Attention Data Analysts and Business Owners! Are you ready to take your data analysis to the next level?
Then don't miss this important newsletter on the crucial steps of data preprocessing.
You would ask me,
Why do I even need to learn Data Processing?
Imagine you just pitched an investor an idea and he says….
How would you react? I am assuming you will be devastated!
Because duh, you use dirty data in your analysis, then most likely your results will be garbage.
If you think you can make ends meet using dirty data and things are working out for you. Here’s how you’re lacking:
If you make your employees figure out things using dirty data, you’re wasting your time, diminishing worker’s productivity, and you’re wasting the opportunities to work on the right leads.
For example, Ashad is your loyal customer who purchases very often from you. But your system’s mind had fallen to its knee and it characterized Ashad as a customer with a bad credit. And you have been messaging your good customer, Ashad, reminding him to pay off his liabilities.
Ashad would loose confidence in your business, ruining your credibility.
While, your sales and marketing team has been selling to the wrong targeted audience
And ultimately, you loose customers like Ashad and because you did not clean your data, your wrong targeting resulted in failure of acquiring new customers.
Boom! That’s how you lost your revenues!
But when you pre-process and clean your data, you’re setting yourself up for much more accurate insights.
If you want to learn “how to pre-process your data?” Keep reading!
Data Preprocessing: The Key to Better Business Decisions
Companies can gather data from all over the place, but to get the best insights, you need to preprocess it first.
So, what is data preprocessing?
It’s the step in the data mining process
that takes raw data,
cleans it up,
and formats it so it can be read and understood by computers and machine learning algorithms.
Think about it like this: machines like clean, neat information.
But raw data from the real world is messy, it may contain errors, inconsistencies, or be incomplete.
Data preprocessing is all about getting that messy data into shape.
Data Preprocessing Steps:
Step 1: Data quality assessment
Ever played Candy Crush? The candies need to be accurately formatted and consistent in colors to make the Sugar Crush!
Similarly, you want to make sure your data is relevant, consistent, and in the right format. Look out for mismatched data types, mixed data values, data outliers, and missing data.
Here are the five dimensions of data quality.
Step 2: Data cleaning
Data cleaning is like tidying up before guests arrive.
It's the most important step in preprocessing because it ensures your data is ready for action.
Just like you would clean up the mess and fix broken items in your house, you will correct inconsistent data, fill in missing data, remove noisy data, and tidy up text data.
If you run out of party supplies, you can also perform data wrangling or data enrichment to add more data and clean it up too.
This checklist can come in handy while you brush up on your data!
Step 3: Data transformation
Think of it as rearranging the chairs and tables to create a comfortable space for your guests.
It's time to turn the data into the right format for analysis and other downstream processes.
Data transformation involves aggregation, normalization, feature selection, discretization, and concept hierarchy generation.
Look how satisfying the block on the right looks when everything is organized.
So, next time your mom calls you out for not cleaning the house, remember to tell her you’re busy cleaning data for the companies to create real impact!
Just kidding! Go and fix your bed!
Step 4: Data reduction
Data reduction is like getting rid of the leftover food after the party.
It involves removing redundant and irrelevant data, which will make your data easier to manage and reduce processing time.
Conclusion
In conclusion, data preprocessing is all about making sure your data is ready for the big show. Just like a successful party, a successful data analysis depends on proper preparation.
So, when it comes to data-driven decision-making, don’t forget about preprocessing. A little elbow grease can go a long way!
In fact, this is a visual representation of what a day as a data analyst looks like:
So Remember,
With the right steps in data preprocessing, you'll be able to turn your data into valuable insights that can drive better business decisions.
Don't miss out on this opportunity to take your data analysis skills to the next level! Subscribe now to stay informed and be at the forefront of the data revolution.
Until next time,
Hafsa and Zain from Team Inspired Analyst - signing off!
Well Done Sir 👍😎✅❤️