Digital systems permeate our private lives and the entire global business world – and there is no end to this fast paced evolution in sight. The pervasive digitization is affecting more and more areas, modernizing or replacing familiar processes or entire business models.

Zeros and ones, the smallest units of digital processes, are the all-determining basis – never before they had such significance and reach.

This development is also reflected in the dramatic growth of data stocks. Experts assume that there are currently 2 zettabytes of data in the world – a figure with 21 zeros. And this incredible amount of data continues to grow exponentially.

In the IT sector, device performance is improving steadily and significantly. What we used a computer for ten years ago can now be done easily and quickly with a smartphone. And while the foundations for artificial intelligence were already defined at the end of the 1950s, the computing power necessary to use AI on a broad basis has only recently become available.

In parallel, the amount of data we collect, store and process is increasing dramatically. As a result, data growth is an ongoing topic for all digital transformation initiatives. One reason for this is that high computer performance combined with rapidly increasing data volumes also enables criminal activity in an unprecedented "quality".

In the digital world in general and in our business in particular, high availability is essential. We need to make our systems highly resistant to damage, and especially resistant to total failure.

Modularity is a good way to achieve this, and this is where microservices come into play. Microservices are a special style of IT architecture that structure an application as a collection of services, coupled via clearly defined interfaces, and which implement business capabilities.

The microservice architecture not only provides a very high level of flexibility, but also a great ease of maintenance. In addition, it is literally impossible to destroy systems built upon microservices – when one microservice crashes, the system can automatically launch another instance of this microservice. This is the optimal concept to build anti-fragile systems.

Blockchain is one of the hottest topics in information technology right now, mostly mentioned together with the cryptocurrency Bitcoin.

A blockchain describes transactions as a sequence of blocks of code. Each new block is associated with the previous block and contains its history in the form of a checksum. It also contains the checksum of the entire chain, making the order of the blocks unique.

Essentially, you can think of blockchain as a kind of database. However, it is not stored on a particular server – each user has their own and complete copy, and all data are encrypted.

Every user and every transaction legitimizes the others, becoming a “point of truth” within the blockchain. As a result, the blockchain is very tamper-proof.

This creates the trust that is the indispensable foundation of any business, especially in the financial services sector.

The term “Artificial Intelligence” (AI) was coined in 1956, describing machines that were able to perform tasks characteristic of human intelligence. In a broader definition, the area of machine learning/self-learning machines is also attributed to artificial intelligence.

Deep Learning is one of the machine-learning approaches. It was inspired by the way our brain works; however, in contrast to the linear thinking associated with humans, deep learning is characterized by a network approach, leading to better and faster results.

Until a few years ago, a lack of computing power was the main hurdle preventing the broader use of artificial intelligence. However, highly scalable cloud computing has created the conditions necessary for the general use of AI. Today, AI will take the use and benefits of IT systems to a whole new level.

“Data is the new oil“ is a phrase heard frequently in the IT and business world.

This analogy is really apt – just as oil powers transport and logistics, data today drives business processes of all kinds in the economy. And just like with oil, data in its raw form is not usable; it needs to be refined to make it usable.

Big data is another of these modern, fuzzy IT terms – to most, it is not really clear what it stands for and how big it really is.

The term big data describes any voluminous amount of structured and/or unstructured data that has the potential to be mined for insights, for example about customer behavior. Aside from volume, it is characterized by a wide variety of data types.

While big data does not refer to a specific amount of data, IT managers often describe the volume using terabytes (TB) – a 1 with 12 zeroes – if not petabytes (PB), which is represented by a 1 with 15 zeroes. For perspective, in the private sector we usually still think in gigabytes – a 1 with 9 zeroes. The number of zeros gives a vague impression of how much data needs to be handled in the age of Big Data.

Volume: Large and exponentially growing

The industry expects that we will reach 40 zettabytes of data in 2020, and analysts predict that the amount of global data will hit the mark of 163 zettabytes just five years later.

One zettabyte is 1,000,000,000,000,000,000,000 bytes – to save you from counting, that's 21 zeros.

Variety: Data types and formats are also increasing

Today, data is being produced always and everywhere, and across many different channels.

Just think about posts on social media – on Facebook alone, 2+ billion users share, on average, 4,75 pieces of content every day, not to mention all other social media channels, such as Twitter, Instagram, etc.

Additionally, new technologies, such as wearables (for example, smart watches and healthcare monitors) will increase in the future, again creating loads of data.

This results in a wide variety of different data types and formats. As most of them are potentially relevant for our business, we need to be able to deal with them.

However, that does not mean that the data collected and stored is used. In order to be made useable and to create value, data has to be analyzed. And to be analyzed it first needs to be sorted and structured.

Data can be processed using a variety of methods. For example, groups can be clustered based on similar attributes to gain insight into the behavior of different customer segments. There are also models to predict the probability of specific outcomes, and data mining processes enable the discovery of patterns in large amounts of data. Via textual analysis, computer algorithms can even analyze natural language.

