Emerging technologies
Emerging technologies are the tools from different disciplines that are bringing about a great revolution in our societies in general, and also profound changes in the business world. They are changing the way we communicate, manufacture our products, operate in the market, enjoy our leisure time, take care of our health and the environment, and so on and so forth.
They constitute an ever-growing family, and it is not possible to study them all in the present course. We have therefore selected the most relevant ones. We will talk about:
All of them are here to stay and each has the potential to make our lives and businesses easier and more productive. At the same time, it is essential that control mechanisms are in place to ensure responsible use of all of them.
In any case, they are already having a major impact on the world economy and this impact will undoubtedly increase enormously in the coming years.
At some points in the content of the following sections, you are probably wondering how this new technology could affect my business?
Keep in mind that technology is evolving at an enormous speed. It is possible that the applications we can talk about at the time of writing will increase rapidly and very soon there will be new technological solutions that better suit your interests.
For this reason we recommend that, when thinking about your possible relationship with the technologies that we will explain, you make the effort to imagine possible applications of current technologies to your case, but also future scenarios in which technology will be more evolved. It is not easy, but the technological applications of the future are being built day by day.
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Who knows? You may find that your company can easily adopt one of the technologies we are going to look at. Or maybe your company will discover a new way to implement an emerging technology, and bring to market a process, a product, or a service that is more efficient, more profitable, safer, or more respectful of humans or the environment.
Technologies combine with each other to deliver their full potential
The history of mankind is inevitably linked to the history of the different technologies it has created and combined over time. From the control of fire and stone tools, we have arrived at the electronic device you are using right now. Its operation combines different technologies that were already developed, some of them many years ago: electricity, plastic materials, semiconductor materials, displays of one kind or another, sensors, integrated circuits, batteries, programming languages, and so on.
In the same way, new technologies combine with each other like a jigsaw puzzle. The different combinations are already producing disruptive changes, and in the future they will give rise to new technological developments, new tools capable of carrying out new functions that we can barely imagine today and that will progressively extend and deepen.
Source: https://www.youtube.com/watch?v=LskKkNQDe2E
Artificial Intelligence is already present in many processes carried out by our electronic devices, from our smartphones to the robots in our industries.
Indeed, it is at the heart of the transformation we call the Fourth Industrial Revolution or Industry 4.0, but it is also present in many other sectors, and performs a myriad of tasks in administration, logistics, accounting, human resources, and many others. For all of these it is already working in combination with other technologies.
Artificial Intelligence is a generic technology. It is not possible to say what its specific purpose is, but it is applied in very different ways for very different purposes. It is like asking why plastic was invented. Its inventor, Leo Baekeland, wanted to find an electrical insulator, but he himself was aware that the importance his invention would have in the future would not be limited to that. He was not wrong. The applications of plastic have been innumerable, and have certainly gone far beyond electrical insulation. Similarly, Artificial Intelligence does not have a single purpose, but is a tool with a multitude of possible applications, and we now have the opportunity to find ways to use this tool in the most productive ways possible.
In the following sections we will see which are the main emerging technologies that are bursting into our society and our companies, and how they are related to and enhanced by Artificial Intelligence. We will undoubtedly have to adapt to adopt them and make the most of them. They represent a great opportunity for companies that are able to do so.
Big data
Almost without realising it, we generate an enormous amount of data every day, and we do so in many different ways. Data floods our jobs, our leisure and our lives in general.
On the one hand, we are the ones who produce them, whether we are aware of it or not. Can you calculate how much data you produce every day: emails, social media posts, document creation, web browsing, online shopping, biometric records, image recording, etc., etc.?
On the other hand, they are produced by our devices, without any human intervention: electricity meters or any other supply, GPS devices, surveillance cameras, industrial machinery, patient monitoring in hospitals... some devices communicate with others, to which they transmit the data they collect. The internet of things, which we will also talk about, is part of this huge data-generating machinery.
The countless actions that we and our devices perform every day represent an immense source of data, and one that is growing every year.
In fact, we are used to hearing about amounts of data expressed in kilobytes, megabytes, gigabytes and even terabytes. These are the units of measurement that are currently in the range of common usage by citizens. However, with the progressive growth in the volume of data, we will become accustomed to units such as petabyte, exabyte or even zettabyte, which correspond to the higher orders of magnitude.
