Cloud-infused scalability into business

In the last two decades, rapid inventions in the telecommunications industry resulted in increased speed, bandwidth, and penetration. This has led to the internet becoming a bridge between customers and businesses, disrupting traditional models and increasing the efficiency of the survivors. Digital Inclusion is all about connecting billions of people globally through the internet. Software intelligence and telecom inventions led to a profound effect called the ‘Asset-light Model’. From the 90s, organizations have been planning to exploit globalization and achieve topline growth. In scaling up, firms face serious bottlenecks in buying and managing the assets.

Spotting efficiency issues in the cloud model

From there on, organizations started leasing or outsourcing assets, so the entire capex burden transformed into periodic variable costs. This solved the problem of scale-up and asset upgradation. In the technology industry, ‘Cloud computing’ is a classic case of ‘Asset-light Model’. I expect an increase in IT spend on cloud infrastructure from $419m in 2019 to $909m in 2023, CAGR of 21%. Cloud was possible with a wider telecom bandwidth as huge amounts of data need to traverse between multiple communication nodes globally. However, there is an inherent problem with cloud – companies need to accept the additional bandwidth costs. Though the network speed and coverage increased drastically, there is some latency built into the system owing to cloud storage.

Introducing cutting ‘edge’ computing technology

To overcome the operational difficulties of cloud computing, the processing or storage capability needs to be moved from centralized to decentralized topology, leading to the rise of Internet of Things (IoT). As a few million devices are going to get added to the internet ecosystem, I expect data processing at origination becoming a new normal. The distributed computing and storage at edges of the network is called ‘Edge computing’, where data collection and computation happens close to the origination place and time in a decentralized manner. Tesla deployed edge computing in its cars – Machine learning algorithms run at the central cloud, passing outcomes to entire fleet; Cars equipped with processing capability will make decisions with the live data feed.

Edge computing leads to cost optimization– a two-pronged advantage. Processing capability shall be trimmed and data flow traverses in multiple directions, decreasing bandwidth needs. The inherent latency in the cloud, while responding to customers, is reduced and the distributed storage capacity reduces the risks of concentrated cyberthreats at scale.

What do marketers gain from ‘edge computing’?

Edge computing extends the usability of data to unimaginable dimensions. By fragmenting the analytics process, the central cloud shall hold the core algorithms to process one-level preprocessed information from the edge networks. The first processing happens at the level of consumer devices or aggregation of devices. Rather than pipelining the crude data collected from customers, the network passes on usable information to the central cloud. In the absence of decentralized edge processing, the cloud would pick up only fragmented customer data leading to loss of key insights from real-time data. For instance, when a banking customer initiates payment for a biller and if the payment process breaks down, the customer can log into the application again and continue the payment from a quick link retrieving the data. This highlights two important outcomes:

1. Customer engagement is initiated right at data generation
2. Personalized content curation is managed locally

From an operational perspective, tracking the customer journey across multiple channels is tedious. By locating edges across the network, journey details can be stored and retrieved from the edges on time, without losing any information. For customer experience (CX), the ulterior goal is to get customers hooked live in the brand’s digital ecosystem. With edge computing, data processing centers get closer to the customers, resulting in faster and engaging CX. If a wealth management client searches for private assets in the mobile app, edge processes this information and passes it to another edge, which can connect this client to the right wealth advisor in seconds, along with sharing the client’s profile with the advisor. Marketing teams will get relieved from operational aspects of data management. For marketers, edge computing helps in discovering the true sense of real-time data analytics. Businesses will identify data-based outcomes that resonate well with customers.

Going forward…

From a marketing perspective, edge computing creates value for businesses through personalization, contextualization, and timeliness, thereby scoring well on CX and reducing customer attrition rate. Edge computing is a coordinated effort. For instance, AT&T partnered with cloud enterprises, helping businesses to adopt edge computing along with 5G networks. The benefits maximize when the device clusters, co-located close to the data origination, start sharing information to optimize decision making. Edge computing is a potential disruptor and will gain momentum when IoTs proliferate at higher velocity.

