In the past period, many ‘old roles’ have been cancelled or will expire in the coming period. This has everything to do with the rapidly changing world. Like it or not but staying relevant is becoming more and more challenging.
There are a number of roles that we notice here and that we believe are increasingly in demand.
Data Cleaner/ Data Cleaner
Good data hygiene is so important for business. If your data contains inconsistencies or errors, you can be sure that your results will also be flawed.
Cleaning up data is probably the most important part of the data analysis process. However, good data hygiene is not just about data analysis; in any case, it is a good practice to maintain your data and update it regularly. Clean data is a core principle of data analysis and more generally in the field of data science.
You can think of;
- Removal of unwanted observations
- Repairing structural errors
- Remove unwanted exceptions
- Correcting conflicting data errors
- Preventing syntax errors
- Dealing with missing data
- Validate dataset
The question is which entry level is desired. Is this hbo trained work or can a new labour potential be tapped? MBO educated or right the older generation who often has the peace and tranquility?
Do you want to have all the skills in-house or do you want to have access to all skills?
As a network leader, you are mainly concerned with maintaining an ecosystem that ensures that the organization can deliver. This may be that you hire externally or have a collaboration with other companies or professionals who have the desired skillset.
The network leader is actively involved in forming and monitoring the identity of the network and its strategic direction. The network leader steers on the right themes and activities and optimizes time and capacity. The network leader finds new initiatives and ‘collaborations between the participants of the network. The network leader comes into its own in an organization that operates as a network. This is also called Decentralized Autonomous Organization (DAO). DAO is seen as the future of organizations.
Organizations are increasingly using advanced analytics and artificial intelligence (AI) to improve decision-making in business processes. In order to be able to make advanced analyses or apply AI, highly specialized skills are needed. As a result, it is often difficult for a specialist to get the right information from stakeholders in order to be able to offer the right solution. You can already see this same phenomenon in the development of software. It is not for nothing that a Product Manager/Owner has been appointed to be able to make the translation from stakeholder to construction team.
Given the impact of advanced analytics and AI, it is therefore important that there is a clear translation of the business so that the Data gurus and AI specialists can offer solutions that contribute to the business processes.
You now see more and more the Translator emerging within organizations.
Translators play a crucial role in bridging the technical expertise of data gurus with the operational expertise of frontline managers. The Translators help ensure that the deep insights translate into high-impact solutions for the organization.
The Translator is not a technical role focused on Data architecture or modelling. A good knowledge foundation in areas of Data, AI and modelling is important and what is of great importance is knowledge of the organization or domain where the translator works.
Translators leverage their domain knowledge to help management identify and prioritize their business problems, creating the highest value when they are solved.