Dextrus Solution
Data Wrangling
Data Wrangling with Dextrus
Data wrangling is the method of transforming and mapping data from one "raw" data form into another format with the aim of transforming it into more accurate, appropriate, and valuable form a variety of uses and purposes such as analytics and machine learning modeling. It is the process of structuring, organizing, enriching, and cleaning extracted data so that it can be used for various purposes.
Raw data is a piece of repository information that has to be processed or integrated into a system. It can come in any format like text, photo, Databases, etc.
Data wrangling can also be called data munging, which is the most time-intensive aspect of data processing. According to top industry data scientists this process can take up to 75% of the time of an entire project. It’s time- intensive because accuracy is essential since this data is pulled from various sources and then often used for many purposes.
Data wrangling includes:
- Fetching data from different sources and putting it into a single space.
- Integrating data together.
- Removing anomalies and errors in Data and replacing them with proper Data.
There are various sources from where data can be imported in Dextrus Data wrangling:
- Local system (.csv, .xls files)
- Cloud: From any cloud server we can extract
- Ms SQL
- Oracle cloud
- Amazon S3 bucket
- Amazon Redshift etc.
- Project (Data can be extracted from different other projects present in application)
Dextrus Data Wrangling Module involves Data Recipe creation followed by Importing Data Set and Sampling of imported Data. After Sampling, the process of transformation of Data takes place which consists of the following:
Data Removal
Data Replacement
Data Masking
Column splitting
Identifying Outliers
Percentile
IQR
Rule-Based
Importance of Data Wrangling
- Data wrangling is important because it’s the only way to clean and structure raw data and make it into use.
- Data extracted from different sources, different systems lead to Data mess-up which involves data duplication, incorrect data, or data that can’t be found to be used.
- A good data wrangler will be able to interpret, clean, and transform data into valuable insights and help us understand the business scenario. Dextrus tool can be used efficiently here to clean and transform Data because of its all features, as discussed above.
- Data Wrangling in Dextrus enables Data Privacy and Security which reduces the scams and frauds with Data. It can mask up the Data needs to be secure and can also perform encoding.
- An outlier is a study that gives an abnormal distance from other Data values from a random sample. Good Data Wrangling can identify outliers from the Data set.
Discovering
Data discovering is needed to understand the actual context of the Data, before cleaning. It is important what the data is all about and what purpose is the Data going to be used for. It gives the best approach to analyzing data.
Structuring
As we get Data from different sources and of different formats, Data sets are usually unorganized and unstructured. It is very difficult to understand and use such Data. Such, data needs to be structured in a proper way to be used. Based on step one, you can come to know how to categorize, separate and structure data based on their context of use.
Cleaning
To make high-quality data and process with the most accurate Analysis, Data cleaning is crucial. Cleaning involves removing duplicates, null values and relies on formatting to make data high quality. As discussed above, Dextrus is having the properties to remove data, replace data, and split the columns as per the requirement.
Enriching
After only cleaning, the Data is not ready to compute further If needed, additional data can be added to Enrich the Data set, then you can enrich the data by adding more information. You can analyze existing data to derive what additional information is to be added.
Validating
To ensure the accuracy of Data after cleaning and enriching it and making sure that Data is valid and credible is the process of Validating Data.
Publishing
After completion of all the above processes, the cooked Data and information need to be shared and published. So this is the process to publish the completely cleaned Data.
How Dextrus Can Help
Data Wrangling is one of the most important steps of Data Analysis as it turns raw data into actionable information. Dextrus enables all the steps and activities that are used to process Data Wrangling efficiently.
- Reduces Time: Before Analysis, as we do all the transformation processes, it saves time to visualize Data and create insights. It gives better decision-making in a shorter period.
- In-depth Intelligence: In every aspect and every dimension of Business, Data is used which can impact different departments. After extracting data and performing the data wrangling process, one can understand the current status of a Business.
- Accurate and understandable Data: With good data wrangling, you will have correct data, in turn, you can rely on it to take action as per the requirements.
