Ineco is recognised as a leading engineering firm in the transport sector with highly specialised and highly valued human resources, and in recent years, it has developed a set of innovative initiatives in the IT area, including new digital services for its customers (smart cities, IoT, artificial intelligence, blockchain, virtual/augmented reality, etc.). This process is now to be given a decisive boost with the launch of its Digital Transformation Plan 2018-2020, an initiative that will be applied not only to services provided to customers, but also to the company’s own operations.

Digital transformation makes it possible to unify Ineco’s vision and enable it to move forward, with everyone interconnected, moving in the same direction, making use of the company’s intangible assets and taking advantage of digital tools. In this new stage, information no longer belongs to a single area and becomes part of the entire company.

Technological convergence

Today, the modernisation of companies in the field of transport and government is inextricably linked to the intensive use of new technologies. Technologies such as cloud computing, big data and artificial intelligence are fundamental elements for addressing the current challenges. They also have a mutual multiplier effect, making it possible, for example, to have big data and artificial intelligence cloud computing applications, which would not be possible otherwise.

The use of cloud services in transport management systems, gives companies the potential for lower costs, greater agility and flexibility, improved response to unpredictable events and changes in customer behaviour, reduced risk levels, availability of globally accessible services and easier and faster implementation.

The use of artificial intelligence and big data in the transport sector makes it possible to identify trends, verify phenomena and predict behaviours. With these tools, decision-making will be easier, faster and, above all, more efficient, meaning that this data becomes an element of great value. In fact, this kind of analysis is already underway and is being used by public authorities in areas that affect passenger transport in cities and on the roads. For example, sensors that count the number of vehicles passing a certain point are already being installed on traffic lights to optimise changing times and improve flow.

This enormous amount of data for analysis provides numerous advantages such as enabling better planning and management, reducing environmental impact and optimising the performance of vehicles and drivers. Artificial intelligence is also used in most areas of the transport sector, especially in autonomous driving. In this field, which has been widely developed in the aviation sector, autonomous road vehicles and smart drones are now making their presence felt.

New generations of technologies and mobile devices help improve efficiency and reduce the costs of passenger transport companies, while users can enjoy faster, safer and more reliable journeys that they can plan, manage and easily pay for from their phones. And both agents and users benefit from the potential of analysing the large volumes of real-time data generated as a result of these transactions.

In the case of employees, the use of mobile technologies enables business processes to be transformed and carried out from any location. Employees will have access to all of the information and tools they need to perform their work on the mobile device of their choice, thereby improving productivity and customer relationships.

The Internet of things (IoT), the basis of an environment in which people and objects are interconnected, has the potential to radically transform the transport sector, creating new services with high levels of intelligence. This is the case with air transport and the optimisation of routes with the consequent reduction in travelling times and increased safety; simplification of procedures; and availability to passengers of self-service and seamless processes, both at airports and on the aircraft themselves.

Another major field of application for the IoT is vehicles and driving. Taking cars as an example, the current starting point is the connected car, in which both the driver and car have a high degree of connectivity with the outside and services necessary for journeys are available. The growing adoption of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, together with the development of smart cities and smart roads, will positively influence driving, thanks to direct communication between cars and interaction with traffic lights, signs and even the mobile devices of pedestrians.

This will result is transport systems in which all elements of the environment can communicate and cooperate to create safer and more efficient driving within the framework of smart mobility. The next step is the autonomous car and the concept of car as a service (CaaS): no longer is it necessary to buy a car to travel from one place to another, it can be available as a service only when needed. In this environment, interaction with customers through social media and the collaborative economy provide many advantages, among them, brand loyalty and positioning.

Agile methodologies enable working methods to adapt to the circumstances of a project at any time. The two most widely used today by developers are Scrum and Kanban, the first of which is characterised by its incremental development strategy, with projects coded in increments through iterations called sprints. Conversely, Kanban is a more visual methodology, where tasks, defined according to established rules, progress through different phases (to do, in progress, done, etc.).

In the current IT environment, the term DevOps is one of the most frequently mentioned. Its concept can be defined as a software-creation methodology based on integration between software developers and system administrators. DevOps makes it possible to manufacture software more quickly, with higher quality, lower cost and very frequent releases. Another advantage is the automation of basic hardware and software management through a specialised kind of scripting language, reducing administration costs and generating more homogeneous and efficient systems. DevOps is not a culture in itself, but it does require major cultural and organisational change. A cultural change towards collaboration, communication and, ultimately, complete integration between the old (usually stagnant) development and systems areas.