Gunjan Aggarwal

Expert in Analytics, Artificial Intelligence, Data Science

Gunjan is a head of Artificial Intelligence and Marketing Science where she advises businesses on how to build business cases and successfully manage AI and customer value analytics projects. Gunjan is a firm believer in data-based decision making and applying automation to improve customer experience. She helps clients design their customer data platforms with a customer-centric approach in order to bring business value to customer experience, marketing science, innovation, operational effectiveness and compliance.
She spends time each month doing pro-bono work to support women in the STEM field.

From the pandemic to political uncertainties, today’s business environment is unprecedented, and companies are under increasing pressure to cut costs while increasing agility. This has made cloud adoption and serverless architectures more important than ever. The promise of the serverless paradigm includes high availability, easy scalability, no need to manage infrastructure, and the ability to pay only for services they require and only as they consume them. Taken together, these benefits result in organizations that are better able to respond to changing business scenarios – whether that’s seizing new opportunities or mitigating risks.

There are many choices to be made when developing a serverless solution. Not only is there a functional and non-functional range of requirements, but there are several cloud services. For example, in addition to Lambda, AWS  offers more than 50 serverless services, plus more than 175 other cloud services – and that’s just one vendor.

The benefits of a decision framework

When I speak with decision makers, I’m often asked how they should decide which serverless services to use to build the business solutions they need and when to use them. Our recommended approach is to create a decision framework. A properly crafted decision framework is meant to help organizations take the right approach to migrating applications to the cloud. When done well, it:

• Recognizes that one size does not fit all. A decision framework will help identify which cloud-based solution is right for the needs at hand to help organizations go beyond Platform-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS) solutions and embrace the benefits of serverless. For example, while single page applications will be a good fit for serverless if all actions and events are handled via API, a NodeJS-backed application may be the best fit on a PaaS platform.
• Drives architecture maturity. Serverless is the best-in-class architecture for cloud-based solutions. A decision framework helps organizations uncover opportunities to embrace serverless that they may not have recognized, thereby driving organization-wide maturity.
• Enables a forward-looking architecture. Serverless allows organizations to quickly build out new capabilities using solution building blocks.  Developers can focus on building out functional capabilities while the cloud provider handles everything else.

Key considerations for a serverless decision framework

A decision framework helps an organization address key issues, including an organization’s:

• Functional requirements, which dictate the structure and composition of the technology solution, including user experience applications, the business layer, and analytics.
• Non-functional requirements, which help an organization drive differentiation in the market and inorganic growth. They include usability, availability, scalability, accessibility, maintainability, and fault tolerance.
• Technical constraints for the technology used such as platform, processor, memory, and storage requirements.

A comprehensive decision framework should go beyond just the application layer of a solution, that is from application to data analytics layers, as per the below.

A serverless decision framework in action

By taking the above requirements into consideration, a decision framework helps organizations determine when to use serverless. It also helps organizations determine the best approach when serverless requirements are not met. The below diagram illustrates how to determine whether or not serverless is the right fit as well as which alternatives to consider.

Capgemini has built an exhaustive decision framework covering all the layers of an IT solution to help organizations decide the right mix of AWS serverless cloud services and build a serverless solution that will allow them to succeed in today’s business environment and be ready for the future.

Please contact me for more information about serverless or learn more about our experience, case studies, and thought leadership in this area.

Author

Prafull Surana

Prafull is a technology leader, an architect, and a seasoned technologist with 20 years of IT experience.

Key takeaways

• External and hybrid sources of innovation will become increasingly important – by 2025 startups will rank as a top innovation source for companies with great potential for sustainability.
• Venture client units are a breakthrough corporate venturing vehicle, which if operated with a specialized venture client process and with dedicated resources, enables corporations to benefit strategically from top startups.
• For corporates, the main advantage of a venture client unit is that it enables the entire organization to gain measurable competitive advantage from more and better startups – at lower risk and without any capital requirements than possible through traditional corporate venture capital programs.
• For startups, the main advantages are to quickly gain high-profile reference clients whose expert user feedback is critical to iterate and improve their products as well as through an increased valuation that results from increased traction and revenues – all without additional dilution.

