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Director Market Unit at Volvo Penta
Digitisation and Corporate Culture
Digitisation has become a keyword for many companies and industries. In one way or another, they are working on process automation, data collection and analytics. The advantages of digitisation are obvious: increased productivity, reduced costs, improved quality, etc. At the same time, some, if not many, companies face resistance from their staff when it comes to implementing software and/or data digitation. A typical example is the implementation of CRM, where the number of successful stories gets lost in the flow of cooler talks about failures. In other words, digitisation is not easily embraced by regular employees. Why is that? Because effective digitisation requires a new culture of working with data and algorithms.
Companies that implement new technologies often face several challenges:
1. Increased Transparency: Digitisation can lead to increased transparency within the company. Employees may feel that their every move is being monitored and recorded, which can create a sense of discomfort and fear of being evaluated or compared with others. This can lead to resistance from employees who are not comfortable working under these conditions.
2. Trust Issues: Digitisation can erode trust between management and employees if data is misused or employees feel that it is being used against them. Building a culture of trust is essential, but it takes time. One misstep in the use of data can lead to a breakdown of trust within the entire organisation.
3. Resistance to Change: Introducing new technologies often requires changes in workflows, processes, and job roles. Employees may resist these changes due to fear of the unknown, a lack of understanding of the benefits, or concerns about job security.
4. Skill Gaps: Implementing new technologies may require a different skill set from employees. Some employees may lack the necessary skills, leading to the need for retraining or hiring new talent. Again, it may bring concerns about job security.
5. The other side – is when the available competence of employees is higher than the suggested software.Employees know their expertise is important to the company, but digitising their knowledge could make them redundant. In such cases, resistance to knowledge digitisation, sabotage, or incomplete and unreliable knowledge transfer may be expected.
6. Integration Challenges: Integrating new technologies into existing systems and processes can be complex and may lead to disruptions in operations if not executed smoothly. The fact is that compilation of legacy systems and new ones go smoothly quite seldom.
7. Data Security and Privacy Concerns: Digitisation and new technologies involve collecting and storing vast amounts of data.
Ensuring the security and privacy of this data is crucial, as any breach can have severe consequences for the company's reputation and legal liabilities.
Context description is another problem. Modern databases often struggle with supporting contextual modeling, especially when dealing with small datasets. For instance, the scent emitted by equipment during its operation may not be relevant if the equipment is installed outdoors. However, it becomes a significant factor when the same equipment is installed in a covered parking area of a shopping mall. How often do customers evaluate the smell of equipment when making purchasing decisions? Along with this, the smell is one of the examples of reality that is difficult to digitise. Adding context often makes data sets very expensive yet very complex.
Digitisation implies the implementation of algorithms. Algorithms entail relatively rigid rules, which means they may no longer correspond to the changes in real life. There are many examples of companies following a process described in software rather than what is convenient for the customer or employee. This affects both customer experience and employee experience. Attempts to make software flexible and accountable for all possible situations lead us to its complexity. Complex algorithms are challenging to update (resulting in increased costs), but most importantly, many people stop understanding how algorithms work. As a result, they cannot assess the adequacy of the output. In some cases, they cannot stop the function's execution, even if they see something going wrong. History has witnessed several examples of technological disasters where the software intentionally limited the abilities of personnel. However, more often, the staff simply agrees with the program's subpar solutions.
Working with such systems requires qualified feedback, self-learning, and other responsibilities from individuals for proper digitisation and interpretation of the context. However, people tend to be lazy and do not create digitised content effectively. This touches on another important aspect of digital culture: people need to be taught to interact with content (e.g., giving likes) and create content. Certain professions, such as medicine, law, and scientific research, cannot exist without constant content creation. A doctor documents every interaction with a patient. However, how many salespeople digitise the results of their client visits? Attempts to compel employees to digitise often encounter sabotage or data input just for the sake of inputting data. In other words, employees digitise only as much as needed to avoid punishment. This means that the system starts filling up with incomplete or, even worse, inaccurate data. Algorithms will then use unreliable data to provide recommendations and make decisions. You reap what you sow.
By fostering a collaborative and supportive environment, companies can enhance the accuracy and reliability of the data they use for decision-making and improve overall efficiency
Creating a digital corporate culture is a lengthy process that involves training, open communication, building an atmosphere of trust, and fostering an environment for knowledge exchange. There is no one-size-fits-all recipe for creating such a culture. Many companies rely on the experience of social networks, feedback-based machine learning systems, and other methods.
However, one thing can be said with certainty: using penalties and punishments to motivate employees is not effective when building such a culture. It does not work well in ordinary life, and it will not work well when fostering a data-driven and digitisation culture either.
In order to foster a successful data-driven culture, companies should focus on positive reinforcement, rewards for good data practices, and recognition of employees who contribute to creating accurate and valuable data. Encouraging a culture of continuous learning and improvement, where employees understand the significance of their contributions and are motivated to provide high-quality data, is more likely to yield better results. By fostering a collaborative and supportive environment, companies can enhance the accuracy and reliability of the data they use for decision-making and improve overall efficiency.
