In the face of digital change, it is becoming increasingly important for companies to strategically use their data and its informational value. Data culture and data competence, i.e., the ability to develop, understand and communicate with data, also form the basis for companies on which they develop their AI and ML strategy and projects.
In the face of digital change, it is becoming increasingly important for companies to strategically use their data and its informational value. Data culture and data competence, i.e., the ability to develop, understand and communicate with data, also form the basis for companies on which they develop their AI and ML strategy and projects.
If more and more employees use AI solutions in the company, a minimum level of data competence is essential. Knowing the correct data to solve a business problem and the ability to interpret it and make AI recommendations is a prerequisite for employees to trust and successfully use AI in their decision-making. A typical data language in the company also opens other doors for successful cooperation with experts. In addition, appropriate measures for change management and further training determine whether companies remain competitive.
A Forrester study commissioned by Tableau confirms how data literacy affects business success. The Data Literacy Survey of Executives and Employees explores the relevance of data literacy to better business outcomes and contrasts management and employee perceptions.
Data Literacy Promotes Innovation
Organizations value data-literate employees because they believe they make better, faster decisions. They are also more productive and innovative, according to a critical finding of the study. 77 percent of executives state that the ability to innovate increases when data is used correctly. More than half see the potential for sales increases as a result. The employees agree. 86 percent of them say they make better decisions with data. In general, the higher the degree of digitization in the company, the more essential skills are in handling data.
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AI Projects Require Data Literacy
For any data-driven AI activity, it is crucial to understand what the data was collected and maintained for. This also applies to the question of how they should be used in the past and the future. To do this, it is essential to train a model with complete data that reflects the actual situation at the moment of decision-making.
However, data scientists often do not know what the data is for and how it is generated: What activities and technological processes are required to provide the data, and what does this data mean for the business? Here, analysts and users close to the data and who know the problems to be solved play a significant role. AI is, therefore, a team task, the success of which depends on the business context and, in addition, primary data and model competence.
Finally, there are human factors critical to project success that companies often overlook when they focus too much on data and technology. With AI, predictions are primarily possible. But there has to be someone who defines the measures by which these can be implemented. Does the proposal make sense because it provides an explicit action, and are the people concerned implementing it? Is there an environment where these suggestions will be effectively taken up?
Identify Promising AI Projects And Expand Them Step By Step
Project selection is probably the most significant challenge companies face with AI initiatives. Crucially, the AI strategy is aligned with business goals.
It is essential first to narrow down the problems and issues that companies want to solve with AI. How are the responses helping to improve business outcomes, and what about the available resources?
The first successful AI project is often the easiest to operationalize and productively implement with the slightest change. It is advisable to have a project that will bring benefits as soon as possible, even if the improvements are minor. It makes sense to involve customers, users, and stakeholders as intensively as possible in the development process. In addition, feedback for more data collection and input from those responsible should be possible.
The fundamental data strategy plays a vital role in the successful application of AI in the enterprise. The continuous building of a data culture creates optimal conditions to develop skills and promote new solutions throughout the operation.
Many companies have invested in data and analytics in digital transformation in recent years. It is crucial to view data and data literacy as a team effort. The task now is to transfer and expand this attitude to AI.
Training Beyond Traditional Roles
Therefore, a high level of data competence is very relevant for all areas in modern, data-driven organizations. Although managers and employees agree on this, this awareness does not necessarily lead to investments in appropriate further training measures. These are also often limited exclusively to professionals. According to the Forrester study, 43 percent of executives believe that further training in handling data is only relevant for traditional data functions, such as analytics or data science. Only 34 percent of them offer appropriate training for the entire workforce.
To unleash the tremendous potential of data, companies must invest in their most important resource – their employees. In doing so, they should offer further training beyond the traditional data-oriented roles because a company’s success depends on all departments being trained to use data for better decisions and ultimately to achieve competitive advantages. So far, executives still assume that expanding the use of AI requires larger teams, especially more data scientists. However, not every business challenge needs to mobilize the data science team. With the right approach, the benefits of AI can be realized without addressing the challenges of traditional data science cycles.
Therefore, those responsible for providing and scaling AI solutions must ensure that AI is perceived as a team task with different competencies. Some AI projects require a specific combination of people, tools, and expectations of what successful use should look like. Defining the respective constellation correctly enables more successful AI projects, expands the group of AI users, and accelerates and supports decision-making for the entire workforce.
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