An Interesting Storytelling for presenting your Ideas with Data Cleaning

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If the room is dirty and how it feels and it is the same feeling when the data is so dirty and it causes difficult for the best presentation.



Presentation plays an important role in our lives. We need to present our ideas in a clear and crisp way so that other can understand easily. If there is any missing information, it can add to the confusion. If there is a misguided information , then it further adds to the confusion. Hence, the presentation is very crucial part of our lives. In the modern digital world, the presentation requires the data in the backup for very effective authority and proof. The claim we are making with our presentation needs evidence and data quality is required.

In this situation, if you have a data with has lot of misinformation or not crisp enough. That is called as dirty data and how to detect or clean the dirty data needs operations and processes to be followed for removing and create the best data presentation.

1. Messy Things needs the fix by cleaning, scrubbing and also detecting at the right time



Mostly in Business Corporations, the people working there tend to take decisions using data available with them such as their customer data or their product sales information and what new products their competitors are launching. For example, the company has a table which has order processing information and if there is a question whether the order is processed - Yes "Y" or No-"N" and in the datasheet they find A, they cannot make any decision and how this dirty data came into picture would be a separate question. There are so many areas where the dirty data would be present and how to clean them is what data cleaning is all about for the best presentation.


2. Making an error or mistake is a learning path in life but we need to take action in identifying and correcting those mistakes


when we say learning from mistakes, we need to first identify what are the mistakes. Similarly, what constitutes the dirty data and how to detect or identify them is the question. Is it a misleading data or duplicate data would be the starting point. Spelling mistakes or formatting errors are other areas to focus later in the process. The quality issues would be like there would be 1 somewhere and One in another place or negative value in the age field.

In some columns the last name would not be available and if it is blank, the data can be cleaned by specifying NA as Not available for better presentation. As a starting point, you can try using your hands on " Google Refine" - A super free data cleaning tool used by the data scientist commonly.

3. Data cleaning is like we record everything with our mobile phone and then we need to pick,select and edit the video what we accurately need.




The Business has now a very big data of so much factors including the social trends and it is going to be messy with lots of data and hence data cleaning is a very crucial editing factor which is required on the raw data. If there is something measured with a column name say X and we have the question what does that X represent, it is a salary or its a policy number or car number. That accuracy needs a edit and correction. All industries such as finance, retail, banking use all sorts of data and the data cleaning is getting hot as the digital cleaning job in the future.

4. Cleaning can be seen as an time consuming process or even just getting nostalgic with memories



Just like cleaning, the data cleaning takes time and process and hence the job positions are going to get explored in this area in the future. People need patience and understanding in working with business data and the sound business and industry knowledge adds to the process. People needs to consider this as a big movie making process and then it can be seen as an interesting work for people to work with passion and interest.

In the initial stage, Understand the raw data and try to verify the data type such as if it is numeric in age field and character in name field and things like that to start. Then dig litter deeper like a data mining to understand better about the data like exploring a new place like shopping in India. This gives the visual representation of the data and a hands on what is present and how we would like to present. These end result must always be valid and good such that it gives a visual picture to take better decisions in the Business.

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