If your data is outdated or irrelevant, it leads to poor decision making which costs your organization money. According to Gartner, poor data quality costs organizations an average of $12.9 million. Considering how much an organization could lose with poor data quality, it seems like data maintenance is a worthy investment.
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 and can make better decisions from it.
Why maintain your data?
Imagine if you could help your revenue grow and decrease operational costs at the same time. Proper data maintenance can help with those goals.
Poor data quality can have disastrous consequences on an organization. Data storage may seem affordable, but it can unnecessarily add up if you don’t delete irrelevant records. It may also have an impact on sales and marketing efforts since teams may end up working with outdated data.
By maintaining data, an organization can improve its records management and make it easier for teams to identify opportunities for growth.
Some other benefits of data maintenance include:
- Ensuring compliance with regulatory requirements
- Keeping data up to date
- Promoting a fast database
- Reducing downtime
- Simplifying data recovery
- Automating tasks saves time and resources
- Gaining visibility into problems
- Setting up clear processes and procedures
- Reducing data silos
- Improving customer segmentation and personalization
What are the essential data maintenance activities?
Data maintenance generally follows three stages:
- Data appraisal and classification
- Data retention
- Data disposition
Each stage has different strategies to ensure a high-quality data inventory. Let’s review each stage a bit further and what the essential data maintenance activities are within each one.
Data appraisal and classification
Accurately analyzing and classifying records is crucial to determine 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. By accurately classifying data, you can give it a different level of security and management. This helps your organization determine the minimum time data should be stored before its disposed of.
For example, your organization may collect personally identifiable information (PII) or 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’s easier to make data maintenance decisions on how to handle it.
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 needs to follow a retention schedule. A pre-defined timeline for retention ensures organizations are following compliance regulations and only analyzes relevant data. Once data is classified, data management can determine how long it should be stored. Generally, data retention occurs as long as the records meet one of the following criteria:
- Subject to a legal hold
- Has ongoing business value
- Required due to compliance regulations
While data is retained, it should receive proper records management and security measures within its lifecycle. Database maintenance tasks can include removing duplicate records, rebuilding indexes, fixing data errors, or checking for abnormalities and problems in the database.
Once you no longer need data for compliance regulations, legal holds, or ongoing business value, then the retention period should end and data needs to be securely disposed of.
There are two main ways to handle data disposition–data deletion or data archival. If data has permanent value, then an organization may consider creating a policy on transferring it to a secure archive.
Most of the time, an organization will need 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 failed to properly dispose of data in hard drives in violation of HIPAA guidelines. This led to almost 117,000 patients having their protected health information (PHI) exposed.
Efficient data disposition strategy can ensure employees and third-party vendors are aware of what is needed to properly dispose of data.
Challenges companies face with data maintenance
Oftentimes, organizations begin collecting data without identifying a purpose for it. Then they fear deleting the data just in case they may need it one day. This leads to building a large and unorganized database. It’s challenging for organizations to know where to begin the data maintenance process, especially if they lack the resources or time to conduct a proper evaluation.
In the face of an overwhelming amount of data, organizations may choose data cleansing instead of data maintenance. Data cleansing identifies errors and tries to remedy them. However, data cleansing is often a reactive tactic and doesn’t improve data management in the long term. While it’s an important part of an overall data maintenance process, it won’t create an efficient database on its own.
By having a data maintenance strategy in place, organizations are more likely to reap the benefits of an organized database.
How technology solutions can help
Frequent data maintenance ensures efficiency and organization. However, many organizations lack the funds and expertise to create maintenance plans and properly manage their databases. Using technological solutions can help organizations automate the process of appraising and classifying data and properly tag them with the correct retention schedules, holds, and disposition policies.
RecordPoint uses a single, user-friendly dashboard for easily accessible information, improved reporting, and greater content oversight. We leverage technology like machine learning and Connectors to automate data management in the background.
Improving your data maintenance enables your company to grow and helps you ensure relevant compliance regulations are followed. It also gives your employees relevant data on your customers. Armed with quality data and timely insights, your sales and marketing teams can improve customer relationships and grow the company.
Learn about how RecordPoint can solve your data maintenance challenges. Schedule a demo.