Gradually airlines are shifting to a “re-launch” mode. Going forward both operational costs as well as capital expenditure will remain closely managed. Cost saving initiatives and efficiencies will be prioritized even further. The centrepiece of this will be for sure further digitization initiatives. The foundation of any digital strategy will be the data supporting this, its integrity and quality. In this article, Rob Vermeij shares some experience from the field in how airlines can evaluate and manage their data integrity to support successful digitization strategies:

The terms data quality and data integrity, probably you have heard these terms before across the engineering floor and especially in projects focusing on M&E systems. Mostly used as two interchangeable words, however there is actual a difference between the two.

To have good Data Quality, you generally need Data Integrity, but one is not excluding the other. Sounds complicated? Allow us to explain.

The Definition 

Data Integrity concerns the accuracy and consistency of data, while Data Quality is best described as a measure of how useful the data is in the end to the organisation and for its purpose. Officially data quality covers:

  • Completeness
  • Uniqueness
  • Validity
  • Timeliness
  • Accuracy
  • Consistency

Both are essentials metrics to get right in continuous airworthiness management, but of these two Data Integrity is at the base of all things.

How to ensure Data Integrity?

Data integrity is based on structure, logic, and overall robustness. It needs to ensure a single source of truth, and a consistency throughout where that data might pop-up. There is more than one way to accomplish this to satisfactory levels.

The start is the software you use such as your M&E system, and lucky, most modern software has been designed to provide the first base level of data integrity. It even relies on this data integrity to even work, otherwise you will get the familiar error pop-ups. This is all driven by the hierarchical database in the background, where each data point is linked to another data point, ensuring it is always consistent and accurate. A particular piece of data can only be changed on one point in the database which automatically links to all the related records.

While software lends a helping hand, it is the input and how you work with a system that even counts more. You will have to consider multiple aspects:

Clear definitions

It all starts by having clear, unambiguously, base definitions for core data in the system. Think about ATA chapters, aircraft types, part numbers, material classes, delay codes address codes etc. This ensure consistency throughout the rest of the system.

You would think is has generally been standardised in the airline industry. But in our experience and the numerous airlines we have advised, definitions are far and in between, and the industry standards were introduced overtime (spec2000, ATA200) are often not implemented due to the legacy of the dataset.


Integer data has birth to death traceability. Which means it can always be traced back to its source, the source of truth, and any revisions or alterations are logged. You and I being in aviation are no stranger to this, the whole CAMO principle is based on traceability.

Yet, even with digital systems this remains a huge challenge. How often did you not find a separate Engine tracking Excel sheet, or LLP sheet, next to the main system? This immediately deteriorates your data integrity as there is no traceability, no single source of truth. The sharp-witted reader will argue this due to the existence of the Dirty Fingerprints as ultimate, traceable, source or truth and he/she is right (in theory at least). However, on a daily basis in the daily flow of work, the general to-go source is in a digital form which also brings us to the next points.

Clear procedures

A system(s) can be hyper-modern has many smart features and a super robust database, nonetheless it all starts with the human (you and your team) operating it. While systems try to limit the amount of ways someone can use it, us humans are infinitely more creative (for now) which allowed us to design these systems in the first place. To keep data integrity only works if your business processes and procedures also try to warrant that integrity by being clear, non-ambiguous and accessible.

Hence, a “maintaining an Excel sheet next to the actual system” process, should never be in a procedure aimed at keeping data integrity. It can only be considered if the procedure clearly appoints the source of truth (either the excel, or the system) and thus retaining traceability.


As described above, clear procedures are fundamental to keeping data integrity. The only way to have your team follow these procedures and keep current on any changes is a rigorous and continuous training programme. People tend to develop their own working style and will introduce small workarounds that could become the norm if not attended to. Training and evolving procedures are there to safeguard the norm of working with the system and thus data integrity. 

Limit the amount of systems

It is not entirely preventable to have different systems in your operations. FligtOPS, E&M, Finance and HR are the common 4. What should be prevented, however, is to have multiple systems with similar functionality and purpose.

For example: In our experience, we have seen multiple instances where there was a separate system for material management and E&M. E&M and material management are very interwoven processes and more often than not, the E&M system is fully capable of handling the logistical aspects as well.

