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We are at the outset of the largest restructuring in the last century.

There have always been changes - but the dimension and speed with which new conditions develop is constantly increasing. As we digitize more and more, and connect more and more digitally, we are creating more and more networks. In networks, however, the complexity does not increase linearly, but exponentially with each additional participant. Like no other megatrend, digitization is therefore driving volatility, uncertainty and ambiguity. This results in entirely new organizational structures.


The root of our problem is NOT that we are in a "Great Recession" or a "Great Stagnation", but rather that we are at the beginning of a great restructuring. André McAffee, 2011

We do not have a complexity problem, but a decision dilemma

The value of a company ultimately results from the sum of the decisions it makes and implements. Its assets, capabilities and structures are useless if executives and managers are unable to make the essential decisions more often right than wrong.

Because complex problems increasingly have to be solved not only within one's own team but across domains, it is easy to involve too many people for too long. Increasing organizational complexity, which is rooted in strong product-related, functional and geographical areas, additionally blurs the responsibilities. As a result, the number of decision makers has continued to increase. Thus, managers are less and less able to delegate decisions properly. Lower communication costs due to the digital age have exacerbated the situation, as more people have become involved in the flow of knowledge exchange via e-mail, messenger and group chats, without the decision-making authority being clarified. As a result, managers spend more and more time in coordination, and more and more often without results. 

This way, important decisions take longer and longer, or are simply made according to gut feeling. The number of wrong decisions is increasing. As a result, companies act even more cautiously, make decisions even more slowly. 

With increasing age and size, companies are often "trimmed for efficiency". That is why they work with specialized departments, hierarchies and more and more processes. 

This is a great approach for a world where both the problem and the solution are known. In such an environment, processes and specializations are the right way to reduce the risk for a company. But in return, it reduces their ability to be flexible and adaptable and - increasingly important - to respond to new opportunities and threats. As a result, the more efficient a company becomes, the slower it is to react to the unforeseen.  

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Efficiency makes companies slow 

Automation ignores the core of the problem

Simple and repetitive tasks can already be automated with a variety of solutions such as chat bots or robotic process automation tools (RPA). This works whenever all rules and parameters of a situation are known, in other words can be derived from the past. Computers are quite good at finding patterns, especially when huge amounts of data are involved. If the conditions under which such patterns were created are assumed not to change, there is a high probability that they will continue to exist in the future. This is the belief that underlies more or less all prediction systems in use today. However, if an unexpected event occurs, all predictions are worth nothing.  Because algorithms do not have a complete picture of the real world, they always have to fail if something happens that is outside of the specified parameters. Therefore computers can play chess better or drive a car better than humans, but they cannot do everything, they can only do what they were designed for. Whenever the complexity of the situation exceeds the capabilities of the algorithm, or because ethical or moral factors have to be taken into account, humans will still be needed for the future. 

So, let's be honest: Everything that can be automated will be automated. Because it is cheaper, faster and less error-prone. Even in areas where we could not have been thinking of this a short time ago. In result, the demands placed on individuals will become ever higher, making life somewhat more complicated. Human work will become more and more about those things that were not expected, for which there is no standard procedure: The really important decisions with far-reaching consequences. The complex exceptions. Serious moral and ethical questions that can only be judged correctly by people. 

Most companies simply tackle the problem as if it were a pure information challenge. Making data-driven decisions is considered the "best practice" of the time. For many years, however, it is not the companies that have benefited from this approach, but first and foremost the providers of business intelligence and analytics software.  The massive investments in the provision of more and more data have hardly improved decision-making in companies; on the contrary, decision-making processes today take longer than ever. This will not change with the next software release. Thats what Gartner's Rita Salam means when she says "We see a real gap between the information provided in business intelligence and the quality and transparency of decision-making." To really move forward, you need different approaches and new solutions.

Complexity is the most important resource in the world today

One should not mix up complex and complicated. Complexity is a measure of not knowing. It disappears through learning. Complexity is a measure of the number of surprises one has to expect. Complexity can be reduced, complexity cannot. You have to open it up.  


Most real world data is tied to the context in which it was collected. Even if data measure the same thing, two coupled data sets are usually heterogeneous. Suppose you are measuring something as simple as the temperature in a refrigerator, the place where the measuring device is placed, the type of seals, the doors used, etc., all play an important role. But when data is so inextricably linked to external factors, it cannot be accurately cleaned or adjusted to match another set of data exactly. 

In order to become more flexible and resilient and to drive real innovations forward, companies will in future be primarily aiming to acquire contextual competence - the ability to make connections. This is not a task for individuals, but requires cooperation across teams and departments. Those who develop such connections and make them accessible to others will gain (new) solutions, answers and prospects. In this context, complexity is no longer a problem, but becomes the most important resource of our time. 


It is no longer about more and more data, it is about opening up the context.

If a Christmas goose would analyze all the data available to it, the amount of food available to it, the regularity with which it is fed, etc., it would probably come to the conclusion that humans are very well-meaning to it. Just before Christmas, all the forecasts about her future would probably be extremely rosy - and yet we suspect she will soon revise her beliefs... What the goose lacked was not better data, but the right context


Important decisions cannot be made on data alone. They also depend on intangible factors such as trust or enthusiasm, but above all on human experience and the ability to understand complex relationships.  

The comprehensive understanding of the interrelationships of cause and effect of the world we live in cannot be replaced by machines in the foreseeable future. For this reason, any call for exclusively data-driven decisions is a bit like looking for the key just by looking under the light of a street lantern with your eyes closed.

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Data in complex situations unfold their true value only when we consider it in context with other available information. In the intelligent enterprise, this should be possible seamlessly across departments or spatial distances. All processes should be aligned with value streams and no longer with domains. Because all problems are ultimately business problems, they should be treated as such - and not in silos. 


O-Data comes from enterprise applications such as sales, finance, logistics and HR systems. It provides performance indicators such as inventory turnover rate, interrupted purchases, days sales outstanding on receivables or employee churn rates. X-Data, on the other hand, describes the human factor to O-Data: the beliefs, emotions and intentions that reveal why certain things happen and how they can be influenced. X-Data comes from sources such as customer feedback, net promoter score, product reviews, brand sentiment and employee motivation. 

Similar to the operational environment, the analytical environment can also differentiate between key figures and the more soft, human factors. While A-Data aims to answer the question of whether a problem exists, C-Data answers the question of why. Here, it is particularly important to look at situations from different perspectives and to place them in the right context. C-Data is mainly found in report comments, meeting minutes, email distribution lists and chat rooms today.

The 21st century will be all about optimising technologies and processes for people

In recent years we have become incredibly good at constructing the building blocks of decision models. We have generated more and more information from more and more data. We have prepared it beautifully in countless systems and made it accessible to more and more people. It is only in the application of this data that we have so far barely considered the human being. 

beeBlum puts the human being in the center of attention. We support the dynamic process of analysis and evaluation of information. We give predictive analytics a framework for modelling complex systems to support decision intelligence. 

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Decision Intelligence

The analysis and optimization of decision making is a discipline that has hardly been considered so far. This is why an outstanding number of opportunities are available here today. "Decision Intelligence" is still very young and combines data science with theories from social science, decision theory and management science . Its application offers a framework for the use of machine learning on a large scale. The basic idea is that decisions are based on our understanding of how actions lead to results. Decision intelligence analyses these chains of cause and effect. Thus, many facts that are only "felt" today become measurable. 

This is an exciting time. On our way to a knowledge-based society, the next major challenge is how to deal with decisions effectively. Through better communication, collaboration and continuous improvement, beeBlum can help you to find better outcomes of complex issues in an incredible number of use cases

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