Why Every District Leader Needs to Make Data Integrity A Priority

As a school leader, why should you make data integrity your priority? Even though they share a word stem, integrity and integration have different meanings for school users. Both functions are critical, but effective integration (bringing data together) depends on scrupulous integrity (keeping data perfect). When it comes to data integrity, the highest standard is when every bit of a data record is perfectly identical to the original in content, format, and size. That standard is easy to explain, but devilishly hard to maintain.

Previously, our team has addressed data fragmentation as the biggest obstacle to family engagement. We identified data integration as one of the key strategies to fight fragmentation, but sometimes data fights back. In this post, let’s unpack the root conditions that undermine data integrity and what we can do to protect and preserve high-quality data.

Data Integrity Defined:

Have you ever visited a doctor’s office or the DMV and had to fill out a photocopied form that looked like a fourth-generation school worksheet? The printing is faded—the lines are slanted. Pieces of the questionnaire might be cut off or washed out. That copy of a copied copy lacks integrity. It’s impossible to maintain quality through the process of copying, storing, reproducing and reusing.

What we see with our eyes in a physical copy is a concrete representation of how processes can undermine integrity in a digital system. Think of original data as a clear, high-resolution scan of a legal document. Then, imagine reproducing that form on a copy machine, scanning it onto a hard drive, taking a screenshot of that image before printing the result on a printer that is running low on toner. It may seem like an improbable chain of misfortunes, but that scenario is not far off from what we do to school data.

We ask parents, students, or teachers to enter data into different systems. Then, we transfer or copy that data into another archive. Sometimes we make duplicates and backups of original data in different formats that are stored with differing protocols. Further down the data chain, we might import that data into a new roster or communications log. However, every step of the journey—from entry through storage and conversion to retrieval—is an opportunity to lose a little integrity.

The “Severe Seven” Threats to Data Integrity

Anytime we duplicate a data record, we create competing versions of the same information. Unless our systems have strong rules for deciding which record is authoritative, an application or platform might rely on the “B” version and ignore the original “A” file. That would be fine, unless we ever needed to change the original record.

Suppose a student changes addresses; a parent changes phone numbers; or an emergency contact moves out of town? Are the systems we use smart enough to keep track of “A” “B” and even more versions of the same data? As soon as data varies, integrity is at risk.

Many systems manage integration by allowing different systems to edit data through intelligent interfaces. That practice automates the process of updates, but it also introduces another vector for deviation. Even when the data translation is perfect, human error or oversight can create gaps or overlaps in data collections.

All of this generally works against data integration. If we are trying to track parent communication or perform predictive analytics based on student performance, we need to depend on high-quality data. To clarify, if two records refer to the same student with slightly different details, we may lose the ability to track or report with accuracy.

Data Integrity Elevated:

As school engagement architects, technology planners should insist on strong controls and protocols for data integrity. At a minimum, data systems should include routines and queries that check multiple instances of the same record for discrepancy and duplication. Running a discrepancy report should be a routine and reliable process for system administrators and database owners.

Another strategy for preserving integrity is having the owner of the record verify the details on some regular rhythm. Validating contact information and account status is a simple way to add another set of eyes onto every data set.

It is said that ancient scribes and scholars would reproduce manuscripts and then count characters in every line to make sure the copy matched the original. The printing press made the process a lot easier, before early computer programmers used a function called checksum to reproduce in seconds what took classical scholars many hours.

To make data integrity a priority, we don’t have to dial back to ancient times, but we do need to understand the importance of data quality and commit to practices and protocols that keep data clean.