Data Cleaning

Identify, analyse and process raw distorting data giving birth to quality data.

Being the primary stride in the data preparation process, data cleansing is an indispensable process without which the absolute results of data analysis could get affected. We have invented a user-friendly interface making the procedure all the way simpler. We produce correct, consistent and reliable data complementing your needs.

Data Cleaning is one of the most important steps that are not much talked about in research studies. it's definitely not the fanciest part of any research analysis but a very crucial at the same time to get appropriate and desired results from the data, however, this is the most important yet not looked upon step when it comes to research studies. our teams of experts take your project from the initial steps and thereby increasing the robustness of the results delivered.

Checks We Do While Cleaning Data

Data cleansing could prove to be a tedious process involving identification of errors. Spotting corrupted data and to manually rectify, erase them when necessary. That is why we involve the usage of software tools to correct, cleanse and monitor data to ensure data precision. We preserve our two objectives of data cleansing, that are accuracy and consistency, by employing the following steps

Remove unwanted errors

The initial step of data cleaning is removing unwanted observations from the dataset or records that we have. These might include irrelevant or duplicate observations.

Processing errors

We prevent errors from clogging up the work process of data interpretation by focusing on technical variations even in micro scale. We adapt first in, first out method to process errors.

Structuring process

To prevent further impediments, we prefer standardizing the data at the point of entry itself. This prevents duplication of data and keeps it accurate.

Inspecting outliers

To enrich data, one needs to identify the dataset for possible formatting deformities, deficiencies, duplicate entries, inconsistencies, excess or repeated answers and data in contrary with typical statistical distribution.

Rectifying inliers

We invest in data cleaning tools to automate the researching and analysing raw data. We involve techniques like regression analysis and plausible checks to re-measure the data to estimate error-rate and remove duplicates.

Placing values

Our statistical outlier detection methods identify extreme and misplaced values and distinguish between misplaced variables. We employ predefined cut-off points to detect logically impossible values and correct them.

Handling missing values

After observing common errors and replacing them, Our expert panel of analysts use debriefing and data enumeration to resolve this by using statistical values making the data more informative and increasing the accuracy of the re

Recording modifications

A researcher needs to seek the services of a third-party source to append the changes after analysis. We provide the services of publication and auditing the modified fields by recoding variables before documentation.