Does your data keep up?

Now more than ever, organizations introduce AI when their data doesn’t have the foundational capabilities to handle those demands. This is why the Port Authority of New York and New Jersey (PANYNJ) Engineering Department brought us on to develop and implement a digital transformation program, connecting its six divisions and 42 units together to enable smarter growth and innovation.

But that technological leap forward requires a stable foundation to be successful now and well into the future — and that foundation is built by a robust data governance strategy.

Setting the ground rules

Data governance is the set of rules that define the processes for using data, which people can access it and what skills are required to maximize its current and future potential within an organization. The global body of knowledge created by a robust data governance strategy unlocks efficient decision-making processes and innovations such as AI. However, AI software is only as good as the data it can use to complete tasks. If the data is incomplete, disorganized or even not completely digitized, the data’s capabilities are drastically limited.

Establishing capabilities

We need data governance to ensure data’s accessibility, usability, relevance, accuracy and integration, but to ensure all those qualifications are met, we must first establish what the data is capable of. This step is called discovery — we find where the data is located, what information it contains, and lastly, what tasks it can complete. Discovery for the PANYNJ’s Engineering Department identified and defined key stakeholder, customer and business needs, which unveiled bottlenecks and pain points within their data.

It’s important to remember during this stage that not all data is created equal, and it shouldn’t be. Every organization has different demands for its data, ranging from high-level to personal perspectives. Our digital transformation strategy for the PANYNJ was established because their data was no longer capable of serving their needs — visualize this as the caterpillar stage of development. We solved this pain point by efficiently connecting data into one centralized software ecosystem to establish a reliable data pipeline for improved project delivery — like a butterfly fresh out of its cocoon.

Connecting the workplace

Once we’ve established the rules and organizational structure for data management, we connect the pieces with software that meets goals for current and future use. This all-encompassing digital connection closes gaps to resource accessibility, manages the creation and collection of information, establishes quality standards and enhances organizational design to take on the digital generation of the workplace. We created a framework of operating procedures for the PANYNJ’s Engineering Department that were easy to understand and follow, supporting automation and standardization within daily tasks. Because of these procedures, finding, comparing and utilizing data across the Engineering Department became virtually seamless because we removed the boundaries caused by an outdated organizational structure.

As we discussed in our previous article, data governance is woven through a digital transformation strategy — it’s not just one step. Continually reevaluating data, especially as technology advances, keeps an organization ahead of the innovation curve.

In our third article in this digital transformation series, we will discuss how our systematic framework for digital transformation can be applied to any environment, industry or organization to prepare it for the workplace of the future.