New approaches to new architectures based on microservices and software containers also generate benefits. Unlike the traditional approach in which everything is created in a single piece, microservices are separate and work together to carry out the same tasks. Each of these components, or processes, are microservices whose ultimate goal is to deliver high-quality software faster.

As for software containers, these are packages of elements that allow a particular application to run in isolation from the operating system that supports it. If software is developed and needs to be moved from a server installed in a data centre to a virtual machine running on a public cloud, the code may not work well in its new environment. However, if this software is inside a container, it can be transferred to the system that best suits it.

Blockchain is a technological paradigm whose application is being explored by all sectors due to its ability to radically change current business models. Blockchain is precisely the technology that enables the functioning of Bitcoin, the world’s main cryptocurrency and clear blockchain success story, but it is a paradigm that can be used in other very different areas.

Blockchain technology allows collaboration, guaranteeing the security of transactions with a high level of transparency. A blockchain network is a collection of computers, called nodes, connected to each other using a common protocol for the purpose of validating and storing the same information in a P2P (peer-to-peer) network. This information is interpreted as a common ledger, hence the acronym DLT (distributed ledger technology) associated with this type of architecture. The ledger records all transactions between nodes that have occurred since the creation of the aforementioned blockchain network. It provides an incorruptible technology whose processes are secure, error and intermediary-free and fast.

The use of virtual/augmented reality in transport is becoming increasingly significant. Among the main advantages is the creation of immersive virtual environments that allow you to move freely through interactive simulations; physical teleworking, through the use of haptic devices and automated systems; the commercialisation of products and services without the need to have a physical environment for sales; and its great utility for supporting education and training.

5G networks, the new paradigm

5G technology addresses the next (fifth) generation of data transmission for mobile networks, constituting not only a new wireless communications paradigm, but also an essential technological component in digital transformation in the most advanced countries over the next decade.

The main solutions that are driving this digital transformation, the Internet of Things and Big Data, robotics, virtual reality and ultra high definition, will be supported by 5G technology. For its introduction to be successful, it is necessary not only for infrastructures and telecommunications networks to evolve, but also for an entire ecosystem of platforms, services and 5G content to be developed.

5G is expected to reach technological and commercial maturity in the 2020 time horizon with a large increase of traffic on mobile networks, and massively expanding the number of interconnected devices (the number of interconnected devices is expected to increase from 15 billion in 2015 to more than 75 billion in 2025). New technology will play a fundamental role when supporting the vast amount of data that is expected to be handled on the network. It will also significantly reduce file download times. In specific terms, 5G networks will provide very fast, high-capacity mobile broadband with speeds in excess of 100 Mbit/s with peaks of 1 Gbit/s, which will enable, for example, ultra high definition content or virtual reality experiences to be offered. It will provide ultra reliable communications, with low latency of around 1 millisecond (ms) compared to 20/30 ms typical of 4G networks. This could make them appropriate for applications that have specific requirements in this area, such as connected or autonomous vehicles, telemedicine services, real-time security and control systems and others, such as smart manufacturing. They will also make mass machine-to-machine (M2M) communications possible. The capacity to manage a large number of simultaneous connections will be increased, which will allow, among other things, the mass deployment of sensors, the Internet of things (IoT) and the growth of big data services.

Any user connected to the Internet through any device is a potential target for a cyberattack, hence the vital importance of cybersecurity, a discipline in constant evolution which focuses on offering the best protection to systems in the face of a changing landscape of threats in which attackers have been professionalising in recent years and now boast significant infrastructures and organisations that can jeopardise the security of almost any institution or company.

Plan objectives

Digital transformation is an extremely powerful lever of change and innovation for companies. It is not, however, an ultimate objective in and of itself; it needs to be a catalyst that enables the achievement of the goals derived from the aspects analysed above:

1. Total digitisation of processes: ensuring that all of the organisation’s processes are managed digitally, thereby improving efficiency, sustainability and relations with customers.
2. Improved competitiveness based on the intelligent management of data: transforming the analysis and exploitation of the company’s data to improve decision-making and make management smarter.
3. Strengthened collaboration and communication between areas of the organisation: promoting as much as possible collaboration and teamwork among Ineco’s staff to take advantage of all knowledge that exists in the company.
4. Comprehensive digital commercial management: comprehensive management of customer relations, from the generation of opportunities to the execution and closure of projects, involving all Ineco staff who participate in every phase of identifying new business opportunities. Creation of new digital channels for developing relations with customers (e.g. social selling).
5. Permeability of technology at all levels: taking advantage of new technologies to continue contributing value to current products and services, as well as enabling the generation of new solutions to support the growth of the future business.
6. Facilitation of digital transformation: piloting new ideas to generate a disruptive transformation.

