. For example, if they’re trying to convert sales leads into sales, they should also be looking at how they will improve after-sales service for those new customers.

As they look to enhance their customer experiences with a solution such as Data-driven CX, what can CDOs do to maximize their success?

Khanduri: One thing that has always surprised me is that few people actually ask, “Do I have the right data?” They’ve collected whatever data is available and they’ve created a customer profile with it, but they haven’t actually figured out if it’s the data they need to achieve their business objectives. I encourage CDOs to have that conversation with those who use data in their company – to examine the data they have in the context of their business objectives and the experiences they want to deliver to ensure they are collecting the information they need.

Shagrithaya: CDOs sometimes look at what they’ve already done and wonder why it’s not working for them. They’ll point out to me that they’ve invested in CRM, they’ve invested in Customer 360, they have a customer-data platform – and they were told this was all going to help their sales team – but they’re still not able to move the needle with respect to their KPIs, for example, of increasing the lifetime value of their customers. That’s a common starting place when we’ve had conversations with potential clients. We typically start with an architecture review to understand the existing landscape and propose appropriate end state architecture, with the existing investments in mind so that our clients need not shelve their current investments altogether to move to the new platform.

de Forsanz: That’s one of the things Data-driven CX does really well. It builds a perfect asset for the CDO to manage customer data, so all departments in the enterprise can unlock the value in the company’s data.

Authors

Padmashree Shagrithya

In her diversified career spanning over 25 years has crafted and led multiple large and complex transformation programs delivering strong business outcomes for many clients, leveraging Data, Technology, Machine Learning and Artificial Intelligence.

Marc de FORSANZ

Marc has solid knowledge in digital (website development, CRO, UX/UI design, brand content, traffic generation (Google SEO certification, Openclassrooms SEA certification, bloggers outreach). He also is also well versed in data science and machine learning capabilities.

Naresh Khanduri

In his current role as Data-Driven CX Lead, he helps clients maximize, and scale business value across CX channels. He specializes in combining Experience data with Enterprise data and applying advanced analytics, artificial intelligence to build immersive experiences.