How can we deal with such huge amounts of data in a way that is useful to us? The answer is big data.
The term big data refers to the new technology that enables massive data analysis, storage, processing and use of the information obtained through these technologies.
To use big data, companies need to use programming languages, new tools and technologies that allow them to manage these large amounts of data. Some examples are Python, R Language, Hadoop, MongoDB, Apache Spark, Apache Storm, or Elasticsearch.
The main objective of big data is to obtain value from data, useful information, knowledge that allows the best decisions to be made and to maximise returns. In other words, thanks to it, companies can innovate.
Source: https://www.youtube.com/watch?v=Bn9jfoQ5PIw
In the traditional business world, companies innovated by replacing labour with machines, and later by replacing those machines with more efficient and more functional ones. It was the machines that brought innovation to the company, and the information followed, in the form of knowledge about their operation, being in the hands of people.
Nowadays, information technology is advancing faster than physical technologies, so that it is the latter that determines innovation in the first place. Thus, the enormous amount of information that any company handles can be processed, using big data techniques, to introduce innovations throughout the entire value chain of companies. Thus, big data tools by themselves, or in combination with other technologies, can facilitate innovation in the form of:
● Cost reduction at any point in the value chain: Procurement, internal or external logistics, operations, marketing and sales, after-sales services, human resources management, infrastructure or technology.
● Differentiation: Proper data management makes it possible to improve customer service and tailor products to the customer, with immediate differentiation effects against the competition.
● Transforming the geographic scope of work: Data management also increases the company's ability to coordinate activities at regional, national and global levels. It allows taking advantage of a larger geographic scope to compete in a larger market.
● Creation of new lines of business, or transformation of existing ones: the management and exploitation of data makes newly created activities viable, creates demand for new products indirectly, and also new businesses within existing ones.
Big data & Artificial Intelligence & Business
Big data and Artificial Intelligence make a formidable team.
Artificial Intelligence needs to be "fed" with large amounts of data to advance its functioning, while big data allows the large amount of data it manages to be converted into useful information for decision-making.
To understand it better, think about how you yourself have learned. When you were born you didn't know how to speak or read, you didn't know numbers. Through your senses you received a great deal of information, images with the shape of letters, sounds with the vocalisations corresponding to your language. You made mistakes and with help you corrected them and learned from them. Then, on the basis of what you had already learned, you could learn new things and make new decisions.
Bridging the gap, big data and Artificial Intelligence work in a similar way. The area of Artificial Intelligence that consists of learning techniques based on data is called Machine Learning, and thanks to them and the "training" data we can design and train models that help us to analyse situations and make decisions in an infinite number of environments.
Of course, companies can also use these technologies to improve their performance in many different ways. Examples of the combined use of big data and Machine Learning in SMEs include:
? Product development
What is the best product or service you can produce for a given type of customer? The best option may be to have predictive models that allow you to find the best combination of the key aspects, based on the products already known, and even on the new characteristics that you want to evaluate and incorporate.
Instead of launching products for the general public and trying to get it right by trial and error, it is about selecting a target audience and creating a specific product for them.
Thanks to different algorithms and mathematical patterns, experts in big data analytics are able to take the vast amount of data that consumers and brands continuously produce, to create products and services that are as close as possible to what their target audience wants to receive, even if they don't know it yet.
Have you watched House of Cards, Narcos or Stranger Things? It's possible that Netflix used data from your and your family's viewing habits to create them. The consumption habits and tastes of millions of people were analysed to design them using massive data analysis tools.
Of course it is necessary to have appropriate data sources and to monitor them permanently, to be able to make a data analysis that results in a realistic planning for product development, and to be agile enough in its development. Competition is unforgiving.
? Improving customer knowledge
Big data makes it possible to study the massive information that comes from the entire value chain of a product in relation to its consumer. Website visits, online behavioural parameters, social media, call logs, incident logs, logistics logs and many other data sources. All of this can be analysed to refine the user interaction experience, to optimise the value offered and to deliver exactly what the user is looking for. All of this helps to win, and also to build customer loyalty.
Amazon is the world's leading e-commerce company. If you go to their website, it is very likely that they will offer you items that interest you. They have analysed you as a user, your shopping habits, your interests, the trends that may exist in your environment, the behaviour of other users similar to you, etc.