Lines of action

Finally, to achieve the established objectives, work needs to be carried out on various lines of action, which, in this plan, have been classified into six different areas:

• Digital transformation lab. This is a laboratory for testing all kinds of digital transformation ideas, especially those that are disruptive, applying the fail fast paradigm of ‘fail fast’. In other words, quickly discarding any initiatives that are considered to have failed and scaling up those that work. It is also about internally disseminating innovative technologies that have the potential to transform the organisation: Big data, artificial intelligence, RPA, etc.
• Fast execution. This area includes actions and initiatives that aim to improve the execution speed of the company’s processes and productivity of employees, with the resultant increase in efficiency at all levels. Actions in this category can be considered from two perspectives: the user and systems. From the perspective of the user, actions aimed at improving application usability and user experience are incorporated, resulting in improved agility and greater speed in the adoption of new tools thanks to a reduced learning curve. From the perspective of systems, improvements are sought in the ‘raw’ speed of applications and agility in their development and modification thanks to changes in system architecture, less customisation/use of out-of-the-box solutions, reduced number of tools through the merging of functionalities and the ‘mobilisation’ of processes in smartphones.
• Cybersecurity. Cybersecurity initiatives are aimed at safeguarding the security of information and systems. The actions included here involve considering the implementation of guidelines, actions, training, best practices and technologies that can be used to protect the assets of the organisation and users within the computing environment in which they work. The approach focuses on system users at all times, seeking to ensure levels of cybersecurity with the least possible intrusiveness in the daily work of people and understanding that an excellent level of digital security can be achieved through the use of the latest technologies (AI, passive monitoring, etc.) without harming productivity.
• Paperless. The ultimate goal of the initiatives in this area is to totally eliminate paper from the organisation’s processes in order to gain major benefits such as improving efficiency, increasing sustainability, facilitating network working and analysing and optimising processes.
• Data-driven company. Data-driven companies are organisations that are characterised by taking advantage of and exploiting data generated by their daily activities, with the ultimate goal of improving their value proposition, processes and decision-making. To achieve this, the data must be properly incorporated in a digital format, transversally available, ‘unique’ and high quality. The initiatives are therefore aimed at identifying non-digitised data with value for the organisation, including it in tools and solutions from which it is easily accessible, creating new data channels and exploiting all of the available information in an advanced manner through BI/business discovery, AI or similar techniques.
• Co-creation/collaboration. This line includes actions focused on promoting agility, collaboration and co-creation options among the different areas of the company. The underlying objective is to maximise the productivity and utilisation of the knowledge of all employees, taking advantage of multidisciplinary capabilities and promoting teamwork, regardless of the geographical or organisational location of each employee.

The growth projection by 2020 is almost 40ZB (zettabyte, 10²¹ bytes), the majority generated by human beings, followed by physical devices connected to the Internet. Another indicator that allows us to verify this trend is that the Big Data analytics and technology market grows at an annual rate of 20-30%, with an estimated world market of 50 billion euros by 2018.

But it is not simply the amount of data that makes the concept of Big Data unique. We tend to take this concept literally and associate it with a large amount of information, but, as we will see later on, a set of data must have more qualities in order to be considered Big Data.

DEFINITION OF BIG DATA AND ASSOCIATED PROBLEMS

We can talk about Big Data when large amounts of information are generated (Volume) very quickly (Velocity), with heterogeneous types of data (Variety). Recently, the industry has started to add a fourth ‘V’ to these three classic features (the three V’s): Veracity. Given that a large portion of information is directly generated by people, it is necessary that the origin of the data be granted the quality of veracity. There is no point in having a full set of data that is not reliable.

To a great extent, the rise in Big Data technologies has been caused by the social networks, as far as the volume and variety of data are concerned, and by the marketing sector, with regard to the possibilities of demonstrating the value of all the information being generated. Banking is another classic sector that generates and exploits Big Data. The study of the information on uses and habits that can be obtained from banking information makes it possible to design products tailored to customers, or to predict behaviours, such as outstanding payments, according to the correlation of the information available. Engineering firms are also beginning to identify cases of use for which the capacity of Big Data analytics is a competitive advantage.

Finally, the field of the IoT (Internet of Things) and Smart Cities should be noted.
The concept of a Smart City involves an intensive use of information technologies for collecting and processing the information that the city generates using the sensors deployed or other data sources, such as traffic cameras or any other source of unstructured information.

The four qualities that information must have in order to identify with
the concept of big data are: volume, velocity, variety and veracity

THE INDUSTRY’S APPROACH

Big Data projects cannot be efficiently addressed using traditional technologies. The requirements for storing and exploiting such quantities of data, with their qualities of velocity and heterogeneity, have forced the industry to design new technologies that make it possible to work with information in real time, including the previously mentioned characteristics of data volume and variety.

