Una Shortt, Group Chief Data Officer at Schneider Electric, speaks with Dave McEachern, Managing Director at AHEAD, in a video interview about strategies to stay relevant as a CDO, establishing a common language across the organization, and honing the data quality.

Schneider Electric is a global leader in energy management and automation, offering products and services for various market segments.

As the third segment of the interview commences, Shortt reveals the burning challenge of relevance that CDOs face, regardless of the current momentum. She states that it is critical to stay relevant as CDO, assuring synchronization from business data offices and data domain owners.

Driving business value with data is the only way to remain relevant to an organization, says Shortt. It boils down to understanding the organizational strategy and securing the data needed for operational and strategic programs.

The chief data office is trying to refine its language to speak with business executives from a business perspective, says Shortt. Making an impact with data without mentioning ‘data’ is challenging, she adds.

Apart from the much-needed technological upgrades, Shortt maintains that the office directs its energy towards initiatives aimed at driving data scalability, reliability, and quality. She highlights that CDOs would not be relevant if they failed to focus on the right initiatives.

Therefore, Shortt insists on focusing the data efforts on overall business success and remembering “why” the group chief data office exists. She affirms that the office is not set up to define frameworks or blueprints but to operationalize that data strategy.

When asked about establishing a common language across Schneider, Shortt reiterates the key role played by the four golden data rules. She notes how crucial it is for the data office to make the data rules tangible and accessible to everyone at Schneider.

In addition, Shortt considers the golden data rules to be the turning point in Schneider’s data strategy. To explain better, she highlights two of the golden data rules:

Golden rule one – authoritative sources: It asks questions about the sources of the data.

Golden rule two: Having a common definition of data across the company and securing it by defining the very structure of the company.

Defining the first golden rule tells people where to get the data from, says Shortt. In rule two, referential data about the organizational structure is defined and published in a standard digital technology format. People can safely access the common definition of that referential for the company, and that aids operationalization, she notes.

As a result, Shortt expresses satisfaction when critical stakeholders stress the need to have an authoritative source or proceed after defining a referential. That is when she knows that the strategy has permeated the organization as a whole.

Moving forward, Shortt observes that nobody wants a piece of the pie when it comes to honing the data quality. The biggest challenge remains in identifying the core data issues and the consequent business impact.

However, she reminds data leaders that not all data issues bring risk, and it is imperative to identify where to pivot the data quality efforts. Further, it is not a one-person job, but rather everybody’s role.

Shortt believes that it starts with defining the business process and comprehending the data flow through the business processes. Then, the organization must focus on the structuring aspects as it delivers those business processes into digital tools. She mentions a digital lock at Schneider that automatically populates customer or vendor details, which is inalterable.

The pressing issue is not the data quality in the data lakes, but the data quality at the source. Therefore, the data office is moving mountains to make the data strategy everybody’s business.

Furthermore, Shortt affirms that data quality is complex, and getting it 100% clean is an uphill battle. Nevertheless, it should be sewn into the business processes since inception.

Schneider, an industrial tech company, has AI at the center of what it does or plans to do, says Shortt. Accordingly, it is fundamental to have the right data quality. She maintains that AI at scale requires data at scale, and the group CDO and group AI officer work in tandem.

Delving further, Shortt adds that the Chief AI Officer only accepts use cases where the data is at the right level. As a data specialist, she states that 80% of the work of AI remains in data preparation.

Concluding, Shortt envisions a time when automation reduces the data preparation aspect. Ensuring the right data quality and aligning with golden data rules are critical to guaranteeing the use of trusted data at scale.

CDO Magazine appreciates Una Shortt for sharing her invaluable insights with our global community.

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