Understanding Data Integrity
We talk about data a lot, because it is used in so many systems and processes in every industry imaginable today,
but what is it in reality? Data is the digital representation of information, and like information, it is only
useful if it is accurate, consistent and reliable.
For instance, if someone tells you that grass is, in fact, red, that is information, but it’s worthless all the
same, because it is wrong, either through error or deliberate misrepresentation. Data is the same. It only has value
if it is reliably and consistently accurate.
We use the term data integrity when looking at these factors for data whether it is stored or used. It’s a
wide-ranging term, that takes into account the data itself as well as the security of the data (to prevent
breaches that could alter or corrupt the data) , how the data is managed and so on.
As an example taken from the automotive industry, a fleet management application might record a
vehicle’s milage. Data integrity would cover the reliability of the entered data, how it is verified before entry,
who can change that data and from where, how the data is secured and so on. The goal is to know that the data is
accurate and can be relied on when using it to schedule maintenance and so on.
Why is Data Integrity Important?
When measures are taken to ensure data integrity, the data itself can be relied on to be used as required and
provide the accurate insights needed for any task. Conversely, if data integrity is not maintained, however the data
is used, there will be uncertainty as to the viability of any results.
This matters in all situations, but in industries such as healthcare, a lack of data integrity would prevent
effective treatment, and even lead to deaths.
It is also crucial to ensure that your vehicle data is safe and secure from potential external threats. You can protect your data with the AutoPi CAN-FD Pro.
Maintaining data Integrity
Maintaining data integrity requires a holistic policy that incorporates defined policies and procedures, hardware
and software protections to create a cohesive, end-to-end approach for data verification.
This includes:
Backups — Backing up data on a regular basis to ensure that it is protected should something
happen to cause data loss.
Error Checks — Using processes such as checksums or data validation, often both, errors in data
can be spotted during transmission or while stored.
User Access — Limiting who can access and change data reduces the chances of human error or
even malicious alterations in the data. It also makes it easier to trace issues caused by human error.
Security — There are a number of security measures that can be implemented to avoid data
issues, such as firewalls to prevent external attacks, and encryption for both internal and external
data manipulation.
Accurate Documentation — From access logs to change records, keeping detailed information about
how and why data was used will help track and issues.
As we can see, data integrity is an all-encompassing idea that requires multiple ongoing initiatives to maintain.
AutoPi and Data Integrity
Designed for accurate Telemetry and IoT solutions , AutoPi has accurate, trusted data at
its core. Without that, it
cannot provide the functionality it has been designed for. As you may expect then, AutoPi features a range of
processes designed to maintain data integrity so that the system can be used for critical decision making, as
intended.
These are:
Secure Data Communications — Data is at its most vulnerable during transmission , which is why the AutoPi device uses encrypted communications to maintain data integrity throughout by preventing any unauthorized
access.
Error Checking — The system introduces error checks and validation protocols at every stage to
ensure data integrity is maintained from end-to-end within the system.
Multiple Data Backups — Using multiple storage locations, data is protected against localized
physical disasters and digital attacks or incidents.
Regular Audits — Continual appraisal of processes identifies errors before they can impact data
integrity.
Accepted Best Practices — Following industry standards and accepted best practices ensures
secure data handling and integration of new innovations in data integrity.
Conclusion
As data has become so important to so many activities and businesses, the accuracy and reliability of that data has
become a crucial part of everything we do as well. By implementing a range of data integrity protocols and
initiatives that include both hardware and software as well as policies and procedures, and ensuring they are
assessed and amended as needed, data integrity can be maintained, and with it, the validity of any process using
that data.
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