Data Life Cycle
We can bring data to life through a few steps, which we will discuss shortly. But I wanted to highlight that, this is by no means the only data life cycle out there. This cycle may vary based on the nature of your problem and your need for the data among other factors.
My promise to you in this article you will learn about a generic cycle that a data analyst may approach to find the best answer for the quest.
Planning: in planning, analysts tend to take a longer time in comparison to the remaining steps, due to constructing the guideline for the whole process. And deciding what kind of data they need, e.g. quantitative, qualitative data,… etc.
Who will be responsible for it? And where to store the data? shaping the big picture in addition to prioritizing different tasks.
Capture: since the analyst knows the data type to be obtained, it’s time to figure out which kind of resources to utilize, whether is it available within the company or has to be found outside. On that foundation, choosing the right tools to collect the data will become at ease.
Also, important concerns are addressed during this phase to ensure the integrity, privacy and credibility of the data. At the end of the day, no one wants to work with corrupted data.
Manage: storing data has become effortless unless it is done manually! Nonetheless, in data analysis, the main technologies used are SQL Databases and Spreadsheets. Of course, data security is vital at this point. Not only that but also to make sure as we manipulate the data and prepare it for analysis, to keep it intact.
Analyze: moving on, data now has been developed to work with, it’s time to support the business goals, solve problems or improve current processes. Everything that motivated us early in the process, in this phase will be addressed.
This can be done with the help of programming languages, such as R or Python. If the dataset is not complicated, the functionalities that come with spreadsheets may do the job.
Archive: Now that problems have been solved! The next step is to stack the data away for reference, as it would be beneficial for overtime comparison. However, the length of time that analysts keep the data varies according to how often this type of data changes over time, among other factors.
Destroy: as anything in life, has to come to an end! Data also has to be destroyed. Naturally, the irrelevance of the data drives this action. Again, caution should be practiced to guarantee it won’t fall in the wrong hands.
Alternatives
Harvard Business School (HBS) developed their version of the data life cycle to suit its needs. The U.S. Geological Survey (USGS) also has its own version too.
Each company or organization formulates the suitable life cycle, that serves them best. Accordingly, the focus and the importance will be pointed towards a specific stage, certainly without ignoring the rest of them.
Conclusion
In the final analysis, the above-suggested life cycle could be a go-to solution for a good number of situations. However, you can tailor it or even consider a different cycle as there are quite a few options that may be adequate for your purpose.
Thank you for reading until the end. Your comment, is welcomed and much appreciated.
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