Data Wrangling: Speeding Up Data Preparation

Data has the potential to change the world. But before it does, it has to go through a fair amount of processing to be ready for analysis. A critical step in this processing is data wrangling. Data wrangling is a non-technical term used to describe the crucial cleaning and sorting step of data analysis. Specifically, data wrangling is a process that manually transforms and maps raw data into various formats based on specific use cases.

While the process is not considered glamorous, data wrangling is the backbone behind understanding data. Without it, a business’s data is nothing more than an unorganized mess — difficult to read, impossible to access, and unlikely to be analyzed in a useful manner. It’s no surprise, then, that  Data scientists dedicate 80% of their time to data wrangling.

In this article, we will help you understand data wrangling, the steps involved in the process, and common challenges many businesses face when implementing an effective data wrangling strategy.

Why is data wrangling necessary?

The data wrangling process includes manually gathering, sorting, cleaning, and organizing data for analytic use. Pieces of data are carefully checked or removed if they contain inconsistent dates, outdated information, and technological or social factors that don’t fit the desired outcome. In effect, data wrangling identifies the most valuable information in the data, given the parameters and goals of an individual business.

While it is a time-consuming and tedious process, wrangling is a fundamental step that organizes historical data, trends, and other time-sensitive information to make informed decisions, understand a customer base, and improve operations. Data wrangling can also uncover little-known insights from data analytics to improve processes like reporting and other business functions.

Data wrangling steps

With data streaming at breakneck speed, data wranglers have their hands full determining what information is most important, as well as the relevant focus areas based on business criteria. Once that has been determined, there are six main steps to the wrangling process.

• Learning: What’s in your data and how do you want to use it? Key pieces to beginning the process of wrangling involves fact checking, understanding where data originated, and if it has been updated and verified.
• Organizing: Raw data comes in many different formats. It must be organized with specific analytic needs and uses in mind. One column might turn into three or, rather, into a number of rows, depending on the end-use.
• Cleaning: Are there data errors, duplications, or outliers? A simple example of this is state abbreviations, which could be entered many different ways: WI, Wis, or Wisconsin. Once a piece of data is cleaned, all current and future data will be formatted in the same way.
• Enhancing: Consider your data’s undiscovered treasures. In other words, the relationship between different pieces of data, or where they originated, could create new ideas for business intelligence that were not previously transparent.
• Authenticating: Data must be verified for consistency, quality, and security.
• Exporting: The final step is to prepare the data for a specific user or software. The easier the data is to access and use, the better the insights will be for implementation.

Data wrangling challenges

The greatest challenge in data wrangling is its time-intensive nature. This is in part due to the detailed structural nature of the analytic data sets that are produced from the process. As you can see below, each of these structures hold a great amount of information, specific to an individual use case.

• Analysis Base Table (ABT): When machine learning is required, ABT is used to help analyze tables of data for patterns, as well as to predict outcomes. Data is sorted into rows with columns that identify information about each entity, like characteristics, history, or how it relates to other entries.
• De-normalized Transactions: Business transactions that are used for operations like a customer call or medical records — where details of customer or patient history might help to resolve a current issue. Transactions are summarized for online analytical processing (OLAP) data marts, which are in turn used by business intelligence.
• Time Series: This structure follows one or more characteristics of one entity over time and could include seconds, weeks, or years. This structure follows trends over time, for example, a monthly average of the S&P 500 index.
• Document Library: A place where mostly text documents used for text mining analysis are stored.

Although there are significant issues that surround delays related to producing these data sets, such as added workload, shifting priorities, many more challenges arise when trying to implement data wrangling.

• Making sense of the end result: The questions asked of a piece of data depends on its end use. A data wrangler must consider things like which entities are important, whether the data is needed for immediate analysis, or if it will be used to reflect trends over time. If it will be used historically, what is the time period involved? These are the types of questions a data wrangler must clearly define.
• Data access: A data wrangler should have direct permission to access the data, if not, more instructions will be required when data is needed. Working around these policy rules can be time-consuming.
• Clean data: Different entities that are actually the same must be cleaned, or de-duplicated. For example, a customer named Bob Smith, could be B. Smith. That same customer could have more than one account or a shared account with family members. All these things need to be reconciled before using.
• Source data relationships over time: Understanding how data entities are related to one another takes a significant amount of time, effort, and verification. Using a data warehousing model can help sort this out more quickly.
• Manual data integration: Some data comes from sources, like hard-copy documents or spreadsheets, which need to be manually added and organized in the system.
• Specific engineering: Data must be transformed into “features” that can be used in machine learning. There are a myriad of questions that must be answered at this step, including how often the entities can be used and when, amongst others.

Wrangling in the cloud

Although it may seem that effective data wrangling requires a significant amount of time and energy to set up, a comprehensive solution can help you gain essential analytic results for business intelligence quickly. Talend Data Preparation provides a governed self-service platform for business users and IT personnel to collaborate and prepare data that the entire organization can trust. Harness the value of your data faster. Download a free trial of Talend today.