Data management Transforms Reporting into Leadership and Auxiliary Intelligence into Genuine Value Creation

We are facing a new phase in the digital development path of companies, leading towards real-time data-driven leadership. Then, data will no longer be something abstract but an equal decision-maker, one of us.

The digital path of companies began with the development of services and usability and progressed through ecosystem thinking to the final migration to cloud systems. This phase in companies' digitalization measured the trust in technology. In the next phase of the transformation journey, trust in data will be built on the organization's expertise. It's about the company's ability to create compatible structures and models within the organization for operational data management. Ahead lies a development path towards the Internet of Everything and real-time data-driven leadership.

Previously, humans had to learn to communicate in machine language, but with generative AI, machines are now capable of speaking human language. This, combined with holistic data management, breaks down the traditional IT/OT thinking and the boundaries formed within organizations. However, this requires even more systematic data management. Data management ensures the quality of data, without which there can be no data-driven leadership, data-driven product, service, and process development, or AI applications that enhance competitiveness. When an organization can manage its data, it can trust its data. Then data is no longer something abstract but an equal decision-maker.

Three Connecting Factors

When we search for answers with our clients as to why the business goals set for data have not been met, we repeatedly encounter the same questions and problems. These focus on three main areas: data platforms and technology choices, data management and quality, and the usability and applicability of the achieved results. Data silos remain the most significant barrier to strategic data-driven leadership. At worst, data is stuck in an individual business unit with numerous obstacles to its shared use. Data may also be scattered across the organization on different platforms without anyone having a clear view of the overall picture. Siloing significantly affects the second challenge of data-driven leadership: data quality. In silos, there are no common procedures, governance, or development practices for data handling. Therefore, data quality is poor and unharmonized, making its effective shared use impossible. Problems are often tried to be solved separately from each other. The business's data-related goals are delegated to the IT unit, and a solution is expected from technology. However, this does not create sustainable value but rather isolated solutions. Sustainable competitive advantage requires an organization with consistent structures and practices, supported by an information system from data platforms through applications to interfaces.

Emphasizing IT and Business Collaboration

In the AI era, the prerequisite for successful strategic data-driven leadership is that business and IT jointly create solutions that generate the most value for the company. Generative AI, data analytics, machine learning, and all data-based product and service development require high-quality data. The solution to data quality issues lies in the organization's ability to manage the technological whole from the perspective of business goals and continuously evolve.

Data management becomes emphasized when the next phase of generative AI development connects AI to companies' information systems, such as ERP and CRM systems. To make this productivity leap and business renewal possible, organizations must ensure that the information system ensemble, from platforms to integrations and interfaces, is built to support data quality assurance and horizontal collaboration across the company's different units.

From Technology to Capability Creation