Working towards a sustainable future

In today’s fast-moving world, the imperative to stay relevant forces companies to continuously reinvent themselves. New competitors transform from promising startups to unicorns in lightning speed, while rapidly shifting customer expectations and political regulations and global trends determine the market of tomorrow. Moreover, the ongoing global COVID-19 pandemic is changing our world massively, requiring companies to make the right bets today in order to secure a sustainable future.

Startups will become chief sources of innovation by 2025 – particularly focused on sustainability.

According to a joint study from MIT and Capgemini from 2020, external sources of innovation are becoming more and more important for companies. Over the next five years, startups will rank as top innovation sources for companies. Moreover, hybrid forms such as innovation labs are especially important as an interface between internal and external innovation. This is critical for companies that do not have adequate innovation capability internally and are therefore forced to source externally. As a result of this development, traditional R&D and internal business units’ employees will become less relevant as innovation sources within the next five years (see Figure 1).

Sustainability tops the agenda of many customers and therefore of corporates. Corporates often rely on startups, which are particularly good at accelerating (digital) innovation in the area of sustainability. According to a European-wide survey by TechFounders, sustainability is a priority for around 90% of startups. Moreover, a new study found that, even though climate-tech startup investments by VCs and corporates are still low compared to overall investments, the growth rate is five times higher. For corporates, startups’ innovation potential poses a great chance to support their sustainability initiatives and targets. However, with startups gaining importance and corporates heavily focusing on sustainability, this still leaves the question “How.” How can corporations gain strategic benefits from the world’s best startups, quickly and at measurable risk?

A new model of corporate venturing has emerged.

The concept of the venture client model is new, yet simple. Instead of acquiring non-controlling equity stake, the company buys the startup product (see Figure 2). The corporate becomes, hence, a venture client rather than a venture investor with the objective to harness a strategic benefit. The strategic benefit emerges from applying the startup product to improve an existing or create a new product/service, process, business model, or even entire business. The startup solution is applied in a real business environment immediately, without being incubated or accelerated.

Obviously, to identify top startups and to buy and transfer their solutions so that they generate a measurable positive impact is highly complex. Hence, just being a venture client is not enough. The corporate needs to implement a sophisticated model, i.e., organizational venture client capabilities. Such a venture client model was first established at BMW by Gregor Gimmy (see Harvard Business Review) in 2015 with the BMW Startup Garage. Over the years, many global corporations have been following suit, including Bosch, BSH Home Appliances, LafargeHolcim, German insurer Signal Iduna. Beyond Germany, other corporations have adopted the Venture Client approach, such as the Italian energy company Enel and Spanish Telefónica.

The venture client proactively solves problems

Here is an example of how a good venture client unit operates: A business unit or functional unit raises a request for support from the venture client unit to resolve a complex challenge via a startup, as it cannot solve the problem with internal or incumbent external solution providers. These challenges can be manifold (e.g., growth, efficiency) and may emerge anywhere in the company (e.g., R&D, IT, manufacturing, or logistics). The problem is strategic, as it impacts the competitiveness of the company, such as a sustainability challenge to measure CO2 emissions in real time caused by intra-factory logistics.

The venture client unit is engaged by the venture client (the factory logistics manager) to enable the whole process from identifying to adopting the best startups, like an HR department enables recruiting.

The process starts with an in-depth analysis of the challenge, to make sure that it is strategic and that startups may indeed have suitable solutions. If this is the case, the venture client unit sources for startups with relevant solutions. In our example, the venture client unit would source for startups with sensor technology, hardware and software, that detects CO2 levels inside a factory. If the problem has relevance in the startup ecosystem, as is probably the case in sustainability, the team will likely identify over 100 startups.

