Five Use Cases for Data Wrangling

In order to uncover the full potential of data that they store, businesses use various procedures to prepare it. These procedures may have many different goals, from cleaning the data to summarizing it into a more accessible form. However, there are also multi-step processes that entail many of these goals. Data wrangling is one such process. Through this procedure, also known as data munging, data is cleaned and organized for further analysis and usage. Let us look at how it helps to utilize data in business and finance.

Data Wrangling

How it is done

Data wrangling is meant for transforming raw data into a format that makes it easier to read and use it. Preparing data in such a way makes it straightforward to extract the desired value from it and see the underlying message of data.

Of course, having in mind what specifically we want to gain from the wrangled data allows us to direct and handle the process in a particular way to ensure the desired result. However, there are six steps common to the data wrangling process that make-up its framework, which then can be adjusted for precise needs. These steps are as follows.

1) Discovery

The initial stage is where you specify your objectives for the entire procedure. One has to consider what sort of information is expected to be found within data and its most relevant aspects to the present goals.

2) Structuring

Raw data that is being used will usually be unstructured and come in various formats. Thus, it is important to organize the data before going forward and give a certain order to it. This is what structuring does, by putting all the data in a format that is deemed to be most suitable for the data and purposes at hand.

3) Cleaning

When data is properly structured, it is easier to clean as the data set defects will manifest clearly. Data is cleaned by removing duplicates, empty fields, and all other apparent errors, thus improving the quality of the dataset.

4) Data enrichment

As the data is well-structured and clean, there comes a time to determine whether this data is enough to answer the questions that interest us. It might be decided that the amount and type of data suits our purposes adequately. Alternatively, additional data might need to be added to enrich the dataset and ensure the final results’ value.

5) Validation

This step further advances the quality of the data. By applying certain validation rules, it is ensured that the data is consistent throughout the dataset, thus removing potential leftover errors from the previous steps.

6) Publishing

The final step of data wrangling is where data is prepared for further use. This is done by documenting the steps of wrangling and providing notes and access for the future users of the data.

The important use cases of data wrangling

Now, as it is clear what data wrangling is, it is time to look at what it is for. Here are some of the most important use cases for data wrangling in business and finance.

1) Financial insights

Financial analysts use data wrangling to uncover important insights for investment opportunities. When specific questions about the markets and industries are formulated, data wrangling will be the process of methodically answering those questions to inform investment decisions.

2) Improved reporting

Various departments in financial firms and other businesses constantly need to report their general results or some specific information. However, when data that shows these results is raw and unstructured, it may be hard to properly convey the information. Data wrangling improves the quality of reports, assuring that management reaches the correct understanding of information.

3) Unified format

Different departments or parts of a company may use different systems to capture their data. Data wrangling allows to unify the data and see the results of all branches in a single format.

4) Understanding customer base

As customers are different people, the data on them may be very varied. Data wrangling helps to see the underlying patterns and similarities between customers for certain products, which leads to a deeper understanding of the customer base.

5) Data quality

The general use case for data wrangling is improved data quality. Whether you are a financial analyst or a manager of a marketing department, you need your data to be of high quality to derive insights from it—the multiple steps of data wrangling help to achieve just that.

Summing up

Data wrangling is clearly a process that requires considerable time and effort, as it involves many steps that cannot be overlooked. However, this procedure also achieves multiple goals of data quality, accessibility, and answering particular questions. Thus, at the end of the day, it would usually turn out to be cost-efficient and worth the effort.


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