Working Methods and Digital Technology

Machine learning

Data structures Buzzword Machine Learning means an artificial system that acquires knowledge from experience. The system collects data and builds a model from algorithms. This allows the system not only to memorize the information but can also assess and classify previously unknown data. To explain it a little easier: A computer is constantly being fed with data. From this previous information, he can generate calculations and recognize repeatable decisions and events. In everyday life, we ​​often encounter machine learning. Recommendations from Amazon, Zalando, or Netflix are based on this system. You record previously viewed series or products and use them to calculate what might be of interest to the user. More and more companies are using this technology.make it more customer-centric.

Agile work

The word “agile” actually comes from the Latin and means something like “agile”. And that’s exactly what it’s about. Agile work describes the ability of a company or team to adapt to an ever-changing environment. The team grows and always changes according to the environment. It is important to always have a fixed goal in mind. Because even with the changes that digitization brings, the goal must not be lost sight of. If a team manages to adapt to new circumstances, such as a much more digital environment, and still pursue the same goal, it can lead to competitive advantages.

Crowdfunding

This is about getting projects, products, companies, real estate, or much more financed by a large number of people. A person turns to the public and asks them to fund money in the person’s project. What the donors get in return can always be different. Perhaps people can take a percentage stake in companies, get the money back with interest at some point, or much more. However, investors do not have to receive anything in return. It can also be simply investors who want to do something good or find the project interesting. There are no limits to the system here.

New work

Man with laptop at the garden table with coffee work means new ways of working in the new and digital age. The point here is that our previous work system is simply too outdated and that a more modern, flexible, and creative work model is required. These are also the core values ​​of New Work together with independence and freedom. Employees can and should always act more freely and can determine and decide much more themselves. New Work wants to differentiate itself from the established concept of work and to develop a new way of working. In 2020, more and more employees will go in a new direction and work as freelancers. This way of working is a good example of New Work. The freedom of the workers is much greater. Working hours, projects, and places of work can be freely decided. More and more people are taking this step and are moving into a new and more modern working world. This also means a change for companies, they will have to plan their employees more flexibly for projects in the future and work more with external specialists.

Customer centricity

Advertising does not always have to be aimed at the product. Customer centricity is about aligning a marketing concept with the customer. It is about the needs, interests, and desires of each customer. Digitization and networking with the customer make it easy to find out what the customer wants. A marketing strategy is then developed based on these factors. For example, the customer can provide the company with certain data, which they then work with and with which the appropriate marketing is developed for them. An attempt is made to create added value for the customer with marketing and not simply to advertise the customer.

Digital twin

This involves creating digital copies of real objects so that they can be tested for different purposes. For example, if a company wants to launch a new product, a digital twin is created beforehand. This means that the product is virtually replicated. The product can then be put through its paces in this simulation. In this way, weak points or susceptibility to errors can be discovered before the product is fully developed or produced. This is of course very practical in the production of technical products such as cars or various machines. Because if the object is produced in large quantities and errors are only noticed then it can become very expensive. To avoid such problems, the digital twins are created.

Edge computing

Edge computing is the exact opposite of cloud computing. Here, the data transfer does not work purely via a cloud or via the Internet. Edge computing, as the name suggests, is on the edge of a network. Computer applications are moved to the very edge of a network. The data is processed directly on the end device and does not have to be transferred to a cloud-first. One advantage of this is that it saves resources. There is a shorter transmission path, shorter transmission times, and lower costs. The quality of the transmissions also improves.

Social media

The social media area has grown incredibly strongly in recent years. Many companies only focus on marketing in this area. In this very diverse area, there are of course some terms that are not known to everyone. We are now listing four terms so that you are even more familiar with the area!

Omni-channel

Social media There are numerous platforms that can be used for customer contact and marketing. If not just one channel, but several channels are used directly, this is called “omnichannel management”. So it’s about connecting different sales channels and using them professionally. It’s about the interaction between channels, brands, and customers. But this doesn’t just have to include social platforms. TV, radio, and print products can also be included. The term is broad and affects all ways of getting in touch with customers. Since the customer places increasing value on quick and direct contact, this is of course very practical for the customer.

Influencer marketing

In order to explain this term, it should first be explained what influencers are. Influencers are people who influence people through social media. They are people who have a large number of followers, get in touch with them, inform them about various things, and can thus influence their opinion. Influencers are often hired by companies to advertise for them. They publish pictures, videos or texts on products, advertise them, and try to bring them closer to their followers. There are also influencers who only focus on one particular company and only advertise for them. However, it is normal for influencers to advertise different products and companies. Initially, a small market Influencer marketing has become an established marketing tool. However, influencers are now not only moving on their social media, but they are also increasingly being seen on television or even in the cinema. So if you want to market a new product and have the right change ready, you shouldn’t neglect the option of influencer marketing.

Retargeting

Users on the Internet show their interest in products by liking certain things or by following companies that offer this product. This information is used in retargeting. Because here, users are given advertising based on previous contacts with companies or products. For example, You often like pictures on Instagram with specific shoes. Then you will suddenly see advertisements where you can buy this shoe. Previous interests are used to personalize the advertisements for you.

Geotargeting

geo targetingGeotargeting is also about advertisements. However, this is not about previous interests, but about the location of the user. Appropriate advertisements for companies that are in the vicinity are created via the user’s IP address, which is used for the location.

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What Is Machine Learning

When we talk about the definition of Machine Learning we are referring to a term that in Spanish translates as “machine learning” and that, at the moment, is one of the focuses of interest in the development and application of Artificial Intelligence systems. Do you know what Artificial Intelligence is and how it is already present in our lives?