The venture client unit team then filters the best startups from that list. This results in five to 20 startups that are then analyzed in depth. This assessment, led by the venture client unit, involves the venture client, for example an R&D engineer or logistics manager. Once the best startup has been selected by the venture client, the company buys a small sample of the startup product. In our example, the company would buy a sample of sensors large enough to monitor CO2 levels in a small part of the factory.

The purchased products are applied in a real setting over the next two to four months. This is essential to generate real data to validate if the startup technology delivers the expected results. The data now serves to confirm if the startup solution meets the KPIs required for the last step: adoption, which is realized via partnership or M&A. The results from piloting the startup product in a real use case, are now key to enable the adoption decision.

In most cases, adoption will be a kind of partnership (e.g., licensing the startup technology). However, the venture client may also choose to acquire the whole startup, if control is a condition to generate and defend the intended competitive advantage. In addition to the just described active solving of known problems, the venture client unit also constantly looks out for strategically relevant startups to anticipate problems the corporation is unaware of. In case a top startup is detected, the venture client unit proactively contacts one or more business units that could potentially benefit from its solution. Once the relevance has been confirmed, the process as explained first, repeats itself.

The venture client model brings significant benefits for both corporates and startups (Figure 3).

Our view on innovation and the venture client model.

At Capgemini Invent, we believe that startups are a key, but not the only solution. A state-of-the-art venture client unit with high-quality specialized processes and dedicated resources will make a big difference in enabling companies to benefit strategically from the best startups. To ultimately succeed however, most companies will have to continuously scrutinize, transform, and adopt their corporate innovation systems as such. As shown in Figure 4, our innovation operating model involves three tiers: purpose, approach, and tactics. Each tier’s components serve as gears in the innovation machine, and every one of them is an important piece of the puzzle. The venture client model has the potential to significantly influence several of these components and serve as catalyst to the whole innovation system. Our innovation experts at Capgemini Invent are more than happy to support you in establishing your venture client unit and skills. Feel free to get in touch.

Thanks to the co-authors Manuel Wiener and Phillip Schneider.

Authors

Jens Hofmeister Head of Central Region Fahrenheit 212 Part of Capgemini Invent	Olivier Herve Vice President – Innovation and Strategy Capgemini Invent	Kevin Loeffelbein Director – Smart Mobility & Business Model Innovation Capgemini Invent

One of my friends recently drew my attention to an article in Time magazine, in which the International Olympic Committee (IOC) President Thomas Bach has said that COVID-19 vaccinations could be required for athletes and fans to attend the postponed Tokyo Olympics. This is set against a backdrop where vaccines to inoculate against COVID-19 are being developed and (at the time of this writing) set to be given to the public. To limit the spread of the disease at an event vast numbers of people are expected to attend, drastic measures are being considered to not risk another massive increase in cases worldwide.

Given that there are several global events planned for 2021 and assuming that vaccination passports provide a solution, how could they be implemented appropriately?  What regulations should be complied with to protect personal information and reduce the likelihood of the infringement of human rights?

There are many questions to be answered, some of which focus on the governance of personal data.

Reasoning

IATA recently announced that it was creating a digital platform to facilitate the sharing of vaccination information called the IATA Travel Pass. The reasoning for this is: “to re-open borders without quarantine and restart aviation governments need to be confident that they are effectively mitigating the risk of importing COVID-19. This means having accurate information on passengers’ COVID-19 health status.”

It seems prudent that a collective definition of why the data is being gathered across the world should be adopted. If the reason is simply to present proof of having had a vaccination, that in itself is quite different from requiring presentable proof of immunity. Such a requirement should, at the minimum, include a follow-up test to prove that the individual has produced the required protective antibodies.

