Data cleaning services

Hire Experts to Clean Your Data Professionally

Are inaccurate, poor, and duplicate datasets turning your business decisions into a chore?? Are you facing difficulties standardizing your business data even after dedicating large amounts of time and resources?

The answer is to let experts take care of validating, enriching, and de-duplicating your organization’s data with data cleaning services.

However, finding the top data cleaning companies to clean your data is a complicated task. However, you can make things simple by contacting a dedicated data cleaning service provider like Express Analytics, which provides 100% assured services.

Have a dataset of up to 100,000 records?

Why Does Your Business Need Professional Data Cleaning Services?

Having dirty data in your company can lead to making wrong decisions according to that data, and you will be spending money on marketing efforts that could have fewer budgets if you had already cleaned up your database.

Poor or dirty data allows companies to make incorrect decisions, leading to poor customer satisfaction and the waste of money and energy. Without a standard practice to begin and maintain clean data, poor or dirty data issues are expected to increase. Loss of operational productivity as the resources spend their time evaluating and confirming data accuracy manually. The productivity of data experts is wasted as they invest time in cleaning, normalizing, and standardizing data.

How Does Data Cleansing Simplify Your Business Operations?

With well-maintained and clean data, you can shift your focus to revenue-producing activities and not spend much time fixing errors. It boosts the ROI of any promotional activity or marketing effort.

Boosts productivity

Our data cleansing services ensure your data is clean and neatly maintained, thereby boosting efficiency.

Quicker analysis

Our professionals can do database cleaning and allow you to make quicker and more correct decisions.

Boosts revenue

We do provide reliable and up-to-date data for productive analysis and decision-making, which results in higher revenue.

Overcome Data Challenges

Steps to improve Data Quality

Data Cleansing

Remove Duplicates: Keep 1 Latest Customer Record (Merge/Delete)
Fix Invalid Emails: Correct the Email Domains and Top-level domains
Handle Missing Values: As Customers are from the USA, the Default country can be “USA”
Remove Invalid Foreign Key Entries: If Sales tables refer to customer_id, if customer_id is not in the customer table, that must be handled

Data Transformation

Normalize Customer Names: Make Customer Name and Last Name as UPPER CASE/INIT CAP
Aggregate Sales Data
Derive Additional Fields: Age Group (Below20/21-35/36-50/ Above 50)

Data Validation

Check Email Format: ‘%_@__%.__%’
Validate Age Range: Age < 0 OR Age > 120
Date Range Validation: Orders should be between 01-JAN-00 to SYSDATE
Unique Constraint Validation: Customer IDs should be Unique
Pattern Matching Validation: Phone Number should be of Format <+91-9999999999>
Length Validation: Email should not exceed 255 Chars
Combination Validation: if Age>18 , Email should be Present

Some insights from analyzing Customer Profiling:

Email Format Issues: ‘jane.smith@site’ is not valid.
Missing Values: The email field for Customer ID 3 is missing.
Duplicates: Customer ID 1 and 5 are duplicates.
Age Range: Values range from 28 to 52
Country: Values include ‘USA’, ‘UK’, and ‘Canada’.

Data Cleansing Service Offerings

Data Integration and Auditing

Integration involves combining data from multiple sources or formats into a unified dataset. Remove inconsistencies, remove errors, and conduct referential integrity checks.

Data Verification

Data verification is the process of verifying that data meets predefined criteria, ensuring its accuracy, integrity, and consistency.

Data Hygiene

It is the act of cleaning groups of data or datasets to ensure they’re organized and correct.

Data Tagging

It minimizes the amount of time spent on secondary data analysis and improves organizational decision-making.

Synthetic data

Synthetic data reflects real-world sensitive data, statistically or mathematically. It is not the same as randomized and augmented data.

Data Quality Management

It can reduce the waste of energy and time needed to manage incorrect or low-quality data by manually reprocessing it.

Outlier Treatment

Treating outliers and missing values is a major step in data cleaning, and after this step, data analysis or data pre-processing can be performed.

Data Removal

It involves the removal of duplicate and irrelevant data.

Why Choose Express Analytics for Data Cleaning Services?

We at Express Analytics convert data into the preferred format, remove obsolete entries, conduct RI checks, clean mailing lists, index in designated fields, and more. Each record is carefully checked, cleaned, and renewed at regular intervals. Our experts maintain thorough quality control benchmarks via multi-layered quality checks and save you money and time by offering constant, accurate, and clean data.

Making your data errorless and cleaning it manually is tedious and complicated. However, our proficiency in using various data cleaning tools ensures the cleaning of your data and organized evaluation in less time.

In our data quality management approach, we use systematic workflow and various quality checks for the constant delivery of classified, organized, and clean data to machine learning (ML) startups and AI companies.

Have a dataset of up to 100,000 records?

Benefits of Our Data Cleansing Services

Improve customer acquisition activities
Increase productivity
Simplify business practices
Increase in response rates and revenue
Optimize the productivity of experienced resources
Improve efficiencies by depending on automation
Preserve priceless resources