For advanced analytics in 2023, having data is not sufficient. We already mentioned in the previous points how big data is of no use if it’s not clean enough for analytics. It also refers to incorrect data, data redundancy, and duplicate data with no structure or format.

This causes the data retrieval process to slow down. That directly leads to the loss of time and money for enterprises. On a large scale, this loss could be counted in millions. Many researchers and enterprises are looking for ways to automate data cleaning or scrubbing to speed up data analytics and gain accurate insights from big data. Artificial intelligence and machine learning will play a major role in data cleaning automation. [1]

Figure 1. automation of data cleaning

automation of data cleaning is shown in figure 1.Automated data cleansing uses software or tools to identify and correct or remove errors, inconsistencies, and inaccuracies in a company's data. It involves automating the data cleaning process to save time and reduce the risk of human error.[2]

The 5-Step Process to Data Cleansing & Automation

• Step 1: Prioritize Data Fields.
• Step 2: Establish a Data Cleansing Process.
• Step 3: Cleanse Existing Data
• Step 4: Institute Data Rules & Workflows.
• Step 5: Regularly Review and Update Data Quality and Procedures.[3]

Data Cleaning Tools

1. Open Refine
2. Jupyter Notebook
3. Trifacta Wrangler
4. TIBCO Clarity

The Importance of Data Cleaning

Successful data cleaning measures will ensure that your analysis results are accurate and consistent.

We often hear about the power of data and the need for data-driven decision-making in business. But that only really works when you use clean data from the outset.[4]

Data cleaning ensures that the data you need is free of any errors or inconsistencies to conduct a detailed analysis. Businesses must adopt data cleaning if they haven’t yet and leverage its capabilities to derive meaningful outcomes.

The various aspects of data cleaning, including what data cleaning is, how it works, data cleaning automation, data cleaning use cases/examples, and more, are discussed in the article.[5]

References:

1. https://www.datatobiz.com/blog/top-data-science-trends/
2. https://winpure.com/data-cleansing/automated/ -
3. https://www.iasset.com/blog/5-step-process-data-cleansing-automation
4. https://monkeylearn.com/data-cleaning/
5. https://www.nanonets.com/blog/data-cleaning/

Cite this article:

Gokula Nandhini K (2023), automation of data cleaning, Anatechmaz, pp.55