Can We Handle the Truth? Data Veracity in MRO

The aviation industry is making data-driven decisions, but how good is the raw information used in big data solutions?

Printed headline: Trustworthy Data?

We love precision. As leaders in an industry steeped in engineering and product innovation, nothing pleases aerospace executives more than the certainty of an outcome.

There is no small irony then that uncertainty has long been the leading threat to asset availability, operator revenue and aftermarket service provider margins. Today, this uncertainty faces a precision-driven assault from OEM and MRO investments in the Internet of Things, applied intelligence and big data. These investments promise precision through predictive and prescriptive insight-driven improvements to flight operations, operating economics and asset availability. These investments may not, however, reveal the truth.

As others in these pages have noted, the present and future success of digital investments is fueled by data. Getting access to data from aircraft, aligning the industry around data standards and providing data security are all essential to maximizing return on digital investments. 

Yet something more fundamental also is required: confidence in the veracity of the data entering the system. As good as physical mechanisms for collecting data may be, and as far as industry agreement on standards may take us, the promise of digital MRO solutions can be realized only when parties across the aftermarket can truly trust the data that drive business decisions. 

There is a measurable gap between aerospace and defense (A&D) companies’ desire to employ data to make decisions and their trust in those data. For example, take the findings of Accenture’s 2018 Technology Vision. On one hand, 80% of the A&D executives involved in our research indicated their organizations are increasingly using data to drive critical decisions and automated decision-making. On the other hand, 73% believed that while A&D companies are basing their most critical systems and strategies on data, many have not invested in the capabilities to verify the truth within those data.

In short, while we agree data must be trustworthy, we do not necessarily agree they are. That is the fundamental irony of the modern aviation aftermarket. 

Investment is focused on new digital services, but we are not sure we can trust the data upon which those services will rely.

The aftermarket segment is turning to digitalization to improve customer experience and interactions, improve the reliability and safety of operations and introduce value-added services. These are transformational business efforts, not a new set of transactional IT databases. While data have long been considered an IT responsibility—for data and the digital technologies they feed to be trusted to deliver their intended value—there must be a movement to develop a culture of stewardship for the quality of those data within the businesses using them. While IT still maintains the systems in which data reside, it is the business functions using those data as the foundation for aftermarket services that ultimately must be accountable for data veracity.

The journey to that veracity begins with a cruel admission: Not all data are created equal. The promise of big data is not an exhaustive exercise in collecting and investing in more storage, but one of curation. Some data are more important than others in achieving business outcomes and trust.

That trust emerges through three steps: 

  • Defining the framework for data governance.
  • Applying the framework to establish standardized rules and procedures.
  • Using that framework to establish control through data governance.

A governance framework is based on common business definitions for data. Definitions drive standardized rules and procedures that evolve to become the governance model for data quality. 

Our experience indicates millions of dollars of benefit can be achieved when aerospace companies establish basic rules around seemingly innocuous data elements like spare-part units of measure. As data definitions take hold, they can be extended to form the rules under which data are governed. This governance model provides a control plan to ensure the stability and accountability of an organization to its data-governance framework. These rules gradually can be extended outside the enterprise to build trust not only within it but across the extended supply chain.

These are interesting times in the aviation aftermarket. Digital investments promise substantial improvements in asset availability, safety and the cost of operations. Yet for these investments to succeed, a commensurate focus must be placed on ensuring the veracity of the data fueling these digital engines. There is much trust to be earned, and the clock is ticking.

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