The multinational bases much of its success on knowing in advance what its customers need, but you don't have to be a big company to make use of these technologies.
Through Machine Learning, any company can improve its online marketing performance. Each business generates its own data and this data can be enhanced by cross-referencing it with other data from external sources, whether public or private.
By evaluating big data from the market, customers and competitors, A.I. can help evaluate the best headlines, select the best campaign delivery times, adjust personalisations, automate content, etc.
Even a small shop could predict what kind of people are going to pass in front of its premises, their socio-demographic and socio-economic profile. It could then adapt its offer, modify opening hours, plan demands, etc.
Knowing who buys your products, their age, ethnicity, location, etc., will allow you to know who, how, when and where to target your marketing strategy. You will know the message that will keep a customer coming back.
? Preventing cybercrime
As technology evolves, so does cybercrime and online fraud.
And you know that you don't have to be a big company to be concerned about cybercrime. Anyone can be a victim.
Big data and machine learning, among other tools, are used to combat it.
The combination of the two allows large volumes of data to be analysed to identify patterns that may imply fraud. This way it is detected when something different happens with financial data, for example, and those responsible are alerted.
Another type of fraud is the massive use of bots that artificially increase the number of visits to websites, profiles, etc. By analysing big data, it is possible to detect abnormal performance and prevent cybercrime in companies in different sectors.
? Automate production processes
New machine learning techniques make it possible to create algorithms capable of carrying out complex processes while minimising human intervention. Instead of being programmed with sequential instructions, the software is trained thanks to the supply of a large amount of data.
Logically, big data techniques have to be used to feed the software. Then the software being trained analyses all this data, producing results in the form of probability vectors and this allows it to learn. Once trained, the software will be able to automatically carry out the task for which it has been trained.
As you can imagine, the possibilities are virtually endless, but they require a supply of good quality, large amounts of data. The 'experiences' through which machines learn depend on the data they receive, and the quantity and quality of this data determines how much they can learn.
Source: https://cdn.pixabay.com/photo/2018/12/06/10/45/algorithm-3859539__340.jpg
Personnel selection systems
These are models trained to facilitate selection processes, which have several advantages. To begin with, they are faster, as they analyse large amounts of data in a short time, according to the profiles sought. In addition, the selection and recruitment processes are fairer, and reduce the possibility of biases caused by personal preferences or prejudices. They are also cheaper, as they avoid unnecessary management costs and the wrong selection of unsuitable people.
? Automating customer service
It is not always necessary to have more staff to serve customers better.
A chatbot trained by Machine Learning, can attend, solve problems and answer customer questions quickly and at any time. It does not need to have all the answers. If it solves half of them and is able to direct the other half to the most appropriate contacts to meet the user's needs, it will already have gained significant efficiency.
? Making decisions in various processes
For example, in industry. Stopping machinery is always a problem, but it is more of a problem if it is due to a breakdown than if it is due to a maintenance stop. We can train an A.I. with historical data on the operation of a machine to make predictions about possible breakdowns, or to inform us of the need for maintenance.
We can even have a digital twin of a machine, a factory or even a city. Thanks to it, we can experience possible situations before they occur, to plan modifications, or to foresee problematic situations.
In the case of a machine, for example, if we want to know its response when we force it to operate at a higher speed than usual, it is not necessary to do so with the real machine. We can force its digital twin so that we do not run the risk of a breakdown.
In the case of a city, we can plan how a traffic gridlock will affect pollution, how to respond to a given emergency situation, or what prevention measures to apply depending on the weather.
Source: https://www.youtube.com/watch?v=iVS-AuSjpOQ
? Analysing process efficiency
Does your company have a complex supply chain and do you have historical data on its operation? An A.I. can help you improve warehouse management, make smarter shipments, make inventory improvements, monitor human error, create better or safer procedures, etc.
? Automate administrative tasks
For example, there are already applications capable of handling at least 40% of the tasks of a law firm. The tasks of collecting, analysing information and making decisions are greatly reduced.
Similarly with certain accounting tasks, or with project management, to ensure that they are completed on time and within budget.
Source: https://www.youtube.com/watch?v=lb6-QgsRUAM&list=PLNop3ICbZ4AWpD86l0TA8vzdoSyAGnyUp&index=2