Challenges faced by Data Analysts while performing Data Preprocessing
What should you do if you really got stuck behind the walls of data
In my previous article, I discussed data preprocessing, exploring its methods and practical applications.
I suggest giving it a read, as it’ll provide context for this discussion.
Now let’s jump into the main part
Problems faced by data analysts during data preprocessing
Today’s modern enterprises run on the fuel of data, as you know it is the new oil, which is very abundant.
Businesses use it to achieve their objectives, therefore to do so, the data must be wisely curated with cleanliness, consistency and context.
First of all, raw data is extracted from different sources, both internally and externally but later on, refining it is the most tedious task for an analyst to perform, which raises a bunch of challenges and problems while evaluating datasets.
Irrelevant dataset
Irrelevant datasets are like puzzle pieces that don’t belong to the picture you’re trying to create.
More technically a raw dataset contains irrelevant attributes that can distract focus and consume valuable resources. Like contact numbers, names, addresses etc
Solution
Evaluate those attributes only that serve the analysis purpose by removing the unnecessary ones. You can also ask for some tips from an expert to make informed decisions
Duplicate data
Duplicate data is likely having two copies of the same data.
It mainly happens when you combine data from different sources and it accidentally repeats the same information.
Solution
Try to detect the duplicates early during the data cleaning process to avoid duplicate rows and columns being inserted into your dataset.
If the replicates cause confusion then you might go and recheck the original data source to resolve the issue
Incorrect data types
Imagine, somebody doing maths but with letters instead of numbers. In short, incorrect data types can throw a wrench in the analytical process.
Solution
Ensure your data speaks the same language in which it has been dressed. Convert those data stored as strings into the appropriate numerical formats
Multi-collinearity
In datasets, when different columns have correlated values or info, multi-collinearity arises which can create confusion with your analytics
Solution
Decide which column you want to retain and which one to discard to avoid collinearity, removing highly correlated features helps to maintain data’s clarity
Too many dimensions
Too many dimensions case arises when you’re trying to visualize all the attributes of a dataset altogether which makes the analysis complex.
It’s like trying to see all the sides of a building at once.
Solution
Think of it as summarizing a long story by tailoring only the important parts, and removing unrelated information, thus simplifying the analysis process.
Therefore, these are the 5 essential challenges encountered during data preprocessing,
Important note
This data is fully derived through research from Analytics Vidya
Impact of wrong data preprocessing
Maintaining the quality of data is a never-ending task for every enterprise out there,
And they must keep in mind that as their customer base increases, it becomes more complex to handle data.
But what can be the consequences if it is not done properly?
The answer is - creation of poor data quality
O’Reilly, an American learning company, says that organizations are heavily dealing with data quality issues
Either they have multiple data sources i.e., inconsistent data or they lack resources to fully sort out data quality issues.
Poor data = poor decisions
An enterprise’s data-driven decision fully depends upon the information it holds or retains.
They have to make sure that their data represents reality and truth otherwise, it can harm the
- business performance,
- customer relations, and even
- revenue generation
Imagine a scenario, where you send an email to your customer that has incomplete details and information,
What happens? then,
They will only get disappointed, and mistrust arises.
Another situation -
Your company, fails to convert its prospects into potential customers,
why?
Due to the insertion of incorrect customer data into the system
Conclusion – How can you cultivate high quality data for your company
Cultivating high-quality data isn’t rocket science, but it’s crucial for your company, to make informed decisions, avoid fraud, and deliver efficient services.
You must keep these 3 takeaways in mind.
Accuracy
Try to evaluate the values of all the desired fields with correctness.
Always recheck whether those names, and dollar signs in the database records are spelled correctly or not.
Uniqueness
Correct those fields of the database that have repeated information, For example, the entity “Galaxy Ltd” and “Galaxy Limited” are - same in the real world but not in the data world.
It might create data duplication (as mentioned above), so one needs to be eliminated.
Data Update
Update your data manually and frequently because it must be relevant to the needs and requirements of your business.
Therefore, in the end, I would only suggest- treat your data like a gold – it’s the spine of your business. Give that attention it really needs, and it’ll stand beside you like a loyal ally.
If you’re an Data analyst, what kind of other problems do you have faced while analysing that huge chunk of data?
Do you think my article resonates with your data collection struggles?
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