What stumbling blocks should you consider before starting the project?
In the run-up to any IT project, expectations play an important role. What are the project goals and deliverables? What is the timeframe and cost of the project? Preparation is the be-all and end-all: a lack of goal definition is among the biggest stumbling blocks of digitization projects . Transparent communication among all stakeholders is important to create a common understanding. Both internally and in collaboration with external service providers, project roles, responsibilities and authorities must be defined in advance. A multi-stage project plan with milestones also helps to structure complex digital projects and make them scalable.
How do you get your team excited about a new application?
"Change management" is the keyword. Employees play a key role in any digital transformation project. It is therefore important to involve employees right from the start and communicate goals transparently. Management has the task of sensitizing employees to the upcoming change. The "why" and the added value of the project are the focus here. Before "going live," IT training lays the foundation for application in the company. In a subsequent hypercare phase, the administration should be available to employees for queries. In addition, a decentralized knowledge network can be established by means of key users. This relieves the burden on those responsible and shortens information paths. Together, all measures aim to win over employees as advocates of the implementation project.
How does data become valuable information?
Starting small is the motto: The first thing is to develop an awareness of the data that accumulates in the company. For example, does the data accrue in dealings with the target group, with machines or in the course of company processes? The first step is to collect the data that already exists and evaluate it with a view to the intended project result. Comprehensive data collection allows relevant information to be brought together and patterns to be identified. Data gaps can now also be identified. It makes sense to try your hand at a small pilot project. Even small steps are purposeful for data-driven projects. Only in mature data infrastructures do algorithms eventually enable autonomous evaluation using artificial intelligence or machine learning.
Source: Whitepaper "Value of Data"
