7 Steps To Cleanse Data

Identifying Unwanted Observations to Cleanse your Data

Remove Duplicates and Irrelevant Data

Duplicate entries can muddy the waters and skew our analysis. Imagine having the same customer record appear twice—our insights would be as clear as a foggy morning! Here’s how to tackle this:

Scan for Duplicates

Start by sorting your data based on relevant columns (like customer IDs or timestamps).
Look for identical rows—those sneaky duplicates hiding in plain sight.
Once spotted, bid them farewell! Delete or merge these clones.

Trim the Irrelevant to cleanse your data

Not all data points are created equal. Some might be as useful as a screen door on a submarine.
Identify columns that don’t contribute to your analysis. Maybe that “Favorite Emoji” column isn’t helping much with sales predictions.
Trim away the fluff—remove irrelevant columns to streamline your dataset.

Ensure the Dataset Is Free of Unwanted Entries

Our dataset is like a garden; we want to keep it free of weeds. Here’s how to ensure only the good stuff remains:

Quality Control

Scrutinise each observation. Is it relevant? Accurate? Trustworthy?
Weed out any entries that don’t meet your quality standards. Maybe that “Mystery Guest” entry with a blank name needs to go.

Handle Outliers

Outliers are like eccentric neighbors who throw wild parties at 3 a.m.
Detect extreme values—those oddballs that don’t play by the rules.
Decide whether to prune them (remove them) or gently nudge them back into the fold (adjust).

Cleansing data is like decluttering your room—it feels fantastic once it’s done! Next up, we’ll standardise our data.

7 Steps To Cleanse Data

Standardising Data when you Cleanse Data

Unify Data Formats

Data formats can be as diverse as a spice bazaar. Some are spicy, some bland, and others downright exotic. But fear not! We’ll whip them into shape:

Date Formats

Dates can be tricky—like deciphering ancient hieroglyphs. Is it “MM/DD/YYYY” or “DD-MM-YYYY”?
Choose a standard format (e.g., ISO 8601: “YYYY-MM-DD”) and stick to it. Consistency is key!
Convert all dates to this universal language. No more date dialects!

Capitalisation Consistency

Text data is like a crowd at a concert—some shout, others whisper.
Standardise capitalisation: Title Case, Sentence Case, or ALL CAPS.
Ensure that “CustomerID” and “customerID” don’t play hide-and-seek.

Convert Data Types Consistently

Data types are like puzzle pieces—they need to fit together seamlessly. Let’s make sure they do:

Numeric Types

Numbers can be integers, floats, or even complex numbers (yes, imaginary friends exist in data too!).
Convert all numeric values to the appropriate type. No more apples masquerading as oranges!

Categorical Types

Categories are like Hogwarts houses—Gryffindor, Slytherin, Hufflepuff, and Ravenclaw.
Ensure consistent categories (e.g., “Male,” “Female,” “Non-binary”) across the dataset.
No Sorting Hat confusion here!
Data standardisation is like tuning an orchestra—harmony awaits! Next, we’ll tackle handling outliers.
Handle Outliers

Outliers—those quirky data points that stick out like a pineapple on a pizza. Fear not! We’ll tame them and ensure our data plays nice:

Remove or Adjust Extreme Values

Imagine a scatter plot where one lonely dot is miles away from the rest. That’s an outlier!
Identify these rebels—values that defy the norm. Maybe it’s a typo or a measurement glitch.
Decide: Should we kick them out (remove) or give them a stern talking-to (adjust)?

Ensure Data Integrity

Data integrity is like a trust fall exercise. We want to catch each observation without dropping any.
Check for inconsistencies. Is the “Age” column showing negative values? Unlikely!
Validate data against business rules. If a customer claims to be 150 years old, we raise an eyebrow.

Remember, handling outliers is like hosting a quirky dinner party—balance the eccentric guests with the well-behaved ones! Next, we’ll validate our data.

7 Steps To Cleanse Data

Validate Data

Data validation is like a security checkpoint at the airport—only the legit passengers get through. Let’s ensure our data behaves impeccably:

Use Validation Techniques to Prevent Errors

Think of validation as a spell-checker for your dataset. It catches typos, missing values, and other hiccups.

Techniques include:

Range Checks: Is that “Temperature” value really -500°C? Doubtful!
Format Checks: Dates masquerading as phone numbers? Not on our watch!
Consistency Checks: If “ProductID” doesn’t match any known products, it’s an imposter.

Screen the Dataset for Inconsistencies

Inconsistencies are like plot holes in a movie—they break the immersion.
Scrutinise each row. Do the columns align logically? Does “Customer Age” match their birth year?
Weed out the oddballs. Maybe someone claims to be both a “Cat Lover” and an “Allergic to Cats” enthusiast.
Data validation is like proofreading your masterpiece—catch those sneaky errors before they escape into the wild! Next, we’ll map data to valid values.

Map Data to Valid Values

Mapping data is like translating between dialects—it ensures everyone speaks the same language. Let’s get multilingual with our dataset:

Develop Codes to Transform or Replace Data

Think of this as creating a secret decoder ring for your data.
Develop codes (functions, rules, or lookup tables) to transform messy data into something coherent.
For example:
If “Gender” has values like “M,” “F,” and “X,” map them to “Male,” “Female,” and “Non-binary.”
Turn those cryptic product codes into friendly product names.

Ensure Uniformity Across the Dataset

Consistency is our mantra. Imagine a choir where everyone sings in harmony.
Apply the codes consistently to all relevant columns.
No more “Apples” in one column and “Granny Smiths” in another.
Remember, mapping data is like creating a treasure map—X marks the valid value! Next, we’ll securely store and share our sparkling clean data.

7 Steps To Cleanse Data

Address Missing Data

Missing data—like a jigsaw puzzle with a few pieces mysteriously vanished. Fear not! We’ll patch up those gaps and keep our dataset whole:

Handle Missing Values (Impute or Remove)

Missing values are like elusive unicorns—they exist, but we can’t quite see them.
Impute: Fill in the blanks. Maybe use the mean, median, or a clever algorithm to guess the missing values.
Remove: If a row has too many gaps, consider letting it sail off into the sunset.

Maintain Data Completeness

Completeness is our holy grail. We want every row to be well-fed and content.
Regularly check for new gaps. Did someone spill coffee on the “Revenue” column?
Keep your dataset plump and happy—no missing-value diets allowed!

And there you have it! Our data is now squeaky clean, ready for analysis, and as polished as a freshly waxed car.

Securely Store and Share Data

Our sparkling-clean data deserves a cozy home—a place where it can rest, safe from prying eyes and cosmic rays. Let’s make sure it’s snug and accessible:

Store Cleaned Data Securely

Think of data storage as a digital vault. We want Fort Knox, not a leaky sieve.
Consider options like:
Databases: SQL, NoSQL, or graph databases—pick your flavor.
Cloud Storage: Amazon S3, Google Cloud Storage, or Azure Blob Storage.
On-Premises Servers: Your own data center, guarded by dragons (or firewalls).

Make It Accessible to Relevant Stakeholders

Data isn’t a hermit crab—it needs interaction.
Set permissions: Who gets read access? Who can modify it? Who’s the data whisperer?
Create user-friendly dashboards, reports, or APIs. Let stakeholders feast on insights.

Secure storage is like tucking your data into bed with a bedtime story—it’ll dream of meaningful analyses!

Conclusion

In this article, we’ve explored the seven essential steps to cleanse data and transform it from a chaotic mess into a valuable asset. And there you have it! Our 7-step data cleansing journey is complete. From identifying unwanted observations to securely storing our pristine data, we’ve transformed chaos into clarity.