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.
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 allows analysts to tackle more complex data more quickly, produce more accurate results, and make better decisions.
Data Wrangling in Practice: What to Expect
There are typically six iterative steps that make up the data wrangling process.
- Discovering: Before you can dive deeply, you must better understand what is in your data, which will inform how you want 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.
- Structuring: This 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. Movement of data is made for easier computation and analysis.
- 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.
- Enriching: Here you take stock in your data and strategize about how other additional data 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?
- 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.
- Publishing: Analysts prepare the wrangled data for use downstream – whether by a particular user or software – and document any particular steps taken or logic used to wrangle said data. Data wrangling gurus understand that implementation of insights relies upon the ease with which it can be accessed and utilized by others.
Trifacta was Built to Speed and Ease Data Wrangling
Trifacta 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 Trifacta’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.
Experience Trifacta’s innovated approach to data wrangling for yourself. Try out Trifacta Wrangler for yourself.