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The key to beecome a data-driven organization is context.

Data gets commodity. The underlying challenge is to open up its contexts in order to make better use of it.


Everything that can be automated will be automated. However, algorithms can only work within the context they are set. That is why they will be able to handle only the routine jobs, and only the regular cases. Every exception, every abnormality must be analyzed in its individual context. However, the exploitation of contexts will remain the domain of humans for a long time to come.


However, the challenges we face today are so complex that no one can develop them alone. Thus, agility will no longer come from pure speed in the future, but from the ability to explore multiple areas at once and combine them into something that is more than the sum of its parts. 


The contextual knowledge of employees is the refinery for deriving real benefit from any data. It is only through the contextual knowledge and interpretation of knowledge workers that valuable insights can be derived from the information obtained and thus meaningful actions can be trigged. It is only through the contextual knowledge of people that we find out which data helps us in which situations, which criterion is missing in our algorithm in order to function properly in such unusual situations. 


To make informed decisions as an organization, first of all, you don't need more data. You should also not try to work on the mindset of your employees. Focus on helping knowledge workers understand the context of information instead. 

Every process that people are involved in is an analytical process. In order to be really data-driven in a world based on division of labor, all employees must therefore be involved in decision-making processes: Existing concepts such as dashboards, email or group chats are not suitable for this.

Knowledge workers need a tool that helps them to open up complexity, discover relationships and hidden pools of knowledge, enable collaborative decisions and foster innovative solutions.

Business users who are less familiar with data need a simple, intuitive access to information and an uncomplicated way of getting involved

Companies benefit from a better understanding of correlations, more effective and faster processes and a better working culture

With beeBlum you translate your problem or question into a board based on the Kanban principle. The collaboration scales because the exchange takes place via personal feeds with the departments. Even occasional users can be integrated into the process using an app specially optimized for them.

Knowledge workers get information much faster and save a lot of valuable time. Thanks to artificial intelligence (natural language processing), they not only summarize results much more effectively, but also find previously unknown relationships and hidden know-how. They develop their findings directly in beeBlum into alternative courses of action, weigh the advantages and disadvantages of the various options against each other.

Free Whitepaper: 

"Why being data-driven
is no longer enough.


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Data-driven decision making is no longer enough

A company is data-driven when data provides added value in the daily flow of business processes. When people benefit from it. As we've discussed, this is not achieved by making more and more data available, but primarily by improving the possibilities for collaboration in the context of decisions. The main challenge here is to link across domains and departments. Without artificial barriers such as complicated authorization procedures, which ultimately only lead to misdirected energy to bypass the same. This is exactly where beeBlum supports you.

beeBlum helps companies to make solution-oriented decisions.

Knowledge work always starts with the problem, the challenge in the given situation. This can be not only a deviation in a dashboard, but also a message from a customer, the result of a meeting, an open task, and so on. To tackle this, you draft possible actions. This involves designing possible futures and then creating actions to realize or avoid one or more of those outcomes. And not until then do you collect the data to do so. Crossfunctional and goal-oriented. The system-based modeling of such complex cause-effect relationships also takes the load off our brains and helps us to better understand interrelationships. Thus, in the future, the work of knowledge workers will shift away from analyzing data and toward understanding the context and optimizing decision models. 


Hence, a successful data strategy does not start with the data, but always with the decisions themselves. 

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