How to Face the Nightmare of Data Cleaning?

Data analytics is one of the sexiest jobs of the 21st century. Aspiring data scientists and analysts are taking courses of python training in Delhi and other cities for a lucrative career in this industry. They imagine themselves to be at the forefront leading their respective companies forward by driving insights through data analysis. They think of spending every moment of their time driving new methods to improve their company’s operations. However, is this really the work data scientists currently are doing?

A survey done by CrowdFlower suggests that data scientists spend 60 percent of their time cleaning and organizing data, rather than driving insights from it.

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76% of data scientists find this as the least enjoyable part of their jobs. Every day more than 2.5 quintillions of data is created, out of which more than 60% is of no use. To clean and organize such amount of data is a daunting and painful task. Moreover, research from MIT suggests that big data is costing enterprises 25 percent of the revenue as cleaning the data is both time and cost consuming.

Why is Data Cleaning a nightmare?

Cleaning the collected data is a menace due to:

  • Inconsistency in Data Format: Enterprises today work with huge amount of data that is both structured and unstructured. While cleaning structured data is an easy task, problem arises when it comes to unstructured data. It includes irregularly formatted data, missing data and irrelevant data analyzing which is a waste of time. Some of the unstructured data also includes important information that needs to be analyzed like audios, videos, presentations, XML documents and webpages.
  • Amount of Data to Be Cleaned: The volume of data that enterprises deal with on a daily basis is in the range of terabytes. To make sense out of such huge amount of data coming from different sources in a variety of formats is hard.
  • Cleaning Process Is Tricky: Cleaning data requires removal of unwanted duplications, removing and replacing missing entries, ensuring consistent formatting, correcting misfielded values and other tasks which take a considerable amount of time. After cleaning the data securing it is also important. A log of the entire process must be maintained to ensure that the right data is transferred to the right place.
  • Outsourcing the cleaning process is expensive: Provided that data cleaning is hectic, many enterprises tend to outsource these jobs to third-party vendors. This reduces the time taken but increases the overall cost of the process. Many enterprises cannot afford to hire these vendors, so they rely on their data scientists to accomplish these jobs.

How to Tackle this Nightmare?

Now that we know that data cleaning is the most annoying part of this amazing job, here are some tricks to tackle this problem:

  • Data Auditing: In this process, a sample of data is taken from the sources to find errors and inconsistencies in the data. It can also be done by using the database and statistical
  • Defining Workflow and Mapping Rules: Defining workflow and mapping rules largely depends on the number of data sources are available. It also depends on the degree of heterogeneity and invaluable or pointless information in the data. Therefore, a large number of cleaning and transformation steps of the data must be executed.
  • Verification: In this step, the effectiveness and correctness of the data is checked. It is done by taking different data samples from each source. This step must be performed multiple times until the data is completely cleaned.
  • Transformation: The data is transformed by running the ETL or Extract, Transform and Load process in this step. Extraction of data involves taking it from various heterogeneous and homogeneous sources. Transformation involves processing them into a proper storage format for analysis and querying. Finally, loading involves inserting the data into the final target database.
  • Backflow of Cleaned Data: After the removal of all the errors, the cleaned data should replace the original unstructured data in the sources to give legacy application to the improved So that there is no case of redoing the data cleaning again in the future.

 In conclusion, you can say that while data analytics is the best job available in the market, there are some things which also make it a hard-working job. To get more idea on how to tackle the problems you might face as a data scientist you can join python training in Delhi at centres like PST analytics. Such centres offer first-hand experience on how to tackle these problems.

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