This article on data cleansing overview, is about what the overall data cleansing process is along with brief accounts on how the process works. Let us begin with what data cleansing really is?
In the simplest of the terms, data cleansing is also referred to as data cleaning. The term is self-explanatory. However, it is defined as the process that ensures that the data you possess is flawless.
Here it would mean that data cleansing aims at filtering your data of all the scum in terms of duplicates, incorrect contacts, etc.In other words, this process aims at making database correct, consistent and useable.
This is done either by identifying any error or corruptions that exists in the data. The consequent step would be to either rectify or update the data or to delete them. This is usually processed manually. Here is where we need to understand why data cleansing is needed.
In this data cleansing overview, let us quickly look at the benefits of data cleansing. We shall then percolate down understanding what is data cleansing, the types of data cleansing and techniques.
Where Is Data Sourced From?
Database providers collect and source data from various sources. Depending on the extent and expanse of the business, the database providers source their data from various geographical locations, globally. However, it must be remembered that there will be multiple data sources from the same location, which will inevitably lead to duplication.
Data Cleansing Overview On Benefits
Invalid contact numbers and redundant data will also be a concern in the raw data. Data cleansing will remove major errors and inconsistencies that are inevitable when multiple data is being pulled into one dataset.
Proper data cleaning has one base that it works on, garbage in garbage out. It is therefore easy to understand how a database can take your business or even your project to success or slap it down to a failure. From a clean database even simple algorithms can learn.
This is one the reasons why professional data scientists spend a lot of their in cleansing and appending database. However, it must be understood that different types of data will need different types of data cleaning techniques to be adopted.
With data cleansing mitigating errors and discrepancies in the database, people working on this database are much less frustrated. On the other hand, it also leaves clients happy to have a clean set of database.
The sum-up of the data cleansing process as a whole is best summarized as the intent to keep the database Clean, current and complaint.
Auto And Manual Data Cleansing Processes
The data cleansing overview showcases two defined and primary processes via which data is cleaned: Automatic and Manual.
Automatic or Automation of data cleansing refers to the adoption and employment of software intelligence to cleanse and append the data. Such a database cleansing process has been attributed to an accuracy rate of about 60-65%. This leaves an enormous amount of data to be cleaned and appended.
The manual method, on the other hand, involves human intelligence to carry on the cleansing and appending of the data. Of course, this is more time consuming but nothing beats human intelligence and understanding errors. Coupled with research and cross-checking by calling and reconfirming the details, data cleansing processed. The accuracy rate is almost perfect.
Data Cleansing Overview On Techniques
One of the first steps to be adopted towards data cleaning is to run check-in terms of auditing the current data. The aim should be to remove duplicates or irrelevant data. Pause to think of your own self. How many jobs have you changed, or cities have you moved? Do you not know of friends and colleagues who were not able to keep a stable job and or were laid off?
These are important reflections that matter in the data cleansing process. You see the moment an entry in the database changes jobs or moves location, the old data of that entry becomes redundant. Even if it was a quality lead, reaching out to that person becomes impossible because of the redundant data available.
In the B2B lead generation industry, it is therefore extremely important to update the database and clean up the redundant entries. This is one of the crucial functions that the data cleansing process takes care of.
In the B2B scenario, aligning the sales and marketing teams is one way of ensuring that the database remains clean. Without alignment, there is bound to be decreased productivity.
Data enriching refers to the process by which the data is refined to ensure the availability of all the relevant information. Data enrichment goes a long way in helping marketing and customer relationship experiences grow.
This brings us to the most important aspect of data science: The techniques used for data cleansing. Here is a quick rundown on the techniques:
Checking the validity of the data, with its adherence to various parameters as defined by Google is a quick way of understanding how through the data cleansing process has been or is in place.
Here are the checkpoints in a rush: Data quantity, the workflow, Inspection, data cleansing, verification and reporting. The science behind the data cleansing process might seem simple. However, the truth is its complex or simple depending on the data at hand to be cleaned.
Moreover, the way data was gathered initially also plays a vital role in defining how the data cleansing process will progress.