Since 2010, Supply Value has conducted an annual survey on the most important trends within the purchasing field. Since this year we have been adding the IT trends research. The aim of this research is to support Information management and technology professionals in setting the most important priorities and the right focus. In this blog we look back at the top 3; Dates science, Cyber and data security and Artificial intelligence.

Do you want to read more about these and the other trends, and do you want to know what the IT professionals think about these trends? Download belowto this page the entire research report!

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1. Data Science

Information systems, networks, databases, chips, computers, social media, websites, Big Data, Internet or things: we now work in an environment that is digitizing faster and faster. Data plays an important role in this, but organizations are struggling with the growth in the amount and variety of data. To be able to and manage this data and to derive valuable insights from it, data science originate. In 2012, the Harvard Business Review labeled the data scientist all to The Sexiest Job or the 21st century and in the Netherlands there is a real Jheronimus Academy of Data science rigged. This rise of data science also appears from the IT trendsresearch performed by Supply Value. More than 50% of respondents calculate data science to the three main trends. Moreover, more than half of the respondents indicate that they will give much to very high priority to data in the coming year science. This makes the future of data seem scientist to be rosy.

What is Data science? 

Dates science unites scientific methods, processes and systems to translate both structured and unstructured data into knowledge and insights. From the broad domains of mathematics, statistics, information science and computer science, data science use of techniques and theories. With the help of this, the data goes through scientist different phases to analyze and understand phenomena: 

1. The understand of the business and the problem to be solved. 
2. The collect of data from different sources using data mining. 
3. The clean up and preparing the raw data for processing. 
4. The explore of the cleaned data to understand patterns and bias. 
5. The develop from measurable functions in front of the problem that is being analysed.  
6. The to model of the data with which insights are generated for the problem.  
7. The visualize of the data in a simple yet effective and visually pleasing way. 
8. The understand of the business to complete the cycle again. 

Trend development 

Before dates science has become what it is today, it has gone through a number of phases.The started with a in need of business intelligence, in which the production and consumption of data together came. At the end of the 20ste century entered the term Big Data the world stage. New powerful tools made it possible to extend the existing internal data with external data, such as social media. It has also become possible toet predictive analytics recognize trends and predict future trends. 

Subsequently, these analysisschth methods integrated into products and services, enabling dates science originated. prational every organization in every industry kan make use of possibilities of data science as better search algorithms, recommendations, suggestions and targeted ads. The development of data science doesn't stop there, but continues automation from analytical methods. Of artificial intelligence, machine learning and deep learning can those methods further be trained to automate and self-sufficient at turn into. Because dates gthvaryan becomes, be still more complex training methods are needed for these automatic analyses.

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2. Cyber and Data Security

In 2017, an international container company was hit by an attack with ransomware, after which the company had to install more than 45,000 PCs and 4,000 new servers reinstall, which cost about $300 million in profit. This shows the major and even disruptive consequences that cyber attacks can have. Digital threats are constantly lurking, while society, economy, individuals and organizations have become completely dependent on digital resources. The scale and seriousness of digital threats are significant and continue to develop, according to the National Cyber Security Center (NCSC). In addition to the threat of cyber attacks, the risks surrounding data protection are also growing. Due to changing legislation – the General Data Protection Regulation (GDPR) – and increasing privacy concerns serve organizations even better prevent data leaks and maintain control over their data. This research shows that there is a lot of awareness for the protection of digital assets and data. Nearly 75% of respondents are reasonably to very familiar with this topic or even consider themselves an expert in cyber and data security. Respondents also give high priority to cyber and data security in 2019. More than 45% give it high priority this year and more than a quarter of respondents indicate that they give high priority to data and information system security. 

What is Cyber and Data Security? 

Cyber and data security are related topics, but they do not mean the same thing. Cybersecurity encompasses the protection of IT systems against theft or damage to hardware, software or electronic data. It also consists of protecting against disruption or misuse of the services provided by IT systems. On the one hand, a 'hard' attack can take place, including by exploiting a backdoor in a system or algorithm, where the attacker makes use of a design or configuration flaw to evade security. On the other hand, attackers target the 'soft' side of IT users, for example with phishing and other forms of social engineering. Protection against such attacks is becoming increasingly important due to the increasing number and reliance on IT systems. The trend of increasing attention to cybersecurity is further enhanced by the rise of smartphones, smart TVs and other applications from the Internet or things. IT professionals try to arm themselves against cyber-attacks with measures that eliminate or prevent the threat, weakness or attack by limiting or warning the damage. Like the attacks, these measures focus on the hard and soft actions, procedures and techniques. The way in which one responds to a security incident is also crucial but challenging. Using Hackers proxies, temporary accounts, and other means of staying anonymous, making them difficult to trace. In addition, - by means of the large amount of attacks that often also take place automatically - not the source behind every attack prosecuted. 

Data security focuses on protecting digital data against destruction or unwanted actions, for example in the event of a data breach. In addition, it prevents unauthorized users from breaking into data. Such data loss can be caused by, for example, computer viruses and technical defects in data carriers, but also by human errors such as the loss of a hard disk. To prevent this, data security provides technical measures.  