Compliance

The concept of data sovereignty means that personal information (including health data) is usually governed by regulations that afford some protection to the citizens of the region where the data is stored. Examples of this include:

• HIPAA (USA)
• PIPEDA (Canada)
• GDPR (EU)
• Data Privacy Act (Philippines)

However, how do you apply the principles of health data governance internationally? What standards should be used to protect the data? How should it be stored, and what should happen to it when it is no longer needed? The standard requirements of asset management and data governance must be observed when processing personal data, even in a global context.

Integrity

In order to have a trusted worldwide system that can prove that an individual has had a vaccination, it would seem logical that such a system should have traceability built in. This would imply that an assertion that an individual has had a vaccination can be traced back to a point in time where the injection was administered (and, potentially, which type of vaccination it was – especially given that different vaccines have different efficacy rates).

Administration

How should such a system be administered? Should it be on a country-by-country basis, given that each nation could claim ownership of said data and how it should be used? If the aviation industry (IATA) is setting up its own system, should this be a process that is extended to travel across land borders? How would such a system be applied consistently in different countries, with varying levels of social and technical infrastructure, so that travelers around the world have equal access to transport?

Conclusion

In the next 12 months, the world has an expectation (or hope) to return to business as usual, including international travel. That includes the following sporting events postponed from 2020:

• Football European Championships (Europe)
• Copa América (Argentina and Colombia)
• Ryder Cup (USA)
• Olympic Games (Japan)

If we are going to reduce the likelihood of a return to the levels of infection seen throughout 2020, a number of measures will have to be implemented. Ideally, these should enable equal access to travel, irrespective of the economic background of one’s country.  A vaccination passport may well be one of these, but to keep the pandemic in check it will require a truly collaborative approach to the governance of data that matches to that seen by the global medical community to make a real difference.

To learn about Capgemini Data Protection and GDPR Services, visit: https://www.capgemini.com/service/digital-services/gdpr-readiness/data-protection-gdpr/

References

http://www.bbc.com/travel/story/20200831-coronavirus-will-you-need-an-immunity-passport-to-travel

Terence Stamp

Expert in Identity and Access Management

I advise my clients on the implementation of Identity and Access Management solutions using a number of different technologies. I also architect and design these solutions performing integrations with both on-premise and cloud platforms.

Over the last few months, we have seen many sectors respond to the cross-infection risk of COVID-19. What can we learn from them? How can our new focus on emerging technologies such as vocal interfaces, facial recognition, and mobile-based applications support improved healthcare in a pandemic era?

Infection prevention and control is a vital aspect of healthcare. The use of good handwashing and appropriate personal protective equipment has always been paramount. But the focus was previously on preventing cross infection between patients or between carer and patient. We have never before had the same intense focus on transfer between carers.

In other sectors, the importance of the hierarchy of hazard controls is widely recognized – elimination, substitution, engineering, administrative controls, PPE. This was developed in the chemicals industry where it was a one-way flow, protecting the worker from a hazardous substance. In healthcare, it is more complex. The hazardous substance, for instance a virus, still exists but we need to protect individuals from the virus that is carried by people as well as physical objects.

In shops, we regularly see physical barriers; in restaurants, there are often screens between tables; in hospitals, there are often screens between patients – but I rarely see them between staff at workstations. Every time a person makes contact with a surface, there is the potential to deposit or pick up an infectious agent. A key concept in retail has been “contactless” – to me one of the biggest lifestyle changes is no longer carrying cash. A single patient will come in to contact with many staff and many objects touched by staff, can this be reduced? Devices can warn you of your proximity to others and can analyze the locations to assist redesign of one-way flow systems, used in manufacturing plants but not in hospitals.

Touchless delivery of supplies from storerooms to the patient could reduce human contact; autonomous trollies could carry drugs from pharmacy, linen for the laundry, food from the kitchens, directly to the patient’s room. Ordering these supplies can mirror online ordering with which we are so familiar, with minimal human contact.

