Post Menu and Details.
- The basics of the procedure
- Data wrangling examples useful for business
- Tools, techniques, and the end result
- Data Wrangling
- Summing up
Reading time: ~4 minutes
Anyone who has ever worked on data for business benefit can say at least two things about data. Firstly, data can be extremely valuable. Secondly, it can also be very messy. Dealing with a lot of raw data means having to work through its defects and bring some order into it before its full potential can be utilized. That is why data wrangling is such a crucial part of data management. Through this procedure, data is cleaned, organized, and unified into accessible formats. Check out Coresignal for a deeper understanding of how it works. And here we will look at data wrangling examples that matter for business.
There are multiple other names that are in some contexts used interchangeably with data wrangling, such as data munging or data remediation. However, in other situations, these names may be defined to refer to procedures that are subtly different and complementary of each other. This already points to the fact that data wrangling can be understood and done slightly differently depending on the requirements of particular cases and projects.
Standardly, however, data wrangling is understood as involving six steps each of which is fundamental procedures themselves. At the basic level, they can be described as follows.
- Discovery or acquisition of data, where one identifies the accessible data sets to be worked on and inspects them to get familiar with their features.
- Structuring or organization of data, which is the procedure of bringing some order into data by rearranging it into accessible formats.
- Cleaning involves removing various errors and issues in datasets.
- Enrichment or augmentation, where one has to determine whether there is enough data for beneficial usage or should it be enriched with data from additional sources.
- Validation – The process of repetitively applying validation rules to make sure that data is consistent and of high enough quality.
- Publishing involves providing access and otherwise preparing data for further usage.
All of these steps of the wrangling procedure encompass various practices, which raise the value of data assets. This allows for many data-wrangling examples that directly promote business benefits.
Firms today store or otherwise have access to a lot of raw data. Sometimes it is assumed that analysis of all such data is the straightforward path to business insights and benefits. However, before data analysis comes wrangling, which is critical in setting the stage for beneficial analysis.
In fact, without correctly done wrangling procedures, data analysis may be harmful rather than useful. For example, if the decision to enter a new market is based on research that uses less than representative data on competition, it can result in considerable losses and reputational damage. This is prevented by either enriching data or barring its usage for such research.
Here are some more data wrangling examples that go to show just how important it is to properly prepare data before using it for business and investment insights.
- Applying cross-system consistency check rules identifies inconsistencies in data as recorded in different systems of the same company. This helps to ensure efficient cooperation across platforms and departments by ensuring that, for example, everyone working with customer data has the same information.
- Other data validation rules, like format checks or various consistency checks, identify issues that might make data unreadable for specific AI tools. Removing such issues promotes automation of data handling which is both time-efficient and cost-efficient.
- Removing empty, incorrect, redundant, or irrelevant data saves memory space and prevents misconceptions about the volume of owned data.
- Transforming data to fit into a common structured format makes it more readable thus enabling various agents to access it and work for value extraction.
- All data-cleaning activities from correcting errors to removing outdated data points ensure that data conforms to a certain standard of quality. High data quality is necessary to be sure that computer modeling and machine learning as employed to forecast investment opportunities will make the correct predictions.
These are just a few cases in which high-quality data-wrangling advances analysis goals as well as a financial success for companies and investors. Another way data wranglers may boost analysis is by providing notes on the wrangling process when publishing data thus making analysts better acquainted with the data.
As with many other data handling and analyzing procedures that are crucial in business, data munging is done better when proper tools are utilized.
From the above-mentioned data-wrangling examples, one might have gathered that computer programming is involved in the process. Especially in the validation process, where computers check data according to commands known as validation rules. Wranglers use such programming languages as Python which is considered a good choice for wrangling tasks.
Later on, interactive data visualization systems were advanced enough to enable non-programmers to do wrangling as well as making the process easier for programmers. Such systems aim at assisting data wrangling with clearer data visualization, as well as integrating various tasks of data wrangling to allow cooperation.
Some basic useful tools for wrangling include Excel which can be further assisted by data transformation in Tabula. Additionally, there is CSVkit which helps to convert data into different formats.
The end result, that is the structure of the data and the format in which it will be presented for further analysis will depend on the types of data that are analyzed and merged into final unifying forms. For example, if time is an important variable in the data, like in data on seasonal sales revenue changes, the end result should be presented as a time series. Textual documents, on the other hand, have no clear and unifying mathematical variables, therefore they end up in the document library until further qualitative analysis.
The multifaceted importance of the wrangling procedure should be clear from the data wrangling examples mentioned above. Data wrangling can be considered at once a data management procedure, careful preparation for analysis, and the first wave of analysis in itself. Therefore, the benefits to businesses and investors brought by this procedure also come from many sides and in many shapes.
Thank you for reading!