Data cleansing is a rigorous process in which erroneous, unfounded, and unethical data is discovered and removed from the database. The process also involves going through the database and detecting incomplete, improper, and irrelevant data and altering and modifying it to make it more accurate.
Regardless of how systematic and organized, the data collection process is, these messy data always find its way in the database and has to be flushed out. There are many reasons behind the grubby data. Sometimes it is the customers who end up giving the wrong information, other times, it is the staff that makes an error while entering data.
In spite of this, not many businesses take data cleansing seriously. Partly because they don’t know why it matters and partly because it seems like too much work. So they let bygones be bygones and focus their attention on the new data.
What they don’t realize is that cleansing of the data matters greatly because it ensures the presence of quality data only. Quality data means all the information in the database has accuracy, correctness, completeness, consistency, and integrity. Another thing they don’t realize is that they don’t have to manually inspect database and detect faulty data, automation process tools exist to make sure of that.
One of the main operations of data quality management is measuring the quality of the data. It is only when the quality is measured, do the businesses realize the shortcoming and take actions to cleanse the data. In order to measure non-quantifiable source such as data, metrics are needed. The same metrics also allow us to access the endeavors that needs to be taken to increase the data quality. The data quality metrics must be clearly defined in every organization. Some of the common quality data metrics are summed up as ACCIT, standing for Accuracy, Consistency, Completeness, Integrity, and Timelessness.
These metrics are assessed in various ways. Comparing the ratio of data to errors is one of them, or defining a metric that that measures full data entry and points out empty values etc.
Let’s learn why data cleansing is a crucial part of data management and how it can be automated:
How Crucial Is Data Cleansing?
In the earlier days, all data is good data approach was followed. It wasn’t until the late 1990s that subpar quality of data was recognized as one of the main reasons behind failed database projects.
It is common knowledge now that data analytics is the key to success for businesses. However, the data that is used to draw analytics need to be of high quality. Dirty data is likely to mislead with its distorted information. Uncleaned data that majorly consists of superficial information cannot be relied upon to derive business intelligence and for data mining.
In order to gain deep operational insights, make sure the data is of the highest quality possible and for that, businesses need to create a process for monitoring, analyzing and cleaning data.
When the first step of cleansing the data is missed, it leads to bad analysis and inaccurate insight, which then leads to bad business decisions.
According to the Big Data expert, Bernard Marr, “Much of the data may be unstructured, noisy and in need of thorough cleansing and preparation before it is ready to yield working insights”
The Forrester Report released in 2017 shared that when a typical fortune 1000 company increases its data quality by 10%, it experiences an additional income of $65 million.
Why? The answer is obvious. Cleansed and enriched data leads to more accurate analysis, it leads to insights and predictions and smart decisions. All of this results in high and improved revenue.
However, the quality of data cannot be measured in numbers. Data needs to have certain dimensions in order to be passed as quality data. Accuracy, timelessness, completeness, and consistency are some of the prerequisites for data to be of eminence.
Automating Data Cleansing
Data cleansing is a self-explanatory process in which data in the database is altered, modified, hacked, and brushed through to make sure it is error-free for the analytics to be carried out. There are numerous ways in which data is monitored and treated. It can be done manually, or through a certain software and via automation. The best data cleansing method is the one that uses a suitable method for every step of data cleansing.
The process of data cleansing is also known as data scrubbing or data cleaning, and there is no one way to do it. Every organization can create its own methodology to cleanse their data. The most popular method of data cleansing is the one shared by Müller and Freytag.
The duo shared three major steps involved in data cleansing:
- Defining and determining errors
- Detecting and identifying error instances
- Correct the errors
Define and Determine
This is the step of data cleansing that requires human intelligence. If this step is recognized and established early on, it can lower the cost of data cleansing later.
Defining and determining all the things that often go wrong with data is something that catches the eyes of all those individuals who closely work with data. These errors are also particular to every different organization. Some of the common errors that most of the firms identified as recurring are missing values, misspellings, outdated values, interval violation, partially empty tuples, outdated reference, wrong entries, ambiguous data, incomplete contextual data, a a difference in data across various databases while for the same entity, etc.
After organizations determine all the defects that make their data dirty, these defects are used to measure and detect their occurrence in the data.
After the first step, the data management team knows what to look for in order to maintain the quality of data. However, the amount of data is too excessive for employees to detect the aforementioned errors based on their individual monitoring alone. Automation or semi-automation of this process not only speeds up the data cleansing but also saves costly expenditures.
The predefined expected data errors can be fed into the automation software so that it detects the errors during the data collection process or soon after.
Automation software also offers the possibility of screening all the data and comparing it with the screening criteria programmed in it, so that it immediately highlights the erroneous data, dubious data, or any data that does not meet the standardized criteria.
In the case of incomplete data or any other form of data error, automated query generation can also make corrections or ensure the integrity of data instantly.
Cleansing and Correcting Data
In their report, Müller and Freytag’s defined one of the methods of data cleansing as choosing appropriate methods to automatically detect the errors and remove them.
After determining all the possible errors in data and then automation tools flagging the predefined errors, the same automation software would continue to remove them.
For instance, if the invoice number is a combination of six letters and numeric values and the automation tool is programmed to read just that, then any invoice number that does not matches the standard format or is incomplete will be automatically removed by the system. The algorithm of RPA tools reduces the data cleaning time greatly and allows the companies to augment the degree of automation in data cleansing.
Data cleansing is the underpinnings of data management. As crucial as data analysis is, it is counterproductive if the data is not of the highest quality in the first place. To deal with this on day to day basis, the best solution is to arrange a standard daily protocol that will take care of cleansing data and the solution is in RPA.
Omnisys Solutions also helps companies leverage the data they already have to get tangible dollar benefits. With a focus on business value, Omnisys helps its customers make the most of their technology investments and define the right roadmap for them.