Can we deliver touchless care? Since the time of Osler, medical assessment has depended on taking a history, examining, and then undertaking tests. Taking the history can be contactless (videoconsults and phone consults are now commonplace). But can the examination be replaced? Contactless vital sign observations are now possible, Bluetooth stethoscopes that can be held by the patient already exist, but can we replace palpation?   Many tests could be remote or robotic. Robots are already used for remote surgery but how long before we have bedside robots that can put up a drip, change your catheter, give you a wash?

Staff who have been with a patient then contact equipment that will be used by other staff. Can we copy the example of banking with contactless payment systems? For example, instead of a dirty keyboard each individual uses a personal device, or drug cupboard keys are replaced with smartphone proximity access.

Even the process of washing your hands can be made safer. Far more hotels and airports have contactless taps, toilets, hand dryers than I have seen in hospitals. I suspect short-term cost takes precedent over safety.

But there is one big problem. Healthcare is more personal than shopping. You feel better if you have human company; you recover better with personal conversations about your care;  you want to have conversations and physical contact with your family. A simple hand on the shoulder or holding a hand is a powerful clinical treatment. Could we not be smarter than just refusing people access to their relatives?

We know that the public support a move to touchless technologies. How do we best use technology to reduce cross infection without isolating the individual physically and mentally? This is one of big challenges of healthcare design in the era of the pandemic.

I would love to hear from you if you are thinking about how you can shift to contact lite healthcare and think we might be able to help you develop your ideas.

Matthew Cooke is Chief Clinical Officer at Capgemini. He spent most of his career working in the NHS as an emergency physician and was the National Clinical Director for Urgent and Emergency Care.

Matthew Cooke

Expert in Healthcare

Working at CapgeminUK to develop digitally enhanced healthcare supporting more care in less intensive environments, improved initial clinical assessment systems and improved patient flow through health and social care system.
My background is as an emergency physician who has also been the National Clinical Director for emergency care in the NHS England and is a Professor of clinical systems design.
I am bringing expertise from a clinician perspective to our healthcare projects, as well as an in depth understanding of health policy and how the NHS works.

Introduction & observations

In an ever-changing market cost/income ratio of financial institutions is under heavy scrutiny. Linked with the ever-increasing push to support end-customers in the right manner, at the right moment and through the right channel. The key is data; which is too often seen as a cost driver. Proper attention to data is often only paid after regulatory pressure or fines. Let’s see data as a value driver, not a cost driver.

Value?

Data needs to be valued as an enterprise asset. The challenge is that there are not many generally accepted ways to put value on data, which leads to some questions:

• What would it cost to rebuild your full data collection?
• What would it cost to buy your data on the market (if available at all)?
• How much more revenues could you generate if you have additional (quality) data?
• How much cost could you save if you would have perfect information at the right time?

The incremental revenues is the most interesting question to ask. It will help you think much more in the lines of value rather than cost.

Key examples of data as a value driver for the Financial Services industry are:

Risk management

At all financial institutions data challenges are imminent: KYC, CDD, GDPR, Basel and other key topics on the executive board agenda are driven or led by data. To address these challenges, and to comply with these regulations data needs to get in order and underpin value by data based, trustworthy evidence.

In this process financial institutions are already creating a lot of value, showcasing the right information, no double work, single sources of truth finally start to exist, and a single view of the customer will slowly be created. Resulting in value from data.

Cost reduction

Poor data quality can lead to additional cost, more waste, rework in business processes and undetected risks. Poor data quality in many cases is also an inhibitor for unleashing the potential of Artificial Intelligence.

In many situations some of the consequences of risk/cost-based approach lead to additional collateral damage. For example, the cost of resolving technical debt on data ecosystems is becoming increasingly high as risk avoidance keeps the system unaltered, resulting in a deteriorating competitive position. Moving to a data value centric approach can help turn this around.

Customer focus

Moving away from the risk driven, fine based approaches to programs like GDPR into a holistic overview and the opportunity to bring a better personalized and improved offer to individual customers. Making it a completely different business case; seeing where early movers in digital transformation are and will be focussing on the hyper personalization the GDPR programs could be a value driver with a positive focus.

