The process of data cleansing is one of the most mandated processes of the data outsourcing department. This process extracts, deletes/detect corrupt, inaccurate and duplicate data from the data set. The process enhanced the quality of the data and frames it more precisely so that the organization can get well-grounded information or report. The team of data science tools experts at Apeiron Techno Ventures is providing the service of data cleansing in a procedural way.
The team is responsible to ensure that the process is accomplished within a time frame and has followed each mandated step.
The team of experts is also responsible to guide the client about the difference between data cleansing and data enrichment, in order to make sure the client about the fine process being followed by Apeiron Techno Ventures.
However, in many cases the term data scrubbing also surfaces and business owners often get confused with the different words. We make sure that in the majority of the cases data cleansing and data scrubbing follow the same work culture and people use this compatibly.
Errors Fixed by the Data Science Team
After several fine operational attempts and findings, five major errors have been discovered by the teams of Apeiron Techno Ventures. These errors are often found frequently in large data sets.
a) Inconsistent Data: This type of data contains several major differences. Taking an example for your better understanding, that information like name or address formatted in the form, system or directory may contradict each other. This process is not as easy as it is defined because it requires a lot of effort and master plans. At this level, the outcome read by the reader often creates a fuss in the organization or the consequences may be faced by the customer. To avoid this situation, the data cleansing or scrubbing process is adopted by the team of Apeiron Techno Ventures. The team performs the work in a sharp dedicated way to bring the precise outcome.
b) Inaccuracy: This step is mostly faced by the teams who attempt the data precision procedure and filtration process. Teams often found that the data fed in the system was factually different from the data picked from the external source. It requires master data and formulas using science tools. The accuracy needs the true value data source. Inaccurate data may result in loss of customers or revenue for the company or organization.
c) Duplicate Data: This type contains repeated entries, identification, address or other words. The extra or repeated words are considered exact words. This situation is frequently found when the process of data collection is done from multiple sources like relevant departments, multiple places, clients etc.
The data combined in one place is expectedly to contain duplicity. Our team at Apeiron Techno Ventures initiates the data cleansing process to merge or remove these duplicate words and filters the data in order to produce accurate information or results for the organization or company.
d) Irrelevant Data: During the analysis of the data, there is some information or words which are found irrelevant and don’t fit in the data frame. To remove an outlier from the data set, you should carry strong reasons. Unrelated data is not considered by the analytics applications and that’s the major reason for the removal of unwanted and unrelated data. Our team of data scientists at Apeiron Techno Ventures uses advanced and optimized data science tools to follow the process of data cleansing.
e) Data Missing or Invalid: This error is not so frequently found by the team, but large sets of data always contain this error. These types of data show the missing of important information from the form or system. Sometimes it produces the error of invalid while initiating the assessment process by the team. The team collects this missing and invalid error and completes the data using external sources in order to produce authentic and accurate information or result.
Parameters of Qualitative Data
Accuracy: The data filled in the system or record should be accurate in all aspects and should not refute each other.
Outright: The data should be complete in the system and record and it shall measure all required fields which are and are not mandatory.
Consistency: Each and every word constructed in the data table should match each other and there shouldn’t be any sort of mismatch across all systems. This requires timely updates and inspection.