AI Use Cases
GenAI Accelerator
With the right strategy, data assets can accelerate the achievement of corporate goals and generate business value, created through improvements in decision-making, optimization and automation in operational business and through data monetization.
With the right strategy, data assets can accelerate the achievement of corporate goals and generate business value, created through improvements in decision-making, optimization and automation in operational business and through data monetization.
A data strategy harmonizes analytics and data governance, as well as the architecture, tools, processes and rules for working with business data. The strategy defines how data is to be managed, analyzed or processed. The data strategy not only relates to decision support through sound data, but also includes compliance and security aspects.
Develop a vision of how business strategy, business operations, data operations and risk management lead to an improved analytics architecture.
Target operating model BI and analytics architecture – this is the core of the strategy. Based on the evaluation of requirements, best practices, market trends and the maturity level of the organization, a target model is developed and scenarios are evaluated that describe the path to the goal.
Processes – the data strategy also looks at the optimization of processes. Data should not only be available for analysis – it should also be obtained automatically and without system interruptions. In the area of conflict between legal requirements, customer interests and trouble-free processes, the potential must be determined here.
Organization, culture and roles – the data strategy is driven by the business strategy and the chosen operational business model. But this is not a one-way street: it is essential to clearly define which positions need to be adjusted or changed.
synvert’s partnerships with innovative and market-leading software providers, combined with a broad base of customer projects spanning three decades, have resulted in comprehensive know-how about data strategies in the business intelligence and data analytics worlds with their drivers, best practices and trends. synvert accompanies you through change and provides customized tools for strategy development.
Keeping an eye on the architecture – we take a comprehensive and neutral view of your information and data architecture. And we develop a suitable target architecture and the roadmap to get there. Where should it go? To the cloud, more AI, data engineering, self-service BI with storytelling? Do you want to build a data lake or a data catalog?
synvert works with you to determine achievable and sensible use cases so that nothing unrealistic is planned. It’s about setting the right course in the right context so that the use of new data and technologies can later be put into practice and generate benefits. synvert has created its own tools to support the development, management and documentation of analytical use cases, which are used in the strategy definition.
synvert interprets data as assets that should benefit the entire company. Data governance and the management of data quality, metadata, master and reference data, information security as well as content and knowledge management, all interact with the data strategy. The synvert Data Governance Framework gives structure to these themes.
Actions on the one hand lead to effects on the other. In most cases, the data strategy is not just an IT question, and strategy considerations must be broader! It is about change management, communication, and perhaps the reorganization of roles and responsibilities, or, as previously mentioned, the optimization of processes. Organizational change will also need to be described in the target image and the strategy.
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