Data wrangling, also known as data munging, is the process of cleaning, transforming, and organizing raw data for further analysis and integration. In this article, I will explore the significance and benefits of data wrangling, the process of making raw data usable for different end users, and the application of data wrangling in AI and machine learning. Let’s start by taking a closer look at data wrangling and its significance in finance.
According to Statista, the market for big data is predicted to reach 103 billion dollars in 2027, making unlocking its full potential more relevant than ever. As big data continues to evolve, companies continue to find new data types. However, as technology creates more data sources, data management becomes a growing obstacle for businesses.
Likewise, Forbes reported that data scientists responsible for data preparation spend approximately 80% of their work hours preparing data for further analysis. Included in this 80% is data wrangling.
Importance of data wrangling
Having an in-depth understanding of data wrangling is essential for businesses and investment firms, as data wrangling is a precursor to many other data transformation and analysis processes commonly utilized by financial professionals.
For instance, investors can leverage job postings data to understand market growth better and track promising start-ups. Similarly, companies can utilize firmographic data to better understand their competition. Both objectives require significant amounts of data to be collected and transformed for further analysis or integration. Ultimately, these objectives cannot be achieved smoothly without data wrangling.
3 benefits of data wrangling
As previously mentioned, big data has become integral to business and finance today. However, the full potential of said data is not always clear. Data processes like data discovery are useful for recognizing your data’s potential. But to fully unleash the power of your data, you will need to implement data. Here are some of the key benefits of data wrangling.
1. Data consistency
The organizational aspect of data wrangling results in a more consistent dataset. Data consistency is crucial for business operations that involve collecting data input from consumers or other human end-users. For example, if a human end-user submits personal information incorrectly, such as making a duplicate customer account, it would consequently impact further performance analysis.
2. Improved insights
Data wrangling can provide statistical insights about metadata by transforming the metadata to be more constant. These insights are often the result of increased data consistency, as consistent metadata allows automated tools to analyze the data faster and more accurately. Particularly, if one were to build a model regarding projected market performance, data wrangling would clean the metadata to allow your model to run without any errors.
3. Cost efficiency
As previously mentioned, because data wrangling allows for more efficient data analysis and model-building processes, businesses will ultimately save money in the long run. For instance, thoroughly cleaning and organizing data before sending it off for integration will reduce errors and save developers time.
Now that I’ve examined the objectives, significance, and benefits of data wrangling, let’s explore the more granular details of data wrangling and its relation to machine learning (ML).
Data wrangling formats
Depending on your data type, your final result will fall into one of four final formats: de-normalized transactions, analytical base table (ABT), time series, or document library. Let’s take a closer look at these final formats, as understanding these results will inform the first few steps of the data wrangling process, which I will discuss later.
Transactional data
Transactional data refers to business operation transactions. This data type involves detailed subjective information about particular transactions, including client documentation, interactions, receipts, and notes regarding external transactions.
Analytical Base Table (ABT)
Analytical Base Table data involves data within a table with unique entries for each attribute column. ABT data is the most common business data type, consisting of various data types contributing to the most common data sources. Even more notable is that ABT data is primarily used for AI and ML, which I will examine later.
Time series
Time series data involves data divided by a particular amount of time or data related to time, particularly sequential time. For example, tracking data regarding an application’s downloads over a year or traffic data over a month would be considered time series data.
Document library
Lastly, document library data involves much textual data, particularly text within a document. While document libraries contain rather massive amounts of data, automated data mining tools designed explicitly for text mining can help extract entire texts from documents for further analysis.
Data wrangling tools
Data analysts and other professionals use various data wrangling methods, ranging from munging data with scripts to using spreadsheets. Additionally, with some of the more recent all-in-one tools, everyone utilizing the data can access and utilize their data wrangling tools. There also are visual data wrangling tools for beginners that make it easier for people without programming skills.
Here are some of the more common data wrangling tools available:
- Excel – a simple yet powerful data wrangling tool
- Plotly (data wrangling with Python) – useful for maps and chart data
- CSVKit – converts data
- Tabula – regarded as an all-in-one munging solution
- Google DataPrep – cleans, prepares and organizes data
Data wrangling process
1. Discovery
Before you can start the wrangling process, it is critical to consider what may lie beneath your data. It's crucial to think critically about what results you anticipate from your data and what you will use it for once the wrangling process is complete. Once you've determined your objectives, you can gather your data.
2. Organization
After you've gathered all the raw data within a particular dataset, you must structure it. Due to the variety and complexity of data types and sources, raw data is often overwhelming at first glance.
3. Cleaning
When your data is organized, you can begin cleaning it. Data cleaning involves removing outliers, formatting nulls, and eliminating duplicate data. It is important to note that cleaning data collected from web scraping methods might be much more tedious than cleaning data collected from a database. Essentially, web data can be highly unstructured and require much more time than structured data from a database.
4. Data enrichment
This step requires that you take a step back from your data to determine if you have enough data to proceed. Finishing the wrangling process without enough data may compromise insights gathered from further analysis. For example, investors looking to analyze product review data will want a significant amount of data, accurately portraying the market and increasing investment intelligence.
5. Validation
After determining you have gathered enough data, you must apply validation rules to your data. Validation rules, performed in repetitive sequences, confirm that your data is consistent throughout your dataset. Validation rules will also ensure data quality and security. This step follows similar logic in data normalization, a data standardization process involving validation rules.
6. Publishing
The final step of the data munging process is data publishing. Data publishing involves preparing the data for future use. This may include providing notes and documentation of your wrangling process and creating access for other users and applications.
It is also important to note that, like many other data transformation processes, data wrangling is an iterative process requiring you to revisit your data regularly. To better understand the munging process, let’s look at data mining, a subset of data wrangling.
Data mining vs data wrangling
At first glance, data mining and data wrangling appear nearly identical, especially if you are not a data scientist. However, as previously mentioned, data mining is actually a subset of data wrangling. While they have similar benefits, they both achieve different data processing objectives.
Data mining is defined as the process of sifting and sorting through data to find patterns and hidden relationships in larger datasets. Data wrangling requires a few more steps, such as cleaning, enriching, and integrating, transforming raw data for deliverable insights. Additionally, data wrangling removes inadequate data from the dataset.
Ultimately, data munging enhances the data mining process, providing companies with powerful insights and hidden patterns regarding customer behavior, market trends, and product feedback.
Data wrangling and machine learning
Data wrangling is a crucial component of machine learning. According to InfoQ, 60% to 80% of the machine learning pipeline involves data preparation and data munging. More specifically, data munging analyzes data that feeds into the machine learning model you are building. Without data munging, the model wouldn’t have clean data to process, and therefore, the model would produce false predictions, failing its objective.
Likewise, as the AI software market increases, with a reported market revenue of 10.1B in 2018, which is still growing today, machine learning has become increasingly prevalent in the world of finance. However, because data wrangling has yet to be 100% automated, machine learning has yet to reach its fullest potential.
The complexity of metadata management and the sheer amount of processing power required to wrangle data properly are the main contributors to this machine learning roadblock. Because machine learning requires extensive data wrangling, companies are eager to find the highest-quality automated data wrangling tools.
Closing thoughts
Ultimately, data wrangling is an excellent data preparation method for businesses looking to utilize AI, machine learning, and automated data analysis tools or data analytics processes, among other applications. Data wrangling tools will look to AI for more solutions as data science evolves.
And when it comes to AI, you can already utilize clean AI-enriched datasets for companies, employees, and job postings.
