The digitalization of society means that your organization will increasingly need to learn how to use and organize technology, data, and AI. Technology is becoming more integrated into primary products and services, and data is enabling smarter process handling and unique customer experiences. Employees also expect an optimal digital workplace. Therefore, it is high time to think carefully about how to organize your tech and data capabilities more efficiently, says Dr. ir. Gerard Weijers.
Tip 1: Organize according to different types of IT
Over the years, IT has become a multifaceted entity. Where it was once primarily used for administrative recording or as a limited part of technical equipment, it is now the smart interface to your customers and suppliers, processes are fully automated, and IT is the way you differentiate yourself. The time when you could organize IT in one way is over. Some tech teams generate direct revenue and need to function optimally agile, delivering new functionality multiple times a day if necessary. Other tech teams benefit from a certain calm and it is wise to mainly follow the solution provider. More and more enabling teams are also emerging in the field of data and integration. Organizing the Tech and Data organization has become a new discipline where teams need to be structured differently depending on their goals.
Tip 2: Make your tech organization scalable and agile
Scalability and agility are important parameters for a modern Tech and Data organization. Today's needs are not necessarily tomorrow's needs. New legislation, new markets, and new products lead to a different strategic focus. How do you transform a Tech organization increasingly structured around self-organizing teams? New coordination mechanisms will emerge to make the entire organization agile. Ensure clear three-layer coordination at strategic, tactical, and operational levels.
Tip 3: Ensure a strong central data foundation for smart AI and algorithms
The Data organization is growing rapidly. More and more data is becoming available and is needed more timely and relevantly for daily practice. Because this data is directly used in daily processes, the required data quality also increases, and many organizations struggle with poor setup and poor data quality. A certain degree of centralization of data responsibility is needed. The data foundation must be solid. Responsibilities regarding data platforms, data quality, data security and privacy, and data architecture are central responsibilities.
Tip 4: Invest in continuous learning and development
To keep up with the rapid changes in technology, data, and AI, continuous learning and development are crucial. Encourage your employees to stay updated with the latest trends and advancements. Provide training programs and opportunities for professional growth to ensure your team remains competent and innovative.
Tip 5: Foster a culture of collaboration and innovation
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