DATA MANAGEMENT CAREER

 Data management includes all disciplines involved in working with data and treats it as a valuable resource. A more formal definition is provided by DAMA International: Data resource management is the development and execution of architectures, policies, practices and procedures that adequately manage a company's data lifecycle needs. (KAMPAKIS, 2020).

Initially, the data needs to be contextualized and its meaning may change depending on the context. Data can be perceived as objective or factual - when they capture facts of natural phenomena being univocally equal regardless of who looks at them - or subjective - they reflect purely human or social constructions, which gain their right to representativeness from the general consensus, being, therefore, more abstract. Data interpretation is the essence of its business value and both types of data can provide different insights for different observers due to interpretation skills (COREA, 2019).

Another point to highlight is the quality of the data. Most of the time, data quality becomes an important issue. At other times, the main issue may be the volume of data, processing speed, parameters of an algorithm, interpretability of the results or any of the many other aspects of the problem. Ignoring any of them when it becomes important can completely compromise or invalidate subsequent results (GODSEY, 2017).

We should also not forget that a wide range of behavioral prejudices can invalidate the objectivity of the analysis that affects people. The most common among scientists and managers are: apophenia (distinct patterns where none exist), narrative fallacy (the need for series patterns of disconnected facts), confirmation bias (the tendency to use only information that confirms previous hypotheses) - and its corollary that the search for evidence will eventually end with the discovery of evidence - and the selection bias (the propensity to always use some type of data, possibly the best known). (COREA, 2019).

So, to talk about data management, we need to understand where the data comes from. By understanding where the data comes from, we generate a lot of data. Virtually everything you do generates some form of data. Companies, for example, collect data from internal sources, such as transactions, log data and emails, but also from external sources, such as social media, audio and video sources (KAMPAKIS, 2020).

However, the data alone does not make sense unless the right questions are asked. This is where human judgment comes in: asking the right question and interpreting the results are still the competence of the human brain, even though a precise quantitative question can be answered more efficiently by any machine (COREA, 2019).

The Information security engineer should work in collaboration with the information security team to offer support to security tools and technologies such as firewall, proxy server, remote access, and others.

There are two main types of data collection: observational and experimental. The collection of observational data means that the data is collected passively, with no attempt to control the variables involved. For example, collecting customer feedback for a book and a retailer analyzing customer behavior are methods of observational collection, as there is no attempt to control any variables. Experimental data collection involves designing and conducting an experiment, in which certain variables are controlled while you study other variables. This is more common in academic circles, but also in clinical settings. A perfect example of this is when a pharmaceutical company tests a new drug.