In today’s digital era, where humans produce close to 2.5 quintillion bytes of data every day, dirty data is a concern for businesses irrespective of size and industry. This is because any organization that handles duplicate, inaccurate, and outdated information will have to deal with consequences such as:
- Ineffective marketing efforts: Most businesses these days use targeted promotional campaigns. But what happens when the customer information in your records is dirty? It drains time, revenue, and effort from your organization.
- Wrong decisions: Data drives decision-making for businesses. But if decisions depend on dirty data, it can lead to costly ramifications.
- Bad customer experience: A business needs to maintain solid communication with its current and prospective customers to develop a loyal customer base and sustained buyers. But when data used to contact customers isn’t scrubbed, the quality of interaction takes a hit. It can be frustrating for a customer when they experience something they do not expect/deserve. This can also lead to customer churn. Therefore, data cleansing is vital for every business. Data cleansing is the process of identifying and rectifying corrupt or flawed data from a data set, table or database. It helps you substitute, alter or delete dirty data.
Elements of Data Cleansing
Data cleansing includes five elements — data standardization, data validation, data analysis, quality check, and data deduplication.
Data Standardization
Most businesses use data from multiple sources such as data warehouses, cloud storage, and databases. But data from distinct sources may not be in a consistent format, leading to trouble down the line. This is where data standardization helps. It is the process of converting data into a consistent format.
Data Normalization
It is the process of organizing data within a database. This involves making data tables and identifying relationships between those tables based on the rules designed to reduce data redundancy and improve data integrity.
Data Analysis
Data analysis is the process of analyzing data using logical and analytical reasoning to get valuable insights. The derived information helps make sensible decisions.
Quality Check
Businesses need good quality data to make the right decisions. Therefore, quality checks are essential.
Data Deduplication
Data deduplication refers to the process of eliminating duplicate data in a data set by deleting an additional copy of a file and leaving just a single copy to be stored.
In this process, data gets divided into several blocks that are compared with each other. Each block is assigned a unique hash code. If the hash code of one block matches the hash code of another, it is considered a duplicate copy and gets deleted. This ensures that only a unique copy of the data is stored. Deduplication can detect redundant copies of data across data types, directories, servers, and locations.
Importance and Benefits of Data Deduplication
The storage capacity for most small and medium businesses (SMBs) is limited, but the amount of data generated, transferred, and stored is steadily growing. The process of data deduplication helps tackle this issue by:
- Reducing the storage space requirement by storing only a single copy of a file
- Minimizing the network load since less data is transferred, thus leaving more bandwidth for other tasks
Deduplication helps your business:
- Recover faster after an incident
- Save on storage costs
- Improve productivity
- Reduce version control issues
- Enhance collaboration
- Meet compliance regulations
Always remember that training and process documentation helps empower your employees to be a part of deduplication efforts.
You do not have to begin your deduplication journey alone. We are here to help. Our expertise and knowledge make integration of the process into your business easy. Contact us to get started.
