3.3 The generation and use of theoretic knowledge is redefined:
• The theoretic knowledge generation can become data driven. Instead of confronting tacit knowledge of multiple experts, the models of behaviour can be extrapolated directly from the data of the issues themselves. Standardisation and the relevance of the issues would no longer be matter of expert perception, but data based. An example: in financial institutions operational risk management directives are based on the risk probability and consequences perception of the risk management experts. Based on the experiences in the financial sector, it is easy to conclude, that they often put emphasis on the wrong risks.
• Storing the theoretic knowledge: the big data theoretic knowledge is stored in the form of predictive or prescriptive models or is based upon reports, produced by the models. It upgrades and complements the existing theoretic body of knowledge.
• The theoretic knowledge use is instance based. The experience, stored in models is applied upon the instance data. The elaborations, predictions and prescriptions are used to support decision taking, communication and automation. The main advantage of using big data based models is, that users do not need to analyse the complete theoretic backgrounds, but can focus on instances.
The backdraft of implementing BDA is, that it cannot imply all of the knowledge on complex issues, especially if the data quality or the number of cases recorded is not sufficient to provide reliable models. Therefore Big Data stored knowledge should be focussed on supporting processes, related to relatively simple instances, with multiple repetitions.
3.4 The mechanisms to manage the representation of the complexity are redefined. The main goal of an organisation is to coordinate individual capabilities to achieve itsindividually defined goals. The knowledge of an organisation is a complex combination of structured rules and shared tacit knowledge on multiple levels, gained from previous experiences – gained through previous activities or acquired from the environment.
Figure 3. Learning by observation
BDA tools use a different approach than Business intelligence. Instead of reducing variety and focusing solely on financial business outcomes, they provide the option to understand, predict or even propose activities on the detailed data, provided by the organisation.
3.5 The inter and trans team learning and experience sharing is redefined.
Figure 4. Sharing experience across teams
The experiences of multiple teams in similar situations can be successfully identified, understood, and learned upon with the help of BDA. For example, the effects of using multiple communication marketing campaigns in multiple markets can be compared.
There is though a limitation of using BDA to support learning between the teams. It works well in a highly repetitive processes, where data on similar situations are easily obtainable, as for instance sales, or mass production. If there are not enough similar cases, or if the data variety to explain a cases is too high, BDA cannot adequately provide insight.
3.6 BDA support the interhierarchical learning processes and reduce the number of the hierarchical recursion levels.
Figure 5. Understanding the drivers
The BDA is used by the higher levels in two ways: First, by elaborating the feedbacks of the lower structural recursion levels, it can fine-tune the activities, guiding to the desired results. Secondly, it can use BDA to better understand the needs, processes and relations at lower levels to propose solutions that provide value added for all the subjects, affected by the organizations. The higher capacity to manage variety also reduces the need for hierarchy and allows structural recursion. In some cases, the automated guiding systems can entirely eliminate the need for intermediaries between the consumer and provider on a global scale.
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