What is Machine Learning? According to Wikipedia, when we talk about machine learning we are referring to “a branch of artificial intelligence whose objective is to develop techniques that allow computers to learn. More specifically, it is about creating programs capable of generalizing behaviors from information provided in the form of examples ”.

Machines that learn and are capable of predicting behaviors, evaluating actions, and analyzing in seconds millions of data and responses that will determine subsequent actions. The development of Machine Learning is inextricably linked to that of Big Data and in this relationship, we can talk about:

Supervised learning (Supervised Machine Learning): With which we refer to the interpretation of Big Data, previously stored and classified (data mining or data mining). From here you can launch predictions about behaviors, trends, and reactions that will be very useful both from the point of view of the company and in other disciplines such as Education or Health.
Unsupervised Machine Learning: At this point, we go one step further in the use of Machine Learning and Big Data and we refer to the interpretation of millions of data and records that are not classified and for which, thanks to the development of algorithms and Artificial Intelligence systems, it is possible to make predictions with less human presence. The machine rules and we enter the field of Deep Learning or deep machine learning.

Advantages of Machine Learning for companies

The application of machine learning systems is one of the great pillars and challenges of the immediate future. There are already applications and processes that are being applied and developed and one of the points of interest is focused on the business world.
Greater knowledge of the needs, tastes, and buying habits of customers. Machine Learning is already essential to go one step further in improving the customer experience and increasing customer loyalty.
Improvement of the relationship and communication with the client. Artificial Intelligence systems such as chatbots promote 24-hour attention seven days a week. The data they obtain is essential for improving customer service, as well as for getting to know the customer better.
Prediction of trends and needs from the interpretation of millions of data. There are already many companies that optimize their production cycles based on the information that is analyzed and interpreted through Machine Learning systems.
Promotion and development of new products and services based on all these data. Machine Learning encourages innovation and the search for new solutions that, in most cases, are based on the correct interpretation of the data received.
Ecommerce development thanks to the analysis of customer behavior. Automation and development of suggested services or products based on the interpreted data that lead to a much more personalized experience.
The prediction capacity of Machine Learning systems will be essential for the cybersecurity of companies, as well as the computer threats that Artificial Intelligence already uses for cyber attacks. From the point of view of internal operations, the advantages of Machine Learning for the company means having the right data for better decision-making, as well as for the automation of processes that until now used human and material resources.
Improvement and optimization of the selection processes. There are already many companies that are betting on Artificial Intelligence and Machine Learning systems in their human resources departments.
A competitive advantage over the competition and its products. The data provided by Machine Learning take into account the value of having information and uses by the competition. Obtaining data is not only useful from the customer’s point of view, but also in the field of obtaining a more accurate and accurate analysis of the markets.
Optimization of the logistics systems and processes of the company that, in the same way, affect the customer experience and favor an improvement in the organization of production processes that, thanks to Machine Learning, have a solid database with the one to justify decision making.

Trends and platforms that already use Machine Learning

What is Machine Learning being used for today and what are its uses and challenges for the immediate future? The development of machine learning is already widespread in companies from the point of view of the analysis and interpretation of customer behavior, a trend that will continue throughout the coming years.

In the same way, process automation and decision-making supported by the interpretation of these consumption guidelines will be one of the bases for the production and development of Industry 4.0, which will optimize its processes and speed up its response to orders.

Sectors as important as Tourism, e-commerce, or marketing already rely on Machine Learning solutions and systems to optimize their results and reach their clients much more precisely and personally.
In the same way, the question of how to store and manage the enormous amounts of data finds an answer in platforms such as Google Cloud Machine Learning.
They are just some examples that make clear how the future of digital transformation for companies necessarily passes through the application of Artificial Intelligence systems and Machine Learning. 

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Today, frauds are increasingly elaborate. If we restrict ourselves only to the online environment, we can find increasingly elaborate actions such as phishing, mainly, which offers hooks that are increasingly faithful to the original they want to impersonate. The staging is increasingly convincing, more complex, and therefore more difficult to detect with the naked eye.

Fraud against financial institutions is something very serious. Of course, fraud through fraudulent emails is also serious, but in the case of large companies, a scam can mean millions of euros in losses.

Attackers increasingly use more sophisticated systems to find vulnerabilities in the financial “value chain”, using advanced Machine Learning algorithms to find the weakest points where attacks will begin. Criminals use all the computing power at their fingertips to go for the weakest link and from there jump to other links in the chain.

To understand why machine learning is crucial to fraud management, we need to understand how fraud can be “parameterized”:

• There are too many unique cases of fraud to go after them all and to have concrete solutions for all of them.
• Fraud patterns change rapidly, so any non-anticipatory action will be out of date in no time.
• We are talking about people dedicated entirely to studying susceptible systems to specifically compromise the security of the weakest points.
• A (good) scam will mimic the behaviors of legitimate customers, so if we have overly strict or intrusive countermeasures, our best customers may be penalized without reason.

Machine Learning solves all these problems. To get started:

• Minimizes the need to manually review existing vulnerabilities, as well as helping to find new weaknesses.
• You can learn to ‘devise’ new forms of attack by looking at actual attacks by cybercriminals – something similar to what we described when we explained the concept of Honeypot.
• Improves human decision-making, providing greater precision.
• You can reduce false positives through behavior analysis.

Faced with the growing threat of new forms of fraud, many of them supported by machine learning to find new vulnerabilities, the way is to bet on machine learning itself to defend itself, not only from these attacks but also from its speed and amazing capacity. adaptation and evolution.

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