If there is one mantra that both businesses and citizens hear on a daily basis, it is that the answer to everything is in the data . The topic of the 'new oil' is on everyone's lips. It is not surprising, then, that one of the most in-demand professions in today's job market is that of Data Manager. Let's find out what it is about and what its prospects are.
What is a Data Manager?
Nowadays, more and more companies are using computer data collection to plan their strategic objectives. This data can be useful for multiple purposes, such as gaining insight into customer behavior or implementing more efficient business processes . The professional figure who coordinates, analyzes and manages this data is precisely the Data Manager, a highly sought-after specialist who can operate in any sector.
Generally, it is medium and large-sized companies that peru whatsapp data need to process considerable amounts of information and therefore require a Data Manager. The sectors in which Data Managers usually find employment include:
Financial organizations
Publishers and media
Film studies
Retail chains
Insurance companies
Health organizations
Educational institutions
Public service companies
Social media companies
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Functions of a Data Manager
The new General Data Protection Regulation (GDPR) guidelines have made the Data Manager a key figure in implementing the directives regarding the transmission of data within a company, as well as its life cycle. Their functions include accessing, analyzing and storing the data necessary for research , the ultimate goal of which is to facilitate and make business decisions more informed, whether strategic or daily management.
The challenge that most Data Managers face is to make the most of the potential of analysis and to be able to implement its results within business processes. In fact, it is the integration of analysis with data capture that ensures a greater probability of success during this phase of management. Data alone is not enough and must be subject to quality control, which is often carried out on two levels:
- Through manual data entry based on the results of qualitative indicators, which are tasked with pointing out possible problems and verifying the consistency of the format.
- Through data documentation , that is, defining its context, content and parameters.