Data or more specifically Big Data has received much hype in the technology space lately, experts calling it the ‘new oil’ to be precise. The high computation power along with powerful machine learning algorithms has given companies a way to analyze such a massive amount of data in real-time and obtain meaningful insights from it. But, do you think that the data gathered from a variety of sources is instantly ready for analysis?
Well, you would be surprised to know that data preparation alone consumes around 80% of the total time involved in data analysis. The data collected by companies is in the raw format and contains duplicate values, missing values, inaccuracies, and other errors. Unless these errors are taken care of, you will obtain misleading conclusions on analyzing them. This is where data wrangling comes into the picture. It involves doing the required prep work that should be completed before data processing and analysis can take place. A lot of tools are now available in the market that addresses the quality issue of business data through data wrangling and transforms it into a digestible format where analysis can be started.
Data wrangling is an important part of data science and the concept should be covered in any Python Data Science course taken up by aspiring professionals. Read on to know more about data wrangling and why it is crucial for businesses.
An Introduction to Data Wrangling
Data wrangling, also known as data cleaning, is basically the process of transforming and then plotting data from one raw form to the other. It includes all the tasks performed on the data prior to the actual analysis, like weighing data quality and data context before converting the data into the desired form. The exact method followed for data wrangling varies for each organization and the kind of project they take up.
The process of data wrangling may include merging multiple data sources into a single dataset for the purpose of analysis. If there are missing values, they are either filled or such entries are removed. The dataset may also contain redundant or duplicate values and are removed if they will not create any impact on the analysis. Professionals conducting data wrangling further identify extreme outliers in the dataset and either remove them or explain the discrepancies.
Data wrangling can be conducted both manually and automatically. Many companies still have a manual data wrangling process and may or may not involve a team of data wranglers. Usually, for small companies, the task of data wrangling is left to the data analysts. However, if the datasets are exceptionally large, it becomes necessary for enterprises to adopt automated data cleaning.
Though companies have a unique approach towards data wrangling, Harvard Business School has mentioned the six basic steps followed generally.
Data discovery is the first step where data wranglers familiarize themselves with the business data and understand how it needs to be used. Here, they may find some initial patterns and issues with the data. Data structuring is the second step where the raw data, which initially has no definite structure, is restructured according to the analytical model used by the organization. The third step is called data cleaning where inherent errors in the datasets are removed. As discussed earlier, there are missing values, redundant data, or duplicate values in the dataset. This is the step where such values are dealt with and errors are minimized to a large extent.
The fourth step is data enriching where data wranglers determine if all the data necessary for the project is available or not. Though the raw data has been transformed, the datasets might need to be enriched or augmented by incorporating values from other sets. The fifth step is data validation where it is ensured that the data is both consistent and of high quality. It can be performed through automated processes for which knowledge of programming is essential. The last step is data publishing where the data is finally made available to the professionals for analysis. The format in which the cleaned data is shared depends on the organization’s goals.
Benefits of Data Wrangling
When professionals go through data science training, they are given clean data in their industry projects, where it becomes easy to analyze it and predict future trends. In an enterprise environment, however, the data is quite messy and a lot of time is consumed in data cleaning itself.
So, data wrangling is an important step that ensures that the data used for predictive analytics, machine learning, business intelligence, or other analytical applications has enough quality standards that can produce reliable outcomes. The process helps in avoiding duplication of efforts in preparing data that is used for multiple applications. Organizations are able to find and resolve issues that would otherwise go undetected and affect the trends. Finally, they are able to extract more business value and a higher ROI (return on investment) from their analytics and business intelligence initiatives.
So, if you are embarking on a career in data science, make sure to gain expertise in data wrangling first.