We live in the era of information, technological development, Big Data and Business Intelligence.
Therefore, the ability to analyze data and convert it into actions and information of value for decision making and the achievement of objectives has become one of the most valuable skills in business.
It is a skill that drives modern economies. In fact, according to forecasts by the World Economic Forum, by 2020 data analysts will be in high demand in companies of all types and sizes globally.
However, not all data is the same and the same mindset is not applicable and useful in all contexts. As a data analyst you may encounter different methodologies and types of analysis beyond qualitative and quantitative: correlation, regression, visualization, data mining, linear, etc.
Each of them is applicable to a specific activity. It is important that you know how to decide on which direction to focus the development of this skill, either to maximize the role you play in the economic growth of an organization or to ensure you remain relevant and employable in the years to come. Take note.
Prioritize types of data analysis based on time and utility.
Different types of data analytics require time to learn and represent specific utility for running a particular business, driving an industry, and even strengthening a nation's economy. Therefore, you should prioritize your career path by attending to these two variables. This would give you, in general terms, the following matrix of options:
1.- Those you should develop.
This refers to the types of analysis that are highly useful and require little time to learn. Undoubtedly you should prioritize this option because it is about adding value to your career - without the need to dedicate endless months to it.
Also, keep in mind that in the world of analytics, trends can change depending on the technological development and the tools used for the task, so it is ideal that you acquire the knowledge as soon as possible.
2. Those that require reflection.
These are highly useful but require a lot of time to learn. Think, for example, of the neural network technique. Taking into account the accelerated development of Artificial Intelligence and its implementation in companies for process optimization, it can be the most useful knowledge in the short term, but it is one of the most complex methodologies to learn and develop.
In any of the options in this category, acquiring the skill will mean putting it above other activities and learning; therefore, before opting for one of them you must be completely sure that the investment of time and money will be worth it.
3.- Those that you do not need, but of which you should be aware.
These are the ones that are of little use but do not require much time to learn. Since they are not an urgency at the moment and are easy to acquire, you should keep yourself informed about their use, because if they start to be useful, they will immediately become part of the first group.
4.- Those you should ignore.
Avoid considering those of low utility and high time to be learned, it is better to ignore them and not to dedicate effort to them.
In this classification the types of analysis in particular are not mentioned because, if we take into account that each one of them is valuable or insignificant depending on the context in which it is applied, it would be irresponsible to indicate as "useful" or "useless" one or another methodology.
The classification of the types of analysis in the categories described above, as well as their prioritization, must strictly attend to the context in which you operate.
But that is not all
Although the idea is that you should determine the types of analysis you should develop according to the context in which you operate, do not forget to pay attention to the demand. That is, survey the labor market and map out the specific skills you need based on the frequency with which they appear in job postings, press reports, expert projections and university academic programs.
If you need to change your course, don't hesitate to do so. Of course, be sure to rank the options according to the previously presented categorization: usefulness and time.
In my personal experience, by developing this classification or matrix -applicable to any profession- I have managed to acquire the skills that bring the greatest return on investment to my professional career and my knowledge of Business Intelligence. That is why I highly recommend it.
Therefore, the ability to analyze data and convert it into actions and information of value for decision making and the achievement of objectives has become one of the most valuable skills in business.
It is a skill that drives modern economies. In fact, according to forecasts by the World Economic Forum, by 2020 data analysts will be in high demand in companies of all types and sizes globally.
However, not all data is the same and the same mindset is not applicable and useful in all contexts. As a data analyst you may encounter different methodologies and types of analysis beyond qualitative and quantitative: correlation, regression, visualization, data mining, linear, etc.
Each of them is applicable to a specific activity. It is important that you know how to decide on which direction to focus the development of this skill, either to maximize the role you play in the economic growth of an organization or to ensure you remain relevant and employable in the years to come. Take note.
Prioritize types of data analysis based on time and utility.
Different types of data analytics require time to learn and represent specific utility for running a particular business, driving an industry, and even strengthening a nation's economy. Therefore, you should prioritize your career path by attending to these two variables. This would give you, in general terms, the following matrix of options:
1.- Those you should develop.
This refers to the types of analysis that are highly useful and require little time to learn. Undoubtedly you should prioritize this option because it is about adding value to your career - without the need to dedicate endless months to it.
Also, keep in mind that in the world of analytics, trends can change depending on the technological development and the tools used for the task, so it is ideal that you acquire the knowledge as soon as possible.
2. Those that require reflection.
These are highly useful but require a lot of time to learn. Think, for example, of the neural network technique. Taking into account the accelerated development of Artificial Intelligence and its implementation in companies for process optimization, it can be the most useful knowledge in the short term, but it is one of the most complex methodologies to learn and develop.
In any of the options in this category, acquiring the skill will mean putting it above other activities and learning; therefore, before opting for one of them you must be completely sure that the investment of time and money will be worth it.
3.- Those that you do not need, but of which you should be aware.
These are the ones that are of little use but do not require much time to learn. Since they are not an urgency at the moment and are easy to acquire, you should keep yourself informed about their use, because if they start to be useful, they will immediately become part of the first group.
4.- Those you should ignore.
Avoid considering those of low utility and high time to be learned, it is better to ignore them and not to dedicate effort to them.
In this classification the types of analysis in particular are not mentioned because, if we take into account that each one of them is valuable or insignificant depending on the context in which it is applied, it would be irresponsible to indicate as "useful" or "useless" one or another methodology.
The classification of the types of analysis in the categories described above, as well as their prioritization, must strictly attend to the context in which you operate.
But that is not all
Although the idea is that you should determine the types of analysis you should develop according to the context in which you operate, do not forget to pay attention to the demand. That is, survey the labor market and map out the specific skills you need based on the frequency with which they appear in job postings, press reports, expert projections and university academic programs.
If you need to change your course, don't hesitate to do so. Of course, be sure to rank the options according to the previously presented categorization: usefulness and time.
In my personal experience, by developing this classification or matrix -applicable to any profession- I have managed to acquire the skills that bring the greatest return on investment to my professional career and my knowledge of Business Intelligence. That is why I highly recommend it.