Data Analytics – The Future Of Decision Making​

Data Cleaning – The Key To Improving Data Quality

    Through hygiene and cleaning practices, a restaurant maintains the cleanliness and quality of food. The same way your organization also needs to clean data to maintain its quality. Data cleaning is one of the most important tenets of data science.   

What Is Data Cleaning?

  This is the process of preparing raw data for analysis. After that, it is modified to remove any information that is irrelevant, incorrect, duplicated, and incomplete. This maintains the integrity and quality of the data so the organization can have accurate and valuable data.   

Implementing Data Cleaning

  Here are the steps your organization can take to implement an effective data cleaning process:  

Create A Strategy

  Cleaning comes later. First, you have to create a strategy to filter out data. This means to spot problems in the existing data and make it more relevant to what you need.  There is a vast amount of data within an organization. This is why the first step is to choose the datasets you will utilize. Here are some questions to help you create a successful strategy:
  • What is your core dataset?
  • What are you trying to achieve through this dataset?
  • Is the source reliable?
  • Were accurate methods used to collect this dataset?
  • Is it complete?
  • How can the quality of this dataset be tested?

Data Cleaning

  You need to survey the data, make sure it is complete, format, and correct it. This will help in improving the quality of your data and make it easier to clean.  This is the most important step and this is where you need to pay attention to detail for the data to be of high-quality. Correction and completion need to be done for valuable cleaning.  

Validate The Data

  Even after cleaning, you need to double-check the data you just cleaned. This is where you refine your data and try to see additional errors, so they can be dealt with. This may be a challenging process because of the amount of data. A way to overcome this challenge is to validate a small piece of data at one time. This will guarantee fewer chances of error and help you identify problems.  

BigITcon’s answer

  BigITcon Ltd has implemented a lightweight Microsoft based end-to-end solution for overcoming the “messy data” problem. Our dynamic set of packages do the integrity and cross reference checks and builds a “clean” fundamental layer for reporting. In the most cases we implement Power BI reports for data visualization. In the Power Query leayer we implement additional “report specific” data cleaning step in oder to keep the dataset lean, speed up the reports and save storage.  

Combine Data

  You need to add all the missing values that were identified earlier in the process. Apart from that, you need to cross-reference data from multiple sources and combine a final dataset. After that, the usability of the dataset should be checked to make sure that the process has been efficient. Once you do, you will have complete information that can be used to improve decision-making.  

Final Words

  Data is the foundation of every successful business in today’s landscape. However, data can only be as effective as its analysis procedure. This is why data cleaning is essential.  It double-checks the analysis process and then removes anything that doesn’t provide value. This is the process of perfecting a dataset and you need to execute it perfectly for the best results.  Once that is done, you will have valuable information that you can use to make your business successful.
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