This also applies to the current programs in KYC/CDD a defensive mode looking in solving the issues, showcasing that you are in control regarding who is in your books, who is your customer and caring that your customers are doing legal and compliant financial transactions. Whilst this also is a next step towards a pristine path to serve your customer even better.

And market agility

Market developments are increasing in speed and impact, disruptors are around the corner. Not in the last place from companies which are most data savvy: techfins. Although they might not have the in-depth financial services knowledge, they do have an abundance of data and the passion to drive value from it.

With a more value-based approach on data, financial services companies can increase the speed and quality of operations, supporting reduced time to market of new offerings and be at the forefront of (re)inventing financial services.

With innovations thriving on data

Outlining a number of topics that can only be done if you let go of the risk/cost-based approach of data and approach it from a (business) value perspective, foundational for the survival in the future.

• Hyper personalization: shifting from (generic) customer journey thinking to hyper personalized thinking, be at the heart/mind of your client.
• Embedded Banking/Insurance: financial services as a seamless part of other services, API based business models
• Open X: expanding business models, shifting from single products to full services/experiences
• License to Operate: regular demand for data (level of detail, frequency, coverage) will increase, want to do it ad-hoc every time or choose a flexible data ecosystem approach that is able to deal with any future demand swiftly and without friction?

Now what?

Change your mind set about data: it harbours huge business value, but harvesting this value is many times blocked by working in a cost/risk-based approach. Data is often referred to as the new oil or water, look at it differently:

• If data is the new oil, why not do additional refinement, creating much more valuable products for your clients from the raw material?
• If data is the new water, why not make sure that it is treated as the primary source of your organization’s life?

Choose a data valuation approach that fits your organizational culture, make the value of data tangible and widely acknowledged. Define initiatives to improve the value of data and use the incremental business value to reinvest in the next steps of the journey.

Additionally, ensuring your enterprise data liquifies, achieving a situation where it flows freely through the enterprise, without friction to the point where it is needed for decision making and help achieve ultimate stakeholder value.

Finally, expanding horizons by considering data as a value driver. The platform economy continues to be a big opportunity for Financial Services companies that embrace it, sharing data with platform peers, aggregating client value offering a tailored family of adjacent services.

So

No matter the angle data is a value driver, and it is time to acknowledge and manage it accordingly and harvest its yields!

Authors

Erwin Vorwerk Vice President – Insights & Data Capgemini	Vincent Fokke Chief Technology Officer (CTO) Capgemini FS Benelux

What is the most appropriate information technology to maintain and store the master asset system model? We have to acknowledge the need for a network operator to access different variants of such a model. To improve consistency, a clear master-slave model is recommended, and master-slave information flows should as much as possible follow the natural lifecycle flows of a network modification.

In an earlier paper, the GIS-centric Enterprise, we argued that geographic information system (GIS) software is a key component of the enterprise ICT infrastructure for network operators, specifically for its ability to manage topological relationships via a graphical interface. Let’s rephrase the arguments in the perspective of an asset system model lifecycle.

