Even though data is a company’s asset, some companies fail to maintain the data’s accuracy. Reports indicate that 26% of the data collected is inaccurate. Such alarming statistics related to incorrect data have led businesses to start focusing on Data hygiene. Data hygiene helps businesses adopt some rules to clean out dirty data such as incorrect manual entry, spelling mistakes, omissions of data, and the presence of redundant data in various representations.
Here are some of the issues these businesses go through when cleansing their data, along with the solutions to the same.
What Is Dirty Data?
Dirty data refers to customer or business information that is incorrect, duplicated, or missing. The data becomes dirty when an employee erroneously creates a duplicate of a customer record, misspells an address, fills CRM with spam emails, or updates the wrong date. This data becomes useless and only takes up unnecessary space in the system.
What is Data Cleaning?
Data cleaning is one of the most important steps in data quality management. In this process, errors, inaccuracies, and inconsistencies present in the database are identified and then corrected or removed. The goal of data cleaning is to make the data more accurate, consistent, and usable for further analysis or modeling. This process may involve filling in missing values, removing duplicate records, converting data into a consistent format, dealing with outliers and other anomalies, and correcting inconsistent or inaccurate values. There is no definitive way to specify the precise phases of the data-cleaning process. The procedures differ from dataset to dataset. But it is essential to create some ground rules for your data cleaning procedure to ensure that it is carried out correctly.
Different Types of challenges and solutions
Here are some common challenges that you may face while removing dirty data. We have given the solutions that can help you overcome these challenges.
#Challenge: Incomplete Data.
Incomplete data means empty cells or blanks. It happens when a customer leaves out a section during form fill-ups or the data-entry clerk creates a blank.
#Solution: Use the Deletion Method
The deletion method includes eliminating any data entries with blank values. This method is beneficial to databases with large amounts of data. The primary benefit of the deletion method is it removes data without significantly changing meanings . As an alternative, data scientists can contact the relevant participants to fill in the missing values.
#Challenge: Duplicate Data
Duplicate data means records that contain the same information multiple times in similar or different formats.
#Solution: Merge The Data
Users can merge leads, contacts, and accounts based on customizable criteria using external data de-duplication solutions like ZoomInfo OperationsOS. But before beginning with the process, it’s necessary to set the criteria for identifying such duplicate data.
#Challenge: Insecure Data
Insecure data refers to data that is not properly protected from unauthorized access, alteration, or theft. Insecure data is often stored or transmitted in an unencrypted format, or the systems used to store and process the data may have vulnerabilities that could be exploited by cybercriminals.
#Solution: Follow Data Governance
The only solution to deal with insecure data is following data governance. Data governance is the process of defining the data policies, standards, and processes that ensure the proper management of data assets. The goal of data governance is to ensure that data is properly managed throughout its lifecycle, from creation to deletion, to support the needs of the organization while also protecting sensitive information.
#Challenge: Inconsistent Data
Inconsistent data means the data lacks proper segmentation. Segmentation means grouping the data under specific tags.
#Solution: Standardize Your Data
First, establish some universal naming conventions for your business. Tools like ZoomInfo help keep the records in batches for more uniform field names and precise segmentation for existing inconsistent records.
#Challenge: Data hoarding.
Data hoarding refers to the practice of collecting and retaining large amounts of data without a clear purpose or plan for using it. This can lead to a number of problems such as security risks, privacy violations, inefficiency, waste of resources, etc.
#Solution: Perform Regular Data Cleaning
A database filled with too much data can be confusing and a sign of not following regular data hygiene. There is no scientific solution for too much data. Companies have to conduct regular data-cleaning processes and keep only the valuable data in the system.
#Challenge: Inaccurate Data
Inaccurate data means that the details filled in your database are absolutely wrong or invalid.
#Solution: Opt For Data Enrichment
The first step is to keep track of all data entry points and identify the root of erroneous data. If the issue is caused by external data sources, such as web forms or connected systems, opt for help from third-party sources. There are various data enrichment outsourcing companies that will guide and maintain data accuracy.
Conclusion
Data cleaning is a crucial step for organizations, as it can greatly impact the accuracy and quality of the results obtained from data analysis or modeling. Hence, every organization should perform regular data cleansing processes to keep its data free from duplicates, inaccuracies, and missing values.
However, if you feel that the entire process is too complex and time-consuming, you can simply outsource data cleansing services from a reputable company. These companies will perform an in-depth analysis of your dataset to keep your data clean.
Author Bio
Gracie Ben is a data analyst currently working at DataEntryIndia.in, a leading company providing data entry & mining services & other data-related solutions. For more than ten years, she has actively contributed to the growth of many enterprises & businesses (startups, SMEs, and big companies) by guiding them to utilize their data assets. Having a keen interest in data science, Gracie keeps herself up-to-date on all the latest data trends and technologies shaping the industry and transforming businesses. She has written over 1600 articles and informative blogs so far covering various topics, including data entry, data management, data mining, web research, and more.
