What is Data Wrangling and Why Is Data Wrangling Important?

Data wrangling is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. Data wrangling is increasingly ubiquitous at today’s top firms. Data has become more diverse and unstructured, demanding increased time spent culling, cleaning, and organizing data ahead of broader analysis. At the same time, with data informing just about every business decision, business users have less time to wait on technical resources for prepared data which is where data wrangling becomes valuable.

This necessitates a self-service model, and a move away from IT-led data preparation, to a more democratized model of self-service data preparation or data wrangling. This self-service model with data wrangling tools allows analysts to tackle more complex data more quickly, produce more accurate results, and make better decisions. Because of data wrangling abilities, more businesses have started using data wrangling tools to prepare before analysis.

Data Wrangling in Practice: What to Expect

There are typically six iterative steps that make up the data wrangling process.

  1. Discovering: Before you can dive deeply into data wrangling, you must better understand what is in your data, which will inform how you want to use data wrangling to analyze it. How you wrangle customer data, for example, may be informed by where they are located, what they bought, or what promotions they received.
  2. Structuring: This data wrangling step means organizing the data, which is necessary because raw data comes in many different shapes and sizes. A single column may turn into several rows for easier analysis. One column may become two. Data wrangling is made for easier computation and analysis.
  3. Cleaning: What happens when errors and outliers skew your data?  You clean the data. What happens when state data is entered as CA or California or Calif.? You clean the data. Null values are changed and standard formatting implemented, ultimately increasing data quality, which is the goal of data wrangling.
  4. Enriching: Here you take stock in your data and strategize about how other additional data or data wrangling might augment it. Questions asked during this data wrangling step might be: what new types of data can I derive from what I already have or what other information would better inform my decision making about this current data?
  5. Validating: Validation rules are repetitive programming sequences that verify data consistency, quality, and security. Examples of validation include ensuring uniform distribution of attributes that should be distributed normally (e.g. birth dates) or confirming accuracy of fields through a check across data. This is a vital step in the data wrangling process.
  6. Publishing:  Analysts prepare the data wrangling for use downstream – whether by a particular user or software – and document any particular steps taken or logic used for data wrangling. Data wrangling gurus understand that implementation of insights relies upon the ease with which it can be accessed and utilized by others. The data is now ready for analytics.

Designer Cloud was Built to Speed and Ease Data Wrangling

Designer Cloud was created explicitly to enable data wrangling, so that these steps become easier and interactive, and ultimately automated at scale as the system learns your data. With Designer Cloud’s breakthrough approach to data wrangling, analysts are empowered to interact with data in ways they never thought possible and leverage those insights for better decision-making.

Data Wrangling and the data wrangling tools can be extremely valuable for your business. Experience Designer Cloud’s innovated approach to data wrangling for yourself.

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