1. Strategic network planning requires insight in actual and projected asset system performance (power quality measurements, faults, incidents, outages) in relation to (projected) location of capacity demand or production sites and the asset system model itself. The underlying physical asset performance (condition measurements, systematic faults, repairs) may affect overall performance of the asset system and is part of the analytical model.
2. Asset system design conceives network extensions and enhancements based on a well-documented as-built network model and its internal or external constraints. Apart from the electrical or hydraulic characteristics, the system design is strongly geographically determined (rights of way, environmental and safety regulations, soil types, slopes, etc.). High level cost estimates can be derived from an initial asset system breakdown and balanced against initiative value on portfolio level. The investigation of alternative routes is a specific geographic analysis step included in many investment planning studies.
3. The validated asset system designs are engineered in detail as the construction projects are being defined and prepared for construction (bills of materials, compatible units, technology choices, detailed placement, etc.). Here, the projected network model must be translated to a precise topographic linear placement design for authorities to approve and contractors to execute.
4. Projects and construction actors plan their work based on the detailed engineering specifications and there is frequent exchange of geographical information between parties (engineering companies, civil contractors, government bodies). Co-ordination of construction work (often imposed by government) includes the exchange of information (work polygons; construction site location and timing of work) with other utilities operating on the public domain. Further optimization (in timing and location) is sought in shared trench work for multi-utility projects.
5. As construction proceeds, as built network records are documented to enable traceability (welding information on gas mains, equipment installed or replaced, configuration settings, initial pressure or voltage measurements) and linked to the functional segment of the asset system or to the right equipment. Commissioned network modifications are promoted from the “as-it-will-be-built” status (terminology taken from Network Model Manager Technical Market Requirements: The Transmission Perspective) and integrated in the operational network.
6. Network operations use an abstracted (schematic or geo-schematic) asset system view of the same network to take operational decisions (switching, planned outages, flushing).
7. Outage and incident management processes need insight on network connectivity to identify the origin of a problem as well as its impact (customer minutes lost). The field workers receive detailed location information to perform an intervention in a timely and safe way.
8. Results of patrolling and surveying activities have to be reported back and associated to the network including their locations and the equipment they are related to. Correctly located observations and measurements are essential to the performance monitoring of the system.
9. Customer service agents evaluating the feasibility of an access demand look at network characteristics in the vicinity of the premises to be connected and maintain the vital customer-network link.

By counting the number of occurrences of the terms location, placement, geographic, route, vicinity, etc. in this lifecycle overview, it is obvious that asset systems – at least for network operating companies – are and must be spatial.

Spatial solutions provide essential functionalities to manage networks:

• Mapping and visualization services because the map is a natural entry point to geographical information, and through a geographical representation, it can provide insight into complex information.
• Graphical tools as available in GIS support intelligent editing of a (versioned) network model, an interconnected structure where network facilities, equipment and customer connections are linked through cables, pipes, switches, and valves. Intelligent connectivity rules support the management of network structures according to business guidelines.
• And finally, there is the power of geoprocessing tools to relate and combine phenomena that have no specific relationship except that they are located close to one another. Spatial analysis and decision making based on “proximity” and inference is essential for utilities in critical processes such as strategic planning or risk management.

Alternative models derived from the master will remain necessary but in a clear master-slave model as depicted here below.

The transformation to “smart” and “digital” will therefore require fundamental renewals of enterprise GIS systems to provide a solution to the linear problem statement in a smart world. Large corporate initiatives will be required, far beyond the inevitable technology upgrades to systems that are in many cases 20+ years old.

“Wearables, Whereabouts, and Roundabouts” is the title of our next article that will add the event-dimension to asset management and asset system management. In other words: how do we locate event information on the network model in order to use it in network performance and risk management activities?

This blog is part of a series of 6 under the theme “Asset Systems for the Smart Grid”:

1. What is an asset system for a network company?
2. Smart grid or silly tube maps?
3. What’s so special about asset systems?
4. What’s so spatial about asset systems?
5. Wearables, whereabouts and roundabouts
6. What happens on the asset system, stays on the asset system

I have more than 20 years experience in GIS (Geographical Information Systems) and asset management projects in utilities (water, electric, gas), telecom and the public sector (transportation, cadastral services). You can connect with me here.

Jan Van de Steen

Expert in Energy, Utilities & Chemicals

Expert in GIS and linear asset management I advise network operators and other large infrastructure companies in setting up intelligent asset information management structures aligned with network lifecycle and asset lifecycle. On their journey from document-centric to data-centric digital asset management, I help clients articulate their integrated enterprise GIS (Geographic Information System) strategy. My GIS-centric view on Enterprise Asset Management (EAM) enables clients to increase asset lifetime while remaining compliant with regulations and managing risks. Apart from transformational advice, I also play a key role in successful GIS and asset management implementation programmes for large utilities and public infrastructure operators.