Thorough data management planning is crucial for research in the enterprise environment. The Data Stewardship Wizard educates its users and helps plan management of data, the precious results of projects.
First, it is necessary to specify what researchers must provide information to plan data management in the organization properly. As each enterprise organization is unique, the way of planning may be more or less different. The Data Stewardship Wizard is ready for that and allows to create knowledge models from scratch and customize existing ones.
Then the replies to a questionnaire specified by the knowledge model may be transformed into different forms suitable for evaluation or other internal/external purposes. By having a custom export template(s), researchers will be able to efficiently provide reports for their projects, just as DMPs for funding agencies.
Keeping consistent design and branding across information systems is essential in a business environment. DSW can be tailored to use corporate logos, colours, and fonts. Other UI and UX adjustments are also possible using custom stylesheets.
It is possible to also use the same design for exported documents, so all the PDFs from DSW have the colours, fonts, and logo in the header, just as the company's graphic handbook dictates.
The Data Stewardship Wizard provides an API that can be used for automation and integration. Hence, other systems in the environment can query and manipulate the content in DSW. For example, whenever a new project is created in the local project management system, a related project is prepared in DSW for specific employees.
Furthermore, DSW can also consume other services such as local SSO, document management systems and databases, controlled vocabularies and repositories with APIs. Thus, the Data Stewardship Wizard can be easily be plugged into the enterprise environment and become part of everyday workflows.
By planning data management with DSW, the actual burden of creating yet another document for a project will become a benefit. In addition, research staff will gain competence in working with data which will help them conduct research more efficiently.
The organization can, on the other hand, monitor, evaluate, and improve local data management. For example, data policies may be updated (and correspondingly the knowledge model in DSW) based on identified issues in existing projects. Then, employees can update the projects and become compliant with the latest data policies by clicking a single button.