The first thing one notices about the world is that there is data—a lot of it, it appears. And the issue isn't so much about the data itself, which is unstructured raw nibbles or snippets, as it is about the final information that that data represents or will become through refinement.
That dirty task (pun intended) of converting raw or formless data into valuable information is for the data analyst to trawl through in a procedure that some analysts toss around far too easily, like galaxy patrol policemen with a snazzy blaster on their utility belt that they touch too much... "wrangling." Wrangling is the process of transforming data from its basic fragments into useful information for use in things like model training, forecasting, and other applications that require insights. Keep in mind, Officer, that the goal is always the insights. Your blaster and your Insights badge are the end game definers.
The primary steps in data analysis are the extraction, assessment, cleaning, analyzing, visualization and sharing of it. These steps, in this predictable order, are collectively known as data wrangling. They form an approach that can be used to convert all kinds of data into structured and meaningful insights. But how does that work for someone who is new to this, or trying to make sense of what they have been immersed in for a while?
Let us look at the ordered steps in data analysis with a bit more detail in order to gain more clarity.
• Extraction — Every data analyst knows Data sourcing can be difficult in its own unique way. Knowing how to open, read, and write files is just as vital as knowing what tools to use and how to go about retrieving what you want. As a data analyst, this is critical. Most organizations will provide you access to their lakes and warehouses, and you will be proficient in all types of SQL and cloud architecture as needed, but there will be times when you will have to scrape or knock on doors with APIs. Knowing how to get the data you want the right way will make things easier for you.
• Assessment — Exploring what a data set consists of helps in the way an analyst will approach and prod it for information. Ostensibly, viewing data in document readers is helpful and expected, but programmatic assessment can dig deep in the way that only computers know how to. It is useful for asking questions about the data, as well as monitoring and noting the factors involved. What are the obvious relationships? What does the data set have in common? Are there any patterns? Outliers? Assessment allows you to see how well-organized, relational, and sensible the data is, and it might point out areas that need to be improved in the following step.
• Cleaning — This is, without a doubt, the most difficult and time-consuming aspect of any analysis. Dealing with dirty data and associated tidiness concerns can consume up to 80% of an analytics project's time. This step must be done with regard to the need for entries statistically, as well as the effect it will have in terms of information. This is the first iterative step and an analyst may always come back to clean and get exactly what they need later on. Make it clean, but do not over clean. Maintain the data standards for tidiness and cleanliness as you work: every row forms an observation, every column forms a variable, and every entry is a piece of data. If you get this part of analysis wrong, everything could go belly up. Get ready to change data types, to categorize, to cut, to bin, to transform, to reshape—to get really immersed in the dataset. Its benefits lie in making the next phase relatively easy.
• Analyzing — Exploring the data provides the initial exploratory or explanatory insight into the relationships that exist between the variables in the data. Parallels are identified, discoveries are made, and new questions are raised. Sometimes it is necessary to reassess, clean again, and extract something else. All of this is done in order to make the most of the data that is available and achieve relevant outcomes.
• Visualizations – Without them, little good can come from analysis. This is the reason for everything you've done so far, and it's the data that comes out of the data funnel's end. At this moment, the sweat and tears appear to have meaning. It only makes sense for the analyst to go to the next stage once the necessary visuals have been completed.
• Sharing — The best part is sharing the insights and knowing that the newly formed information will make a difference in its own way, you and your client will be happy with each other at this point.
Data analysis has to be one of the most interesting and diverse things one could get their hands on. Its tasks give you a taste of everything, whether it's dealing with medical records, music hits, or preparing various datasets for machine learning training. You may not see what you want to see in the analysis, but that just goes to show why it is vital to analyze: what you believe you know and what you know you don't know and want to find out are all often quite different from what is actually the case. Officer, put your blaster in your utility belt; there's a lot of work to be done.
With this light insight into data wrangling, you should have a reinforced brief on the entire zeitgeist of data analysis.
Feel free to reach out!