Data science and digital processes offer enormous opportunities to accompany transformations and adaptation processes in real time and to support them in a maximally effective way. And with each step of the digital transformation, more possibilities are added. This also creates new approaches and opportunities for change management strategies and methods.
Das „Sense and Response“-Modell
Pinaki Chakladar, Senior Managing Consultant bei IBM, legt besonderen Wert auf das „Sense and Response“-Modell, dass der Sammlung und Verarbeitung relevanter Daten zugrunde liegt. Sein Fokus auf die Wahrnehmung individueller und situationsabhängige Bedürfnisse der Stakeholder kann radikal verändern, wie wir in Zukunft Transformationen begleiten. Er bricht durch diesen Data Science Ansatz mit vielen traditionellen Change Management Modellen. Das Modell verwendet Design-Thinking-Prinzipien (basierend auf Stakeholder-Personas) und agile Programm-Methoden, um einen Rahmen für das Veränderungsmodell zu schaffen. Es ist speziell auf die Erfahrungen der Stakeholder im Verlauf des Veränderungsprozesses ausgerichtet und nutzt fortschrittliche Analytik und Datenwissenschaften.
A cornerstone of this new change model is mutli-channel data analysis. It is based on digitally "listening" to stakeholders and thus capturing the sentiments of employees, sales partners and customers. In the example presented, social media comments, hotline enquiries and the use of training videos are evaluated. Details such as pausing and repeating the training video are also included. On this basis, the interests and tools of a wide range of stakeholders are analysed. The data science model uses continuous measurements to adapt the change plan to the needs of the stakeholders in real time. Pinaki Chakladar thus achieves faster and optimised transformation results for its clients. Currently, this model is being used successfully as part of a digital transformation programme in one of India's largest chemical and petroleum companies.
The path towards a data-based corporate culture
Companies in which the digital transformation is already further advanced are already thinking aloud about a data-driven strategy, data-driven decision-making. This is where the small difference between "data-driven" and "data-supported" comes into play! A discussion breaks out about the acceptable degree of data affiliation, possible data manipulation and the meaningful consideration of human experience and competencies. The question arises as to how much data-driven behaviour we approve of in our company as a whole and at what point. Opinion formation on this is directly related to the degree of digitalisation of the private and social environment and is thus a question of society. Alan Bostakian, Chief Consulting Officer at Intelligent Organization, has taken a closer look at the discussion on "data-driven corporate culture" in the context of Data Science in Change.
A culture does not develop at the push of a button or top down! It is a collective journey to develop this culture. It is guided by the behaviour that is generally accepted and valued. And: our change management practices play an important role in this journey. A data-driven culture only benefits the company if employees also have the ability and permission to live it.
In data-driven corporate cultures, "data democracy" is crucial. "Data democracy means full access to the collected data for all employees, so that it is available to the collective swarm knowledge at any time and from any perspective. Behind this is a decentralised, agile idea with only minimal hierarchical control. However, in order to achieve the optimal degree of utilisation of data science in the company, each employee needs the ability to retrieve the data according to need and to filter it according to perspective. In addition, they need the cognitive ability to relate and interpret the data in such a way that action-oriented conclusions can be drawn from it. We are not there yet! The competence to read and evaluate data is still insufficient. This is clearly a future skill that every company should develop!
Capgemini's recent change study shows that data-driven change management significantly improves transformation success.
The visualisation of data analytics in change is key!
Especially when it comes to newly introduced products, services or tools, comprehensive data analysis can help us learn more about the user behaviour of customers and employees. The data can show both problems and best practices. But visible does not mean understandable. Often we are faced with a data jungle or look at the same bars and pie charts. In change management, however, other information is interesting.
We want to know where things are flowing or where things are faltering. On which topic is help particularly often requested? Which things are easy and intuitive to use, so that employees and customers feel supported by this innovation. Sandeep Bhat, EVP-Products at Gramener Inc, is an expert in successful visualisation of data science in change. The communication of his IT projects relies on the targeted visualisation of data streams and volumes. He uses the fact that the reception of data by the human eye is processed differently in the brain than the reception by the ear. People can grasp and analyse complex situations extremely quickly with their eyes. We do it every day in traffic, when we do our work or observe other people. The bare data can sometimes be difficult to interpret, but the right image can reveal the data AND the information behind it.
Using this graph, one support centre was able to show that the enormously long turnaround times of their service requests were not because the processes were bad or too long. The problem of this centre was that the customer's problem was not properly understood. Here, ready-made solution proposals were repeatedly rejected by the customer as unhelpful. The team focused specifically on this weak point and was able to improve throughput times enormously in the short term.
So for the communication of our change projects, a good visualisation of what needs to be changed is essential. In this way, data → insights and insights → collective knowledge can emerge. Based on the knowledge gained in this way, many stakeholders can be activated to solve the problem in order to bring about changes in a targeted and speedy manner.
Conclusion:
With the use of advanced social analytics and artificial intelligence (AI), enormous opportunities to transform better and faster are emerging. We could easily switch between a systems-based approach and a personalised change approach by adding data science. This would naturally support agile, iterative transformation programmes and thus the evolution towards continuous change in organisations. But the beneficial use of comprehensive data collection depends largely on how quickly we can build the capacity in companies to read, visualise and interpret this data. In principle, it means learning a whole new language for employees to read, speak and visualise! And these skills seem indispensable, given the increasing amount of data available, the need to guard against misinformation and to deal competently with increasingly complex contexts.
"We try to influence through the eyes what we cannot convey to the public through their word-fixated ears!"
Florence Nightingale
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