In the digital world we are flooded with data from a wide variety of different channels. In contrast to the traditional world, where we are used to working with systems such as CRM, these “novel data“ are unstructured.

According to Gartner analysts, over the next five years unstructured data will amount to 80% of total data volume.

Using unstructured data in a business environment is much more difficult. This is very unfortunate because the information contained in this data offers tremendous potential for strategic decision making, marketing and more.

Recently, various approaches (such as automated text analysis or metadata for videos and voice recording) have made it possible to access this potential.

When we want to leverage the hidden treasures of our data, we need to implement a data-centric business model.

Utilizing this approach, data become the primary asset. The goals we want to achieve by using this data define the technical environment – not the other way around.

This involves people, processes and technologies all aligning to ensure the business success of an organization, with the clear goal of generating and using clean and relevant data.

This is even more important, as with wearables and the Internet of Things, we are on the verge of a whole new world of information technology.

We are all aware that the digital age has significantly increased the importance of information and data security. And it is no longer just hackers, phishing and malware that are threats to us.

Although this awareness is widespread, it is apparent that this knowledge does not suffice to make us change our digital behaviors. As a result, our habits make us vulnerable to major security threats in the digital age – be it having an open laptop screen while traveling or having a wifi password visible in a press photo.

We need to develop a higher degree of mindfulness when we use information and data in some form publicly, and we need to ask ourselves if security is an issue and what the potential consequences of a breach might be.

Punk-looking, pale guys sitting in darkened rooms trying to decode passwords – for a long time this was the idea the public had of hackers.

If ever it was true, today it is certainly no longer the case. Hackers today are much more sophisticated than they used to be, and criminal activity is no longer easily identified.

Hacking has become a business model with business processes resembling those of the legal economy. For example, hacker groups contract other hackers on “projects” and share profits.

Data is not always scammed by “traditional” hacking methods. Hackers are constantly revising and refining their tactics. They take advantage of targets’ naivety and lack of awareness – for example, on the phone.

In today’s mobile world, IT is everywhere. For many, smartphones are the most important device; they are not only designated for personal use but also for work – be it in the office, on business trips, or while working from home.

This poses completely new challenges in terms of IT and data security. It is not without reason that BYOD – Bring Your Own Device , an IT philosophy and strategy, has been developed, regulating the use of private devices for business purposes. This involves, for example, employee awareness of general risks, the separation of business and private data, as well as the use of the latest updates to the operating system of the mobile devices, and more.

The upswing of the Internet of Things (IoT) will further exacerbate the threat situation. IoT means that a wide variety of everyday devices, such as cars and other machines communicate via the Internet. The problem is that, unlike manufacturers in the IT and telecommunications sector, manufacturers of such devices are less aware of data security issues. Just think about the Keyless Go issues in the automotive industry. This lack of awareness will make such devices popular entry points for hackers.

The Internet of Things is highly vulnerable to data security threats and will enable hackers to gain access to ever-increasing amounts of sensitive data. For example, it is possible for hackers to gain control over moving cars. One can imagine what the implications of such an attack might be.

This poses new challenges for the insurance sector.

Especially in our business, the trust that customers and partners have in us is our greatest asset and the foundation for our success. Data leakage or misuse undermines this trust and endangers our business.

In the age of rapid digitization and big data, the danger of such incidents is steadily increasing. Information security has always been one of our highest priorities, and not only since the implementation of the General Data Protection Regulation (GDPR), which was enforced in the EU in May 2018.

To date, information security mechanisms are primarily based on digital signatures, which are used to identify parties involved in data traffic. But the method of identifying “good” vs. “bad” signatures -- still used in a lot of anti-virus software -- reaches its limits with the shear amount of data involved in everyday traffic.

New technologies, including machine learning as part of artificial intelligence, can help to reach new levels of data protection. Such solutions are able to identify unusual activities and anomalies, even with extremely growing volumes of transactions. Instead of being reactive, they deliver a proactive approach. They train themselves to identify malware before it executes, and can calculate the risk of executable code damage. Then, files are either determined to be safe for execution or quarantined.

One of our latest security initiative projects is a tool which leverages a big data and machine-learning approach.

For a long time, companies believed that data and information security tasks were just for the IT department. In today's digital and highly-connected world, however, this approach is no longer sustainable.

Information security is no longer an issue that can be solved with simple hardware and software, such as firewalls. A holistic and enterprise-wide strategic approach to effectively protect systems and data is necessary.

This approach, in addition to system architecture and security-specific design of applications, also considers business processes and organizational aspects. An often underestimated element of an information security strategy is having a corporate culture that develops data security as part its DNA.

For every action and decision, we have to ask ourselves what the consequences for our system and data security might be. This philosophy needs to be backed by clear responsibilities and governance.