Data Cleaning in Data Analytics: Why is it important?

Imagine constructing a puzzle with mismatched pieces. Sounds impossible, right?
For data analysts, working with raw, incorrect datasets often feels the same. Data filled with inconsistencies and errors could only lead to faulty analysis, and eventually, disastrous results.
A recent study revealed that organisations lose an average of $15 million annually due to data inaccuracies. Thus, data cleaning in data analytics is more than important to ensure authentic data-driven business decisions.

Data Cleaning simplified

So, what is data cleaning?
Data cleaning, data cleansing, data scrubbing or data wrangling, is the first step to data analysis. In simple terms, think of it as cleaning or tidying up your data before you start the actual data analysis. In fact, data cleaning takes up to 60% of the overall analysis time. Thus, the cleansing part is always an indispensable chapter in a comprehensive data science course online.
Data Cleaning is the process of eliminating errors and discrepancies from the raw dataset to make the data fit for accurate analysis. It enhances accuracy, reliability, and clarity by eradicating inconsistencies and refining data quality. These could be things like missing, incomplete, incorrectly formatted, or duplicate data.

But why is it important?

Flawed or incomplete data could lead to erroneous analysis if not cleaned on time . The cleansing procedure helps to remove the messy bits in raw data to get a trimmed, tidy picture. By cleaning data in data analysis, you ensure that the results are credible and accurate.
Data cleaning aims to save as much data as possible while improving reliability. Some of the popular data cleaning tools are - Microsoft Excel, Python, Ruby, SQL, etc.
Cleaning and sorting data has its own set of benefits. In the next section, you will know about some essential benefits of data cleaning.

Benefits of Data Cleaning in Data Analytics

The benefits of data cleaning cannot be overstated. It's like laying a strong foundation for a building – do it right, and you will have a sturdy, lasting building; mess it up, and everything collapses. That's why experts spend 60-80% of their time cleaning data.
Here is a brief on the importance of data cleaning in data analytics.

Accurate and credible insights

Messy data leads to meaningless results. Data cleaning is crucial before proceeding with the analysis part to ensure accurate insights


Organised data

Each day businesses generate tonnes of data. The data cleaning process helps to get rid of muddy data and cut out only the most essential insights. Trimmed data is easier to organise and sort, helping you improve your efficiency and save storage.


Preventing Errors

Clean data is vital for daily operations and to prevent mishaps in business decisions. Marketing, for instance, thrives on accurate customer databases to avoid blunders.


Boost Productivity

Accurate data insights are essential to keep business operations on the right track. Devoid of junk, clean data offers credible insights that help to reach informed business decisions. Cleaning rids databases of junk, saving time and frustration when searching for necessary information.


Cost Savings

Bad data leads to pricey blunders. Regular checks help to catch errors early, preventing costly fixes.


Better Mapping

Clear data aids in building strong internal infrastructures and applications, facilitated by tidy information.

How to perform Data Cleaning?

Data cleansing or data scrubbing is the backbone of quality analysis. Here is a breakdown of data cleaning steps from start till the final step.

Step 1: Trim the inconsistencies

The Data Cleaning Techniques start with removing irrelevant and duplicate information. Get rid of the data chunks that do not fit your analysis goals. Also, clear out repeated entries to chuck out duplicate information.


Step 2: Tackle Structural Errors

Fix inconsistencies caused by manual entry errors, like typos and inconsistent capitalisation. Ensure uniformity in categories, labels, and punctuation. Look into addressing issues as well like uppercase vs. lowercase.


Step 3: Standardise the Set

The next step in the data cleaning process is standardising the data types and units. Decide on a consistent format for values, whether lowercase or uppercase. Make sure numerical data uses the same units throughout. Adhere to date conventions.


Step 4: Handle Outliers

Identify and consider removing outliers that significantly deviate from the rest. Approach with caution, as they can distort the analysis. Only remove if the outlier is proven erroneous.


Step 5: Correct Contradictions

Address cross-set errors, where a complete record contains inconsistent data. For example, a situation where the total run time doesn't match individual race times. Fix contradictions that compromise the integrity of your dataset.


Step 6: Convert Types and Clean Syntax

Ensure the credibility of the dataset. Validate type conversion, like numbers as numerical data and text as text. Eliminate syntax errors and extra whitespace.


Step 7: Deal with Missing Data

During data cleansing processes, you get three options for addressing the missing data: remove entries; impute based on similar data; or flag as missing. The last often proves best, marking gaps with 'missing' or '0'. This informs your analysis of gaps.


Step 8: Validate Your Dataset

In the end, validate your cleansed dataset. It’s like a double-check of the changes made. Ensure corrections, standardisation, and deduplication are accurate. Use scripts or predefined rules to verify and cross-check against trusted datasets.

Data cleansing services are akin to constructing a solid foundation for your insights. It transforms messy, incomplete, duplicate data into a valuable resource for accurate decision-making. By following the steps outlined – from eliminating unwanted observations to validating your dataset – you ensure to land up with clean, tailored data that is ready to help you for data-driven decisions.

Conclusion

As mentioned above, data cleaning is a vital part of the data analytics process. If you are aspiring to build a career in data science, you have to receive training on data cleaning. Do you want to learn data cleaning in data science? When it comes to choosing a leading data analytics course in Kolkata or India, DataSpace Academy could be a credible name. Enrol today to unlock the secrets of data and embark on a journey of transforming raw information into valuable insights. Your data-driven future awaits!