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Essential data maintenance activities explained
Data maintenance is the ongoing process of collecting and organizing data in a way that’s accessible and useful for the organization. The process ensures organizations retain high-quality data that is both accurate and up-to-date.
Outdated or irrelevant data leads to poor decision making which costs an organization money. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. Given the potential losses an organization might incur with poor data quality, data maintenance becomes a necessary investment.
Why maintain your data?
Imagine if you could help grow your revenue and decrease operational costs at the same time.
Proper data maintenance can help you achieve those goals.
Poor data quality, on the other hand, can have disastrous consequences for an organization. Data storage may seem affordable, but the costs can skyrocket if you don’t delete irrelevant records. The negative repercussions extend throughout an organization as teams may find themselves working with outdated data, greatly hampering their effectiveness.
By efficiently maintaining data, an organization can improve its records management and make it easier for teams to identify growth opportunities.
Other benefits of data maintenance include:
- Streamlined compliance processes
- Keeping data up to date
- Promoting a more accurate database
- Reducing downtime
- Simplifying data recovery
- Automating tasks to reserve resources
- Gaining visibility into problems
- Setting up clear processes and procedures
- Reducing data silos
- Preventing data redundancy
- More accurate forecasting
- Improving customer segmentation and personalization
What are the three stages of data maintenance?
Data maintenance generally falls into three stages:
- Data classification
- Data retention
- Data disposition
Each stage has different strategies to ensure a high-quality data inventory. Let's dive a bit deeper into each stage and explore the fundamental data maintenance activities associated with each stage.
Accurately analyzing and classifying records is crucial to determining retention periods and disposition. Classification is an important step in understanding data sensitivity. Depending on your industry, you may be subject to laws and regulations regarding the security of the personal data you collect. The EU’s General Data Protection Regulation (GDPR) for example, imposes strict requirements on the handling and protection of individuals' Personally Identifiable Information (PII). Failure to uphold compliance can result in hefty penalties. Unless you understand what data you have, where it is located, and its level of sensitivity, you cannot hope to protect it.
By accurately classifying data, you assign a different level of security and management to each category, allowing for more granular protection measures based on the sensitivity and importance of the information. This helps your organization determine where data should be stored, manage access, and understand the minimum time data should be stored before it is properly disposed of. For example, the Health Insurance Portability and Accountability Act (HIPAA) mandates retaining specific document types for six years upon creation or the last effective date.
For instance, an eCommerce shop collects volumes of PII and Payment Card Information (PCI) data. These kinds of records require a different level of security due to the sensitive nature of the data. When you have properly classified your data, it becomes easier to make informed data maintenance decisions.
This stage is also crucial for data purging. This tactic checks data quality and identifies Redundant, Obsolete, or Trivial (ROT) data. Records which contain no relevant data are costly to store. ROT data also poses regulatory and security risks. Conducting a file analysis will help you identify the data you don’t need to hold on to and determine what to do with it.
Data must follow a retention schedule and a predefined timeline that ensures organizations comply with regulations and analyze only relevant data. Once data is classified, you can determine how long it should be stored. Generally, data must be retainedas long as the records meet one of the following criteria:
- Subject to a legal hold,
- shows the continuous business value,
- mandated by compliance regulations.
While data is retained, it should receive proper records management and security measures within its lifecycle. Database maintenance tasks may include the removal of duplicate records, rebuilding indexes, fixing data errors, or checking for abnormalities in the database.
Once you no longer need data for compliance regulations, legal holds, or ongoing business value, the retention period should end and data needs to be securely disposed of.
There are two main ways to handle data disposition–data deletion and data archival. If data has permanent value, then an organization may consider creating a policy for transferring it to a secure archive.
Organizations will typically have to delete data entirely once the retention period is over. This could involve data destruction, disk wiping, or physical device destruction. Employees should be trained on record retention policies to ensure proper deletion. Otherwise, improper disposal of data could lead to compliance issues.
For example, a Massachusetts Health Center’s data storage vendor had failed to properly dispose of data in hard drives, directly violating HIPAA guidelines, and consequently resulting in almost 117,000 patients having their protected health information (PHI) exposed.
An efficient data disposition strategy could have helped communicate the necessary procedures for employees and third-party vendors to follow, essentially preventing a breach of that scale.
Data maintenance challenges
Organizations often begin collecting data without identifying a specific purpose. They then become weary of deleting the data “in case they need it one day”. This results in the creation of an extensive and disorganized database. Organizations in such a situation may find it challenging to initiate the data maintenance process, particularly when facing constraints in resources or time for a thorough evaluation.
Tasked with maintaining an overwhelming amount of data, organizations may opt for data cleansing instead of data maintenance. Data cleansing identifies errors and tries to remedy them. However, it is often a reactive tactic that doesn’t contribute to improved data management in the long term.
By having a data maintenance strategy in place, organizations are more likely to reap the benefits of an organized database and streamline operations.
Maintain data more efficiently with RecordPoint
Enhance data maintenance and lower both the risk and total cost of ownership (TCO) for your data with RecordPoint.
The RecordPoint Platform gives you a clear understanding of all your data, so you can safely and confidently dispose of data you don’t need, and safely manage the rest. Take your data maintenance to the next level with automated categorization.
Why data minimization matters
Retaining redundant, obsolete or trivial data (ROT) raises costs and business risk. Data minimization is the answer, and can enable your team to achieve more.
A guide to data classification: confidential data vs. sensitive data vs. public information
Learn why it's important to classify your data, understand the main types of data classifications, and how automation can make things easier to keep your company's data safe and compliant.