Among the different paradigms presented by the industry when tackling Big Data projects, we can highlight In-Memory (IMDB) technologies and Distributed Systems. In-Memory technology allows all of the information that is necessary to work to be loaded into a memory where the processing is much faster. Furthermore, solutions based on distributed systems are oriented towards parallel processing, allowing a complex problem to be broken down and sorted out by using different machines responsible for solving each part of the original problem. This breakdown allows for the use of affordable computers which together make up a large processing platform. The appearance of Open Source solutions such as Hadoop and Storm has supported this trend.

Additionally, there is a tendency to implement Big Data platforms using cloud services. The problem raised in Big Data projects is infrastructure dimensioning and scalability (growth potential). For this reason, these sorts of projects need to have an infrastructure that is elastic and which allows available resources to be expanded or reduced depending on our requirements at any given moment.

Solutions based on cloud services are going to take the place of private infrastructure contracting (on-premise), as this allows companies to be free from infrastructure installation and maintenance, in order to focus on tasks which contribute value to the project. We are no longer talking about acquiring machines (virtual or physical) where we have installed and configured our own solution, but rather about utilising the services we need at any given time, paying only for the processing time and the storage. For instance, if we need an automatic learning service where we can define a prediction algorithm that works with our own information, contracting the cloud service and only paying for the period of use is sufficient.

WHAT BIG DATA IS HIDING

Once we have this vast amount of data, how do we generate value from our information? There is a misconception that Big Data projects involve storing the existing information and applying a relatively complex technology to analyse what we can obtain. A Big Data project should begin prior to starting to compile information. It is necessary to be sure about the objectives that motivate the project and the type of information we need, as well as to consider all of the constraints involved in the collection and processing of this information.

As opposed to Big Data technology, classic Business Intelligence systems are based on the consolidation of the information which lets us carry out operations with that pre-calculated data. The new Big Data paradigm forces us, on one hand, to be able to analyse the flow of information in real time, and, on the other, to store the raw information. With regard to temperature sensors, for example, we need to record all measurements that the sensor has generated. It is not enough to simply control the average daily temperature, since having the additional information does not allow us to analyse details to be able to predict parameter behaviour or identify behaviour patterns. That is to say that we need to be able to store and analyse the information in its original form, or at a much lower level of detail than in traditional analytical systems.

BIG DATA IN ENGINEERING

The areas of application are far-reaching, ranging from solutions for Smart Cities to automatic learning techniques for predictive maintenance activities. At Ineco we are aware of the importance and the possibilities Big Data technologies have in the field of engineering. Therefore, the Information Technologies division studies and exploits the characteristics of Big Data in different areas. In terms of Smart Cities we work in different fields, among which we can highlight the Smart CityNECO platform, for the integration of information from the various city services (mobility, environment, etc.) allowing for a correct management based on the control panels of the different services provided by the city. In addition, also within the field of Smart Cities, but more specifically concerning the axis of mobility (Smart Mobility), Ineco works in the study and optimisation of mobility in cities by creating prediction and simulation environments in real time that allow the optimal mobility regulation parameters in the different areas of the city to be determined. This solution is based on integrating the simulation models, as well as on the automatic learning techniques, by working with the information concerning the city’s state of mobility in real time.

A big data project must be sure about the objectives and the type of information we need, as well as consider the constraints involved in the collection and processing of this information

Within the field of infrastructure maintenance, predictive maintenance is based on anticipating the problem before it becomes a reality, or before its state loses the optimal conditions. This way, we lengthen the time between maintenance activities, thus improving availability while saving on costs. In this field, we develop predictive techniques using measurements from different parameters thanks to sensors which allow a relationship with their service life to be established. The difference with traditional techniques lies in automatically combining all information regarding their state, characteristics, exploitation and environmental conditions.

Within the area of mobility surveys and capacity, Ineco works on a mobile device survey platform that allows all information relevant to these types of studies to be compiled, including the responses provided by the user, location information provided by the GPS, etc. Additionally, with regard to the answers given using natural speech, we can conduct what is called a ‘Sentiment Analysis’ (opinion mining) which lets us identify the speaker’s attitude towards an issue.

Furthermore, we cannot forget that Big Data does not only consider alphanumeric information. Thus, another area of research focuses on image processing. The objective is to locate defects or objects in an automated way.

To sum up, we are undergoing a digital transformation which, combined with interconnection capacities, is exponentially increasing the amount of information generated. We live in the ‘Time of Data’ and the capacity to analyse that information is going to mark the difference in all fields of business.