1. Eencryption technology to data on a disk to encrypt. 
2. Bbackup of data to dates to recover in case of loss or damage. 
3. Dates masking to hide specific data, as personal data.  
4. data wiping to overwrites all existing data on a diskijven and to destroy completely.  

In addition to these technical measures, organizational measures are also necessary, partly because of the human factor. Even though his data technical well protected, carelessness in human handling of technology can lead to major data leaks. Organizational measures for protecting sensitive data includes:: 

• Security Policy in which simple or extensive working methods, standards, rules and guidelinestlines are described on topics such as remote access and password management. 

• Risk management that assess and prevent or reduce risks associated with sensitive data. 
• Awareness and training with the aim of creating a work culture where everyone in the organization knows what is expected and how to comply with rules and guidelines. 

Trend development  

The magnitude and severity of digital threats have become significant, but will continue to evolve, according to the NCSC. The digital threat remains permanent, sabomination and disruption by other states is becoming an increasing threat and cybercrime from professional criminals continues. At the same time, by no means all organizations take the necessary basic measures to repel cyber attacks. In addition, digital resilience is coming under further pressure due to the increasing complexity and connectivity of the IT landscape, including cloud services and smart devices. devices. pPotential vulnerabilities continue to grow due to digitization, the disappearance of analog alternatives and the increasing volumes of digital data. Also shall States are making even more effort, adopting more complex methods or applying them on a larger scale. As a result, cyber and data security remain necessary for the functioning of society. 

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3. Artificial Intelligence

'Alexa/Okay Google, what's the weather? Sunny, with 21 degrees Celsius. OK, and what about Thursday? It will be cloudy with a 60% chance of rain and 18 degrees.'. Talkback speakers that provide weather forecasts (even consecutively without the word 'weather' appears in it), answer questions and turn lights on or off based on a vote from one of the residents. A voice message is received by a machine recognized and understood. This is made possible by kunsmoderate intelligence, or artificial intelligence (abbreviated to AI). The development of AI has moved so fast in recent years that we hardly see things that are AI anymore because it is already seen as standard technology. Think, for example, of facial recognition by cameras or a spam filter in your mailbox. About 35% of the respondents indicate that they are completely unknown or only somewhat familiar with this AI. The majority of the respondents therefore indicate that they are well-versed in AI. This is reflected in the priority given to it by the respondents, because more than 42% of the respondents gives this year much or very much priority to AI.

What is AI? 

AI is often described as intelligence displayed by machines. We usually see this when machines mimic human cognitive functions, as the dissolve of problems and learn. There are many ways to define the different types of AI. First of all, a distinction is often made between forms of AI:  

• weak/Narrow AI – machines are specialized in one task; 

• strong AI – machines can perform multiple tasks and for example learning and problem solving; 
• Artificial Super intelligence (ASI) – machines that really start to resemble humans because they for example social skills and creativity to own. 

In addition, it is useful to make a subdivision based on the different functionalities: 

1. Reactive AI.Machines with reactive AI have no view of the history or context, they only have a picture of the current situation. They are working on a task or scenario that is being presented to them at the time. 
2. Limited Memory AI. huhalong Is everything alright to machines that can look back in the past for a short period of time. This has been widely used, for example, in the development of autonomous driving cars. 
3. theory of mind. Machines can recognize and understand human emotions, beliefs and expectations, and then act on them. While progress is being made in developments in this area, there are no concrete applications or examples yet. 
4. Self awareness AI. This is closest to a robot that is fully human and thus can reflect on itself and feel emotions. Although this idea has been toyed with for a long time, it is not yet a reality. 

Logically, current developments with AI are mainly based on types 1 and 2. This means that there are endless possibilities for innovation. A number of recognizable examples of applications of AI are; fraud detection in online purchases, personalization of news feeds and commercials, speech conversion to digital text, a robot playing a strategy game like 'Go', and the chatbot from an online help desk. Another great application is a test in which AI is used to: doctors to help detect different types of cancer on scans. 

There are still a number of limitations to the use of AI. For example, large, complex problems can be fed to a machine, but the large amount of choices and reasoning makes it extremely slow. People rarely apply the step-by-step line of reasoning that this AI–use a system and have something that machines cannot (yet) mimic; intuition. In addition, a lot of data is needed to be able to apply AI. Not all organizations have access to such necessary data sources, which makes it difficult to apply AI in a valuable way. Linked to the need for data, there is immediately the pitfall of data. There is often a bias in. In other words, it is extremely difficult to generate data that is completely objective. So it is important to be aware of the subjectivity that may have become part of your data. 

Trend development 

huhthe use of AI is only increased. Forbes indicates at the end of 2018 that a good indicator for the growth of AI is to look at the investments in startups. From January 2015 to January 2018, this increased by 2.1x for AI startups, where it increased by an average of 1.3x for other startups. The use of AI is increasingly reflected in society and this will only increase in the coming years due to the new services that of AI will be developed. In addition, AI will increasingly be used to enrich existing services. The use of AI to take over tasks that are currently performed by humans will also increase. 

There is still a great deal of public debate about the latter. Are robots taking over our work? What jobs will be in 5 until 10 years no longer exist? As every other industrial revolution has shown, jobs will indeed disappear, but many new jobs will also be created. This Of course, this is not without a struggle, and it is important that organizations are well aware of this impact on employees and deal with it adequately.