How to Develop and Maintain Data Hygiene?

The International Data Sanitization Consortium has defined Data Hygiene as the process to make sure that all incorrect, duplicate or unused data has been properly classified and migrated. This archival process of such data takes place on an ongoing basis through an automated policy and process. By following data hygiene practice the organizations can make sure that they are managing data effectively and there is no unwanted and corrupt data in their system.

Data hygiene can be said a process of ‘data scrubbing’ or ‘data cleansing’. We are going to discuss best data hygiene practices for any organization through this article. The steps can help you in maintaining the practice and eliminate irrelevant or duplicate data.

Data Hygiene Practices for Any Organization

Every organization knows the value of data and so they take every possible step to store and protect it. Though the organizations collect data from various sources that in itself is a challenging task but then they have to organize and streamline this data that is spread across several systems. Here the next step for the organizations is to clean and organize this data that is known as data hygiene. In order to maintain data hygiene there must be a proper and executable plan that may consist of the following steps:

1). Data Integrity Prioritization

Organizations should keep data integrated so that it can become reliable and can be stored for future analysis. Not only this even this stored data must be consistent, accurate and free from any undocumented alteration. In any way, the data must be regularly updated.

All alteration must be done to maintain the trustworthiness of data without compromising its integrity.

Here data integrity signifies that none of your data collection protocol should be broken. Even the collection strategy should be solid and well-planned that must continue throughout organization’s lifespan.

2). Data Standardization

In order to ensure data hygiene or clarity, existing organizational data standards should be examined. If you are using multiple software to collect and analyze data then their way and format of storing data may differ, even for similar data.

Like if two software are tracking and storing the name of the customers, then one software can collect and store one name in two columns. Here the first column can be for the first name while the second may be used for the last name. While another software can also store the same name in one column that may have first and last name. Here data standardization may be missed that must be maintained.

The standardization can be maintained either by applying some filters at the data collection point or by using any additional tool that can standardize the information or data of all software.

3). Cleansing of Duplicate Data

After performing standardization organizations should take the next step to clean existing data. It may seem a time-taken and difficult step, depending on the amount of existing data. Data cleaning can assure and enhance data reliability.

There are a number of ways and tools that can be used to clean data. The most common type and way is to identify and eliminate duplicate data values. As duplicate data values can skew large data sets so their elimination is quite critical.

Some of the simplest methods to clean data are aggregation, filtering and merging. These methods help in storing data into a single place where it can be checked for any duplicate value or miscellaneous error.

4). Secure Data Backup and Storage

To maintain clean data it is mandatory to store a regular backup of every data and information. To avoid any permanent data loss you should backup data as much as you can. Even many times frequent data backups may not be possible due to lack of resources, but in such cases, regular or timely data backups must be planned.

Even today organizations are using cloud storage for data backup that is a quite cost-effective and convenient option. Some nonprofit organizations are also using on-site servers to store and backup their data regularly.

5). Purchase a Clean Data Plan

Another aspect to maintain data hygiene is to buy a clean data plan by managers and leaders of the organization to maintain data integrity. Once the process of data cleaning will begin throughout the organization, even the other employees of the organization will also begin collecting and cleaning of data to make it more accurate.

Upper management can help the organizational employees in cleaning data and maintaining its hygiene by availing them appropriate tools and plans. Even just after the initial step of data collection they can plan and maintain data hygiene and clarity.

6). Validation of Data Accuracy

Real-time data accuracy validation is an imperative aspect of data hygiene. You can use some tools like list imports and any popular tool for email verification. Effective marketing can be planned and achieved through high-quality data. To maintain the data accuracy without tools you may need a lot of manpower.

Data accuracy can be maintained and achieved with or without tool but organization must include it in their data cleaning plan . All data after cleaning process must be validated to ensure its accuracy.

7). Develop a Standard Data Quality Plan

For cleaning data set create some standards and the key performance indicators or KPIs. Identify and enlist the ways to attain them. Describe the ways to maintain the data quality and the way to track it.

You should know where usually these data errors are found. Identify and explain the root cause of any incorrect or inappropriate data. Here one of the sources of incorrect data is the entry point.

Some Additional Success Factors
Whether you work with a single data source or multiple data source try to remove inconsistencies and major errors. By maintaining data health you can get better ROI on your database. By implementing tools that reduce manual inspection and streamline the process you can get a better and optimized result. To maintain data hygiene the most important aspect and the step is to identify the source of dirty data. By preventing any wrong or incorrect entry you can get clean and accurate data.