Having two systems running in parallel, where (repair) orders need to be (re)created in both, is a huge risk for data integrity. What is the source of truth when things do not match? Which system depicts the ultimately correct status and details of the order? And so on.


Data comes in all forms and flavours. While everybody thinks we all speak the same language in aviation, there is unfortunately still a huge variety in how each airline (or country) designates universally used data. The industry is global, aircraft transfer to different owners world-wide, thus this mismatch in definitions and notations is actively affecting the data integrity of many airlines. Think about things such as, usage of ATA sub chapters, vendor address codes, part number notations but also the different interpretations of the same maintenance requirements.

Luckily, the industry is working on standards such as the ATA200, Spec2000 and Spec2500, Cage coding etc. However, it is only you whom can decided to adopt these.

How to ensure Data Quality?

Data can have great integrity, e.g. a part number can be defined and has traceability who created or amended it. Furthermore, the same part number links through to all the serialized components that were ever recorded in your system and, also these have traceability of their movements.

But how valuable is this data to you, if it only consists of a part number, maybe a description, a serial number, and some movement history? Furthermore, what if you suddenly have duplicated parts in your system, albeit with a slightly different PN (dashes etc)?

This is the territory of data quality, it is all about how useful the data is to its cause and your operation, it should add value for your business and intended use in the end. You can distinguish this on three aspects:


A simple question at first, but something that causes a lot of headaches during daily business: Is your data in the right (expected) format for your business?

Luckily, many things are already covered by most modern software, such as date notation, order number logic etc. However, software can only go so far and still allows for some wiggle room, the dreaded “free text” fields for example. Are your work steps for example written down in the format you require for your mechanics to understand, or has someone been creative?

If the data is not in the formats as expected, you say it has “bad validity” which is a sign of problems with your overall data quality.


Is your data recent enough on a continuous basis? For example, what is the delay between completing the workorder physically versus processing it in the system. Is this workable or should it ideally be earlier?

Another example, the monthly reliability reporting, if your team is struggling to retrieve all the data on time for that monthly, or the report is structurally delayed, this could indicate on bad data quality.

In most cases, the timeliness of data is a direct result of working procedures, equipment, and system integration. If you notice your data is not actual, that are the three areas to start your root cause search.


What is a part number to your organisation if it does not have a description, ata chapter, material class, alternates and so on? Meta-data is any data that gives more information surrounding a core definition or subject. It is what helps us humans, to better understand what something is, how it’s related to other things and what it is for.

Good data quality means that all the meta-data that you require for your operation is there and for everything (so no gaps). For the meta-data itself, the same rules apply again for data integrity and data quality. It needs to be valid, accurate and actual.

Think about modification revisions, part number information (dash numbers!), task card information and so on. Meta-data is always evolving and in need to be kept up to date.

Improving your Data Quality through data enrichment

Data Enrichment is specifically aimed at improving the data quality in two of the three areas we just discussed. It will ensure data validity and enriches the data by adding relevant meta-data where possible and focuses on introducing common data standards where available.

Common areas that require data enrichment (with some examples):

  • Parts
    • Descriptions
    • ATA Chapters
    • Material Classes
    • Alternate parts
    • Order quantities
    • Dangerous good codes
  • Maintenance Findings
    • Validity – templating
    • Part consumption
    • Panel and zone information
    • Use of abbreviations.
  • Modifications
  • Task cards
  • Worksteps

How EXSYN can help

We partner with Airlines & MROs to help define digital roadmaps and build insights that they need to scale up efficiencies using a combination of our experience and their own data. Having supported over 30 airlines and MROs spanning across a fleet of 1450+ aircraft, our products and services are tailored to meet your specific goals.

Our consultants utilize a proven framework for what we term as a data scan – a process that identifies data silos, inconsistencies and gaps in your Engineering & Maintenance, Inventory data using our database and experience in dealing with multiple Fleet types (Narrow & Wide Bodies), Engines and MRO systems. This is the first step towards building the single source of truth database. The result of a data scan is a recommendation report that elaborates on data gaps, process challenges and opportunities to unearth valuable insights from your data sets after enrichment in line with your goals. We can then assist you with the process of implementing the recommendations and introducing Aviation Standards.