In this digital era, which is powered by the IoT (Internet of Things), Social Media, Edge Computing, along with increasing computing power like Quantum Computing, data is perhaps one of the most valuable assets for any business. The correct (or incorrect) management of data will have a huge impact on the success of an enterprise. In other words, it can make or break an enterprise.
That’s the reason, in order to leverage this huge data, enterprises, whether big or small, are using technologies like Machine Learning & Deep Learning so that they can build useful customer profiles, increase sales and improve brand loyalty.
But in most of the cases, data may be inaccurate, inconsistent, and redundant because of having many collection sources and various formats (structured and unstructured).
Can we access pertinent information in a timely and comprehensive fashion by providing data with such anomalies, to Machine Learning algorithms?
No, of course not! There is a need to clean such data first.
That’s where Data Cleaning comes into the picture!
Data cleaning is the first and arguably the most important step towards building a working Machine Learning model. It’s critical!
To put it simply, if the data hasn’t been cleaned and pre-processed, the Machine Learning model does not work.
Although we often think of data scientists as spending most of their time tinkering with ML algorithms and models, the reality is somewhat different. Most data scientists spend around 80% of their time cleaning data.
Why? Because of a simple truth in ML,
“Better data beats fancier algorithms.”
In other words, if you have a properly cleaned dataset, simple algorithms can even learn impressive insights from the data.
Some important questions related to Data Cleaning that we’ll be covering in this post, are:
- What is Data Cleansing?
- Why it is required?
- What are some common steps to do Data Cleaning?
- What are the challenges associated with Data Cleaning?
- Which companies are providing Data Cleaning services?
Let’s get started the journey together to know about Data Cleaning!
What exactly is this Data Cleansing?
Data Cleaning, also called Data Cleansing, deals with detecting and correcting (or removing) inaccurate or corrupt records from a record-set, table, or database. Broadly speaking Data Cleaning or Cleansing refers to identifying incorrect, incomplete, irrelevant, inaccurate or otherwise problematic (‘dirty’) parts of data and then replacing, modifying, or deleting that dirty data.
With effective Data Cleansing, all data sets should be free from any errors that could be problematic during analysis.
Why Data Cleaning is required?
Data Cleaning is generally thought of as the boring part. But it is a valuable process that helps enterprises save time and increase their efficiency.
It’s kind of like getting ready for a long vacation. We might not like the preparation part, but we can save ourselves from one nightmare of that trip by tightening down the details in advance.
We just need to do it, or we can’t start having fun. It’s that simple!
Let’s see some examples of problems, in various domains, that can arise due to ‘dirty’ data:
- Suppose if the ad campaign is using low-quality data and reaching out to the users, with irrelevant offers, the company not only reduces customer satisfaction but also misses a significant sales opportunity.
- You can understand the impact on sales if a sales representative failing to contact potential customers, because of not having their accurate data.
- Any online business, small or big, can be heavily penalized by the government for not meeting data privacy rule for its customers. For example, Facebook had paid $5 billion fine to the Federal Trade Commission for Cambridge Analytica data breaches.
- Providing low-quality operational data to production machines can cause major problems for manufacturing companies.
What are some common steps involved in Data Cleaning?
Everyone does data cleaning, but no one really talks about it. Surely, it’s not the ‘fanciest’ part of Machine Learning, and yes there aren’t any hidden tricks and secrets to uncover.
Although different types of data will require different types of cleaning, the common steps we laid out here can always serve as a good starting point.
So, let’s clean up the mess in data!
- Removing unwanted observations
The first step to data cleansing is removing unwanted observations from our dataset. Unwanted observations include duplicate or irrelevant observations.
- Duplicate or redundant observations most frequently arise during data collection. For example, it occurs when we combine datasets from multiple places or receive data from clients. Such observations alter the efficiency by a great extent as the data repeats and may add towards the correct or incorrect side, thereby producing unfaithful results.
- Irrelevant observations are those that don’t really fit the specific problem that we are trying to solve. For example, in the realm of handwritten digit recognition, scanning errors such as smudges or non-digit characters are irrelevant observations. Such observations are any type of data that is of no use and can be removed directly.
- Fixing structural errors
Next step to data cleansing is fixing structural errors in our data set.
Structural errors refer to those errors that arise during measurement, data transfer, or other similar situations. Such errors generally include,
- typographical errors (typos) in the name of features,
- same attribute with a different name,
- mislabeled classes, i.e. separate classes that should really be the same,
- inconsistent capitalization.
For example, the model should treat the typos and inconsistent capitalization such as ‘India’ and ‘india’ as same classes rather than two different classes. One example related to mislabeled classes is of ‘N/A’ and ‘Not Applicable’. If they appear as two separate classes, we should combine them.
These structural errors make our model inefficient and give the poor-quality result.
- Filtering unwanted outliers
Next step to data cleansing is filtering unwanted outliers from our data set. The data set contains outliers that are far from the rest of the training data. Such outliers can cause more problems to certain types of ML models. For example, Linear Regression ML models are less robust than Random Forest ML models.
However, outliers are innocent until proven guilty, so, we should have a legitimate reason to remove an outlier. Sometimes the removing of outliers improves model performance, sometimes it doesn’t.
We can also use outlier detection estimators that always try to fit the region having the most concentrated training data while ignoring the deviant observations.
- Handling missing data
One of the deceptively tricky issues in Machine Learning is ‘Missing Data’. Just to be clear, you cannot simply ignore missing values in your data-set. For very practical reasons, you must handle missing data in some way as most of the applied ML algorithms do not accept data set with missing values.
Let’s look at the two most commonly recommended ways of dealing with missing data.
- Dropping observations that have missing values:
This is a sub-optimal way because when we drop observations, we drop information also. The reason being, the missing value may be informative, and in real-world we often need to make predictions on new data even if some of the features are missing.
- Imputing the missing values based on past or other observations:
This is also a sub-optimal way because no matter how sophisticated our imputing method is, the original value is missing which always leads to a loss in information. As missing value may be informative, we should tell our algorithm if a value was missing. Moreover, if we impute our values, we are just reinforcing the patterns already provided by other features.
In a nutshell, the key is to tell our algorithm if a value was originally missing.
So how can we do this?
- To handle missing data for categorical features, simply label them as ‘Missing’. By doing so we are essentially adding a new class of feature.
- To handle missing numeric data, flag and fill the values. By doing so we are essentially allowing the algorithm to estimate the optimal constant for missingness, instead of just filling it with the mean.
What are the major challenges associated with Data Cleaning?
Although Data Cleansing is essential for the ongoing success of any organization, it is having its own challenges. Some major challenges include:
- Having limited knowledge about what is causing anomalies.
- Wrong-way of deleting data leads to incomplete data which cannot be accurately ‘filled in’.
- In order to assist with the process ahead of time, it’s very difficult to build a data cleansing graph.
- For any of the ongoing maintenance, the process of data cleaning is very expensive as well as time-consuming.
Which companies are providing Data Cleaning services?
Below is the list of top ten Data Cleansing companies:
- SunTec Data
- Hi-Tech BPO
That’s all for now, readers! Hope this blog post proves to be insightful for you! Looking up to building a career in Data Science? Explore our Data Science courses here to upskill in Data Science.