Data cleansing or cleaning refers to removing inaccurate and irrelevant data. Sometimes, it is also known as data scrubbing. As this term suggests, cleansing is all about removing inconsistent and invalid data. Primarily, this process targets typographical errors, null values or blanks and duplicates.
But, it cannot be possible without screening anomalies. This is where data validation comes into play. Data cleansing companies check accuracy and quality of data prior to importing and data processing. This process mainly filters blank and null values, unique values and a range of consistent data. This is how a refined version of data shapes up.
Importance of data cleaning:
Not any, but, the cleansed data assist in deriving patterns through mining that configure intelligence. This intelligence significantly helps in decision-making. As a result, the business gets vital breakthroughs. Several turning points come its way. It is just because the data from multiple sources merge global web-based information system in the data warehouse.
But, this merger stems the possibility of redundant data. It is so because they represent different in various data repositories. When they are federated, they may not good-fit the consistency level of analysis funnel. This is why they are said to be erroneous & inconsistent data sets. When used, they compute undesirable decisions that don’t qualify for the success and growth of any business.
In the nutshell, this cleansing appears in a crucial role while supporting data warehouses. It is where the extraction of data from a variety of sources, integration and aggregation take place. However, there is a strong possibility of collecting inconsistent data. But, the cleaning deals with inconsistency, duplicate data entry and missing information through the ETL process.
What is data cleansing in ETL process?
The acronym ‘ETL’ stands for Extract Transform and Load. The data mining companies employ ETL tools to scrape, transform and load data for data cleansing in warehouses. With the assistance of data experts, they target the staging area in repositories/warehouses to search for instances, like misspelling, duplicates and contradictory values etc.. These are where the metadata about the data sources, schemas, mapping and script programs are loaded and then, managed in a uniform layout. The programmers and data scientists specify the delimiters separating sub values for extraction.
They codify if-then and case using a programming language, like R or Python to handle exceptions in data cleansing, like misspellings, abbreviations, missing or cryptic values and values outside the range. If required, they construct a table lookup and functionality to address web scraping issues during query runtime.
Data transformation trails data scraping. An easy-to-use comprehensive graphical user interface carries out this transformation. Again, it is a well-defined if-then, cases and functionality that compose transformation criteria, such as type of data conversions (like reformatting data), string functions (like split, merge, replace and sub-string search) and arithmetic or scientific or statistical functions.
Benefits or Advantages of data cleansing:
- Efficiency of customer retention: With attractive ad campaigns, customers can be boarded. But, the real challenge is retaining them. They stay with the brand that understands them. This understanding develops when you have the correct data on your customers. You can pull the data of buyer persona, behavior, preferences and sales journey. Thereby, you can come with the information to re-target for cross-selling. Hence, customer retention seems no big deal.
- Improved decision making: The up-to-date & consistent data have a tendency to assist in evaluating the appropriate decision that corresponds to your business operation. Let’s say that you collect the email ids of all hotels in Hampshire, UK. But, that record is ten years old. If you use it today for online marketing, it will be inconsistent. The number of hotels will be more possible. There would have some hotels that might be shut. As a result, your decision making fails. The cleansed data could assist in better decision making.
- Removing Loops in Business Operations: The business research companies catch insights through the data of their operational performance, staff and supply chain. If they are not appropriate, the flaws or potholes will be unseen or untapped. Consequently, the entrepreneur will remain in the dark. Being a blind to those flaws can cost a massive loss in the long term.
- Increase Productivity: As aforesaid, the consistent data improve performance. It directly affects productivity. The business research requires cleansed data because redundancy can lead to bad decisions. There is no need to introduce the repercussions of such decisions. Declined productivity, over expenditure and not up-to-the-mark performance are a few consequences that can disturb business growth. The cleansed data reveal the reality of the true performers and actual performance. Thereby, the decision makers can easily do what is necessary.
- Multiply Revenue: Revenue depends on the performance and productivity. Their true picture evaluates top and worst performing areas. The clean data assist in deriving decisions to boost the least performing areas. It will